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Tobias Eidelpes 2024-03-14 18:30:11 +01:00
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,summary,config,name
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124,"{'_runtime': 355.4184715747833, 'test/epoch_acc': 0.8222222222222223, 'test/precision': 0.8928571428571429, 'test/epoch_loss': 0.4269479903909895, 'epoch': 9, '_wandb': {'runtime': 353}, '_timestamp': 1678738469.1834886, 'test/recall': 0.6578947368421053, 'test/f1-score': 0.7575757575757577, 'train/epoch_acc': 0.995085995085995, 'train/batch_loss': 0.0014543599681928754, 'train/epoch_loss': 0.016353931551580648, '_step': 529}","{'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.0001}",dark-sweep-2
125,"{'_step': 1039, '_runtime': 384.5172441005707, 'test/recall': 0.8181818181818182, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.8780487804878049, 'test/epoch_loss': 0.40116495291392007, 'train/epoch_acc': 0.8673218673218673, 'epoch': 9, '_wandb': {'runtime': 381}, '_timestamp': 1678738101.018471, 'test/f1-score': 0.8470588235294119, 'train/batch_loss': 0.31195682287216187, 'train/epoch_loss': 0.3623260387038716}","{'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0003}",trim-sweep-1
126,"{'epoch': 9, '_wandb': {'runtime': 560}, '_runtime': 560.7235152721405, 'test/recall': 0.9090909090909092, 'test/f1-score': 0.8602150537634408, 'train/epoch_loss': 0.6107166709712448, '_step': 2059, '_timestamp': 1678738182.1088202, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.8163265306122449, 'test/epoch_loss': 0.6165981186760796, 'train/epoch_acc': 0.7567567567567567, 'train/batch_loss': 0.6653294563293457}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 4, 'learning_rate': 0.001}",sparkling-sweep-1
127,"{'test/epoch_loss': 0.6796493821673923, 'train/epoch_acc': 0.5515970515970516, 'train/batch_loss': 0.6759337782859802, 'epoch': 1, '_wandb': {'runtime': 118}, '_runtime': 122.13349413871764, 'test/recall': 0.6818181818181818, 'test/precision': 0.6382978723404256, '_step': 555, '_timestamp': 1678737059.0375042, 'test/f1-score': 0.6593406593406593, 'test/epoch_acc': 0.6555555555555556, 'train/epoch_loss': 0.6851893525744539}","{'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.0003}",serene-sweep-1
128,"{'_runtime': 456.3002746105194, 'train/epoch_acc': 0.9914004914004914, 'train/epoch_loss': 0.032788554922144414, 'test/epoch_loss': 0.45068282733360926, 'train/batch_loss': 0.003167948452755809, 'test/f1-score': 0.8888888888888888, 'test/epoch_acc': 0.8777777777777778, 'test/batch_loss': 0.1311825066804886, 'test/precision': 0.9361702127659576, 'epoch': 9, '_wandb': {'runtime': 455}, 'test/recall': 0.8461538461538461, '_step': 1159, '_timestamp': 1678734250.8076646}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.003}",super-sweep-10
129,"{'test/epoch_loss': 0.5302444166607327, '_wandb': {'runtime': 563}, '_runtime': 564.230875492096, '_timestamp': 1678733784.6976814, 'test/precision': 0.673469387755102, 'train/epoch_acc': 0.687960687960688, '_step': 2289, 'epoch': 9, 'test/epoch_acc': 0.7111111111111111, 'train/epoch_loss': 0.5984233345387902, 'test/batch_loss': 0.9658783674240112, 'train/batch_loss': 0.3260266184806824, 'test/recall': 0.7674418604651163, 'test/f1-score': 0.7173913043478259}","{'gamma': 0.1, 'epochs': 10, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01}",distinctive-sweep-9
130,"{'test/epoch_loss': 0.17092165086004468, '_step': 2289, '_wandb': {'runtime': 527}, '_timestamp': 1678733210.1129615, 'test/batch_loss': 0.1419784128665924, 'train/epoch_acc': 0.9496314496314496, 'epoch': 9, '_runtime': 527.6160025596619, 'test/recall': 0.8636363636363636, 'test/precision': 1, 'train/batch_loss': 0.007875862531363964, 'test/f1-score': 0.9268292682926828, 'test/epoch_acc': 0.9333333333333332, 'train/epoch_loss': 0.1743801347293527}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.0003}",winter-sweep-8
131,"{'epoch': 9, '_runtime': 453.52900218963623, '_timestamp': 1678732673.1225052, 'train/epoch_loss': 0.07730489082323246, 'test/epoch_loss': 0.21558621691332924, 'train/batch_loss': 0.04383014515042305, '_step': 1159, 'test/recall': 0.8292682926829268, 'test/f1-score': 0.9066666666666668, 'train/epoch_acc': 0.9791154791154792, '_wandb': {'runtime': 452}, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 1, 'test/batch_loss': 0.27116066217422485}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.001}",stilted-sweep-7
132,"{'_timestamp': 1678732212.5530572, 'test/recall': 0.7555555555555555, 'test/precision': 0.6538461538461539, '_wandb': {'runtime': 561}, '_runtime': 561.7993631362915, 'test/epoch_loss': 0.6190193812052409, '_step': 2289, 'test/epoch_acc': 0.6777777777777778, 'test/f1-score': 0.7010309278350516, 'test/batch_loss': 0.4716488718986511, 'train/epoch_acc': 0.7272727272727273, 'train/batch_loss': 0.48304444551467896, 'train/epoch_loss': 0.5549268187263967, 'epoch': 9}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01}",summer-sweep-6
133,"{'epoch': 9, '_wandb': {'runtime': 453}, 'test/batch_loss': 0.5068956017494202, '_step': 1159, '_runtime': 454.3645238876343, 'test/f1-score': 0.813953488372093, 'test/epoch_acc': 0.8222222222222223, 'train/batch_loss': 0.4434223175048828, 'train/epoch_loss': 0.5186349417126442, 'test/recall': 0.7142857142857143, 'train/epoch_acc': 0.8218673218673218, '_timestamp': 1678731639.156168, 'test/precision': 0.945945945945946, 'test/epoch_loss': 0.4936415394147237}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0001}",different-sweep-5
134,"{'_runtime': 454.26038885116577, 'test/f1-score': 0.8354430379746836, 'test/batch_loss': 0.5159374475479126, 'test/epoch_loss': 0.5482642173767089, 'epoch': 9, '_wandb': {'runtime': 453}, 'test/epoch_acc': 0.8555555555555556, 'train/epoch_acc': 0.812039312039312, 'train/epoch_loss': 0.5429200196149016, '_step': 1159, '_timestamp': 1678731176.111379, 'test/recall': 0.8461538461538461, 'test/precision': 0.825, 'train/batch_loss': 0.5655931830406189}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0001}",wise-sweep-4
135,"{'_runtime': 528.9760706424713, 'test/f1-score': 0.8493150684931509, 'train/epoch_loss': 0.02060394324720534, 'epoch': 9, '_timestamp': 1678730714.7711067, 'test/batch_loss': 1.7588363885879517, 'train/batch_loss': 0.00470334617421031, '_wandb': {'runtime': 528}, 'test/recall': 0.775, 'test/epoch_acc': 0.8777777777777778, 'test/epoch_loss': 0.24194780117250048, '_step': 2289, 'train/epoch_acc': 0.9963144963144964, 'test/precision': 0.9393939393939394}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.003}",misty-sweep-3
136,"{'epoch': 9, '_wandb': {'runtime': 454}, 'test/epoch_acc': 0.8111111111111111, 'test/precision': 0.8387096774193549, 'test/epoch_loss': 0.4792341656155056, '_step': 1159, 'test/recall': 0.6842105263157895, 'train/batch_loss': 0.5347514748573303, 'train/epoch_loss': 0.42904984072326735, 'train/epoch_acc': 0.8329238329238329, '_runtime': 455.41485929489136, '_timestamp': 1678730177.1362092, 'test/f1-score': 0.7536231884057972, 'test/batch_loss': 0.455120325088501}","{'gamma': 0.1, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0003}",unique-sweep-2
137,"{'_step': 2289, '_runtime': 528.4356484413147, '_timestamp': 1678729705.2001765, 'test/batch_loss': 2.5320074558258057, 'test/epoch_loss': 0.5442472649919283, 'epoch': 9, 'test/recall': 0.8636363636363636, 'train/epoch_acc': 0.9901719901719902, 'test/epoch_acc': 0.888888888888889, '_wandb': {'runtime': 527}, 'test/f1-score': 0.8837209302325582, 'test/precision': 0.9047619047619048, 'train/batch_loss': 0.005740344058722258, 'train/epoch_loss': 0.024021292951151657}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.003}",polar-sweep-1

1 summary config name
2 0 {'test/epoch_acc': 0.7333333333333334, 'test/precision': 0.8285714285714286, 'test/epoch_loss': 0.5664619127909343, 'train/epoch_acc': 0.8230958230958231, '_step': 2059, 'epoch': 9, '_timestamp': 1680692970.2016854, 'test/f1-score': 0.7073170731707318, 'train/batch_loss': 0.33577921986579895, 'train/epoch_loss': 0.4241055610431793, '_wandb': {'runtime': 363}, '_runtime': 367.13677954673767, 'test/recall': 0.6170212765957447} {'_step': 2059, '_timestamp': 1680692970.2016854, 'test/recall': 0.6170212765957447, 'test/f1-score': 0.7073170731707318, 'test/epoch_acc': 0.7333333333333334, 'test/epoch_loss': 0.5664619127909343, 'train/epoch_loss': 0.4241055610431793, 'epoch': 9, '_wandb': {'runtime': 363}, '_runtime': 367.13677954673767, 'test/precision': 0.8285714285714286, 'train/epoch_acc': 0.8230958230958231, 'train/batch_loss': 0.33577921986579895} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.0003} fiery-sweep-26
3 1 {'epoch': 9, '_wandb': {'runtime': 338}, '_runtime': 341.8420207500458, 'test/precision': 0.6851851851851852, 'train/epoch_acc': 0.7125307125307125, 'train/epoch_loss': 0.649790015355375, '_step': 1039, 'test/recall': 0.8222222222222222, 'test/f1-score': 0.7474747474747475, 'test/epoch_acc': 0.7222222222222222, 'test/epoch_loss': 0.6454579922888014, 'train/batch_loss': 0.7014500498771667, '_timestamp': 1680692589.503975} {'test/f1-score': 0.7474747474747475, 'test/precision': 0.6851851851851852, 'test/epoch_loss': 0.6454579922888014, 'train/batch_loss': 0.7014500498771667, '_step': 1039, 'epoch': 9, '_wandb': {'runtime': 338}, '_runtime': 341.8420207500458, 'train/epoch_loss': 0.649790015355375, '_timestamp': 1680692589.503975, 'test/recall': 0.8222222222222222, 'test/epoch_acc': 0.7222222222222222, 'train/epoch_acc': 0.7125307125307125} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0003} radiant-sweep-25
4 2 {'test/recall': 0.7837837837837838, 'test/precision': 0.935483870967742, 'test/epoch_loss': 0.34812947780333664, 'train/epoch_loss': 0.01614290558709019, '_step': 1039, 'epoch': 9, '_timestamp': 1680692234.39516, 'test/epoch_acc': 0.888888888888889, 'train/epoch_acc': 0.9987714987714988, 'train/batch_loss': 0.01956617273390293, '_wandb': {'runtime': 333}, '_runtime': 336.8275649547577, 'test/f1-score': 0.8529411764705881} {'train/epoch_acc': 0.9987714987714988, 'train/epoch_loss': 0.01614290558709019, '_step': 1039, 'epoch': 9, '_runtime': 336.8275649547577, '_timestamp': 1680692234.39516, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.935483870967742, '_wandb': {'runtime': 333}, 'test/recall': 0.7837837837837838, 'test/f1-score': 0.8529411764705881, 'test/epoch_loss': 0.34812947780333664, 'train/batch_loss': 0.01956617273390293} {'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.003} blooming-sweep-24
5 3 {'_wandb': {'runtime': 327}, '_runtime': 331.57809829711914, '_timestamp': 1680691883.3877182, 'test/precision': 0.7608695652173914, 'test/epoch_loss': 0.5553177932898203, 'train/batch_loss': 0.5222326517105103, 'train/epoch_loss': 0.5324229019572753, 'epoch': 9, 'test/recall': 0.8333333333333334, 'test/f1-score': 0.7954545454545455, 'test/epoch_acc': 0.8, 'train/epoch_acc': 0.8353808353808354, '_step': 529} {'train/epoch_acc': 0.8353808353808354, 'train/epoch_loss': 0.5324229019572753, 'epoch': 9, '_runtime': 331.57809829711914, '_timestamp': 1680691883.3877182, 'test/recall': 0.8333333333333334, 'test/f1-score': 0.7954545454545455, 'test/precision': 0.7608695652173914, '_step': 529, '_wandb': {'runtime': 327}, 'test/epoch_acc': 0.8, 'test/epoch_loss': 0.5553177932898203, 'train/batch_loss': 0.5222326517105103} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.0003} visionary-sweep-23
6 4 {'train/epoch_loss': 0.7508098256090057, 'epoch': 1, '_timestamp': 1680691538.7247725, 'test/recall': 0.8846153846153846, 'test/epoch_acc': 0.5777777777777778, 'train/epoch_acc': 0.5577395577395577, 'train/batch_loss': 0.5083656311035156, '_step': 410, '_wandb': {'runtime': 70}, '_runtime': 71.64615154266357, 'test/f1-score': 0.7076923076923076, 'test/precision': 0.5897435897435898, 'test/epoch_loss': 1.5602711306677923} {'test/precision': 0.5897435897435898, 'train/epoch_acc': 0.5577395577395577, '_step': 410, 'epoch': 1, '_runtime': 71.64615154266357, '_timestamp': 1680691538.7247725, 'test/f1-score': 0.7076923076923076, 'test/epoch_acc': 0.5777777777777778, 'train/batch_loss': 0.5083656311035156, '_wandb': {'runtime': 70}, 'test/recall': 0.8846153846153846, 'test/epoch_loss': 1.5602711306677923, 'train/epoch_loss': 0.7508098256090057} {'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.01} ancient-sweep-22
7 5 {'_step': 529, 'epoch': 9, '_wandb': {'runtime': 328}, '_timestamp': 1680691453.5148375, 'test/precision': 0.6885245901639344, 'train/epoch_loss': 0.49390909720111537, '_runtime': 331.44886469841003, 'test/recall': 0.9545454545454546, 'test/f1-score': 0.8, 'test/epoch_acc': 0.7666666666666667, 'test/epoch_loss': 0.4844042791260613, 'train/epoch_acc': 0.769041769041769, 'train/batch_loss': 0.4559023082256317} {'_wandb': {'runtime': 328}, 'test/recall': 0.9545454545454546, '_step': 529, 'epoch': 9, '_runtime': 331.44886469841003, '_timestamp': 1680691453.5148375, 'test/f1-score': 0.8, 'test/epoch_acc': 0.7666666666666667, 'test/precision': 0.6885245901639344, 'test/epoch_loss': 0.4844042791260613, 'train/epoch_acc': 0.769041769041769, 'train/batch_loss': 0.4559023082256317, 'train/epoch_loss': 0.49390909720111537} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 16, 'learning_rate': 0.003} fresh-sweep-22
8 6 {'test/epoch_acc': 0.9222222222222224, 'test/epoch_loss': 0.26263883135527266, 'train/epoch_acc': 0.9975429975429976, 'epoch': 9, '_wandb': {'runtime': 355}, '_timestamp': 1680691110.042932, 'test/recall': 0.8867924528301887, 'test/f1-score': 0.9306930693069309, '_step': 2059, '_runtime': 358.66950702667236, 'test/precision': 0.9791666666666666, 'train/batch_loss': 0.0031523401848971844, 'train/epoch_loss': 0.018423480946079804} {'test/f1-score': 0.9306930693069309, 'test/epoch_acc': 0.9222222222222224, 'test/epoch_loss': 0.26263883135527266, 'train/epoch_loss': 0.018423480946079804, 'epoch': 9, '_runtime': 358.66950702667236, '_timestamp': 1680691110.042932, 'test/precision': 0.9791666666666666, 'train/epoch_acc': 0.9975429975429976, 'train/batch_loss': 0.0031523401848971844, '_step': 2059, '_wandb': {'runtime': 355}, 'test/recall': 0.8867924528301887} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.01} pleasant-sweep-21
9 7 {'train/epoch_loss': 0.0014873178028192654, 'epoch': 9, '_runtime': 332.6156196594238, 'test/recall': 0.9148936170212766, 'test/f1-score': 0.8865979381443299, 'test/epoch_acc': 0.8777777777777778, 'test/epoch_loss': 0.3669874522421095, 'train/batch_loss': 0.003317732596769929, '_step': 279, '_wandb': {'runtime': 329}, '_timestamp': 1680690741.3215847, 'test/precision': 0.86, 'train/epoch_acc': 1} {'epoch': 9, '_wandb': {'runtime': 329}, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.86, 'train/epoch_acc': 1, 'train/batch_loss': 0.003317732596769929, '_step': 279, '_runtime': 332.6156196594238, '_timestamp': 1680690741.3215847, 'test/recall': 0.9148936170212766, 'test/f1-score': 0.8865979381443299, 'test/epoch_loss': 0.3669874522421095, 'train/epoch_loss': 0.0014873178028192654} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 32, 'learning_rate': 0.01} fragrant-sweep-20
10 8 {'epoch': 9, 'test/recall': 0.82, 'test/precision': 0.7592592592592593, 'test/epoch_loss': 0.5786970999505785, 'train/epoch_acc': 0.8206388206388207, 'train/batch_loss': 0.58731609582901, '_step': 149, '_runtime': 342.05230498313904, '_timestamp': 1680690397.165603, 'test/f1-score': 0.7884615384615384, 'test/epoch_acc': 0.7555555555555555, 'train/epoch_loss': 0.5623220165765842, '_wandb': {'runtime': 338}} {'test/epoch_loss': 0.5786970999505785, 'train/epoch_acc': 0.8206388206388207, 'train/epoch_loss': 0.5623220165765842, '_wandb': {'runtime': 338}, 'test/recall': 0.82, 'test/precision': 0.7592592592592593, '_timestamp': 1680690397.165603, 'test/f1-score': 0.7884615384615384, 'test/epoch_acc': 0.7555555555555555, 'train/batch_loss': 0.58731609582901, '_step': 149, 'epoch': 9, '_runtime': 342.05230498313904} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 64, 'learning_rate': 0.001} treasured-sweep-19
11 9 {'_timestamp': 1680690042.488695, 'test/f1-score': 0.7865168539325843, 'test/precision': 0.8536585365853658, 'train/batch_loss': 0.5736206769943237, 'epoch': 9, '_wandb': {'runtime': 357}, '_runtime': 360.5366156101227, 'test/epoch_loss': 0.6037532766660054, 'train/epoch_acc': 0.7788697788697788, 'train/epoch_loss': 0.5984062318134074, '_step': 2059, 'test/recall': 0.7291666666666666, 'test/epoch_acc': 0.788888888888889} {'test/recall': 0.7291666666666666, 'test/f1-score': 0.7865168539325843, 'test/precision': 0.8536585365853658, 'test/epoch_loss': 0.6037532766660054, 'train/batch_loss': 0.5736206769943237, 'train/epoch_loss': 0.5984062318134074, '_step': 2059, 'epoch': 9, '_timestamp': 1680690042.488695, 'test/epoch_acc': 0.788888888888889, 'train/epoch_acc': 0.7788697788697788, '_wandb': {'runtime': 357}, '_runtime': 360.5366156101227} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 4, 'learning_rate': 0.0001} desert-sweep-18
12 10 {'_timestamp': 1680689670.8310964, 'test/f1-score': 0.8333333333333334, 'test/epoch_loss': 0.3740654948684904, 'train/epoch_acc': 0.8697788697788698, '_step': 2059, 'epoch': 9, 'test/recall': 0.7446808510638298, 'test/epoch_acc': 0.8444444444444444, 'test/precision': 0.945945945945946, 'train/batch_loss': 0.5778521299362183, 'train/epoch_loss': 0.3086323318522451, '_wandb': {'runtime': 362}, '_runtime': 365.3367943763733} {'epoch': 9, '_runtime': 365.3367943763733, 'test/recall': 0.7446808510638298, 'test/f1-score': 0.8333333333333334, 'test/precision': 0.945945945945946, 'train/epoch_acc': 0.8697788697788698, 'train/epoch_loss': 0.3086323318522451, '_step': 2059, '_wandb': {'runtime': 362}, '_timestamp': 1680689670.8310964, 'test/epoch_acc': 0.8444444444444444, 'test/epoch_loss': 0.3740654948684904, 'train/batch_loss': 0.5778521299362183} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.003} celestial-sweep-17
13 11 {'test/recall': 0.9285714285714286, 'test/f1-score': 0.9176470588235294, 'test/precision': 0.9069767441860463, 'train/epoch_acc': 1, 'epoch': 9, '_wandb': {'runtime': 337}, '_runtime': 340.39124369621277, '_timestamp': 1680689237.7951498, 'train/epoch_loss': 0.0053219743558098115, '_step': 149, 'test/epoch_acc': 0.9222222222222224, 'test/epoch_loss': 0.18080708616309696, 'train/batch_loss': 0.004256190732121468} {'train/batch_loss': 0.004256190732121468, 'train/epoch_loss': 0.0053219743558098115, 'epoch': 9, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 0.9069767441860463, 'test/epoch_loss': 0.18080708616309696, 'train/epoch_acc': 1, 'test/f1-score': 0.9176470588235294, '_step': 149, '_wandb': {'runtime': 337}, '_runtime': 340.39124369621277, '_timestamp': 1680689237.7951498, 'test/recall': 0.9285714285714286} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 64, 'learning_rate': 0.01} cosmic-sweep-15
14 12 {'_timestamp': 1680688886.363035, 'test/recall': 0.8222222222222222, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.925, 'train/epoch_loss': 0.09628425111664636, 'test/epoch_loss': 0.23811448697621623, 'train/epoch_acc': 0.968058968058968, 'train/batch_loss': 0.21692615747451785, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 356}, '_runtime': 359.0396990776062, 'test/f1-score': 0.8705882352941177} {'test/f1-score': 0.8705882352941177, 'test/epoch_loss': 0.23811448697621623, 'train/epoch_acc': 0.968058968058968, 'train/batch_loss': 0.21692615747451785, 'train/epoch_loss': 0.09628425111664636, 'test/recall': 0.8222222222222222, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.925, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 356}, '_runtime': 359.0396990776062, '_timestamp': 1680688886.363035} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.001} stilted-sweep-14
15 13 {'_step': 149, 'test/f1-score': 0.9278350515463918, 'test/epoch_loss': 0.16714997291564945, 'train/epoch_acc': 1, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 0.9574468085106383, 'train/batch_loss': 0.007201554253697395, 'epoch': 9, '_wandb': {'runtime': 333}, '_runtime': 336.5640392303467, '_timestamp': 1680688517.0028613, 'test/recall': 0.9, 'train/epoch_loss': 0.007631345846546077} {'_wandb': {'runtime': 333}, '_runtime': 336.5640392303467, 'test/recall': 0.9, 'test/f1-score': 0.9278350515463918, 'test/epoch_acc': 0.9222222222222224, 'train/epoch_loss': 0.007631345846546077, '_step': 149, 'epoch': 9, 'test/epoch_loss': 0.16714997291564945, 'train/epoch_acc': 1, 'train/batch_loss': 0.007201554253697395, '_timestamp': 1680688517.0028613, 'test/precision': 0.9574468085106383} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.01} frosty-sweep-13
16 14 {'test/epoch_acc': 0.8777777777777778, 'test/epoch_loss': 0.32556109494633145, 'train/epoch_loss': 0.17368088453934877, '_runtime': 331.98337984085083, '_timestamp': 1680688162.2054858, 'test/recall': 0.8181818181818182, 'test/f1-score': 0.8674698795180724, 'test/precision': 0.9230769230769232, 'train/epoch_acc': 0.9496314496314496, 'train/batch_loss': 0.27152174711227417, '_step': 529, 'epoch': 9, '_wandb': {'runtime': 328}} {'_timestamp': 1680688162.2054858, 'test/recall': 0.8181818181818182, 'test/epoch_acc': 0.8777777777777778, 'train/batch_loss': 0.27152174711227417, 'train/epoch_acc': 0.9496314496314496, '_step': 529, 'epoch': 9, '_wandb': {'runtime': 328}, '_runtime': 331.98337984085083, 'test/f1-score': 0.8674698795180724, 'test/precision': 0.9230769230769232, 'test/epoch_loss': 0.32556109494633145, 'train/epoch_loss': 0.17368088453934877} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.001} young-sweep-12
17 15 {'_wandb': {'runtime': 332}, 'test/f1-score': 0.7311827956989247, 'train/epoch_loss': 0.5277571982775039, '_step': 1039, 'epoch': 9, 'test/recall': 0.8292682926829268, 'test/epoch_acc': 0.7222222222222222, 'test/precision': 0.6538461538461539, 'test/epoch_loss': 0.5193446947468652, 'train/epoch_acc': 0.7469287469287469, 'train/batch_loss': 0.3307788372039795, '_runtime': 335.6552822589874, '_timestamp': 1680687816.5057352} {'test/f1-score': 0.7311827956989247, 'train/epoch_acc': 0.7469287469287469, 'epoch': 9, '_wandb': {'runtime': 332}, '_timestamp': 1680687816.5057352, 'test/recall': 0.8292682926829268, 'test/epoch_acc': 0.7222222222222222, 'test/precision': 0.6538461538461539, 'test/epoch_loss': 0.5193446947468652, 'train/batch_loss': 0.3307788372039795, '_step': 1039, '_runtime': 335.6552822589874, 'train/epoch_loss': 0.5277571982775039} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.1} sandy-sweep-11
18 16 {'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.8085106382978723, 'test/epoch_loss': 0.4616309046745301, '_wandb': {'runtime': 334}, '_runtime': 336.80703043937683, '_timestamp': 1680687470.9289024, 'test/recall': 0.9047619047619048, 'train/batch_loss': 0.0030224076472222805, 'train/epoch_loss': 0.003708146820279612, '_step': 149, 'epoch': 9, 'test/f1-score': 0.853932584269663, 'train/epoch_acc': 1} {'train/epoch_acc': 1, 'train/epoch_loss': 0.003708146820279612, 'epoch': 9, '_wandb': {'runtime': 334}, '_timestamp': 1680687470.9289024, 'test/f1-score': 0.853932584269663, 'test/precision': 0.8085106382978723, 'test/epoch_loss': 0.4616309046745301, '_step': 149, '_runtime': 336.80703043937683, 'test/recall': 0.9047619047619048, 'test/epoch_acc': 0.8555555555555556, 'train/batch_loss': 0.0030224076472222805} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.1} laced-sweep-10
19 17 {'_runtime': 265.48077392578125, 'test/recall': 0.08888888888888889, 'test/epoch_acc': 0.45555555555555555, 'train/epoch_loss': 9.16968992828444, '_wandb': {'runtime': 265}, 'epoch': 7, '_timestamp': 1680687113.1220188, 'test/f1-score': 0.14035087719298245, 'test/precision': 0.3333333333333333, 'test/epoch_loss': 11610.708938450283, 'train/epoch_acc': 0.5331695331695332, 'train/batch_loss': 9.74098777770996, '_step': 422} {'_step': 422, 'epoch': 7, '_runtime': 265.48077392578125, 'train/epoch_acc': 0.5331695331695332, 'train/batch_loss': 9.74098777770996, 'train/epoch_loss': 9.16968992828444, '_wandb': {'runtime': 265}, '_timestamp': 1680687113.1220188, 'test/recall': 0.08888888888888889, 'test/f1-score': 0.14035087719298245, 'test/epoch_acc': 0.45555555555555555, 'test/precision': 0.3333333333333333, 'test/epoch_loss': 11610.708938450283} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.1} jumping-sweep-9
20 18 {'test/precision': 0.8913043478260869, 'train/epoch_acc': 0.8955773955773956, 'train/epoch_loss': 0.3055295220024756, '_wandb': {'runtime': 327}, '_timestamp': 1680686834.80723, 'test/f1-score': 0.845360824742268, 'test/epoch_acc': 0.8333333333333334, 'test/epoch_loss': 0.3831123087141249, 'train/batch_loss': 0.34334877133369446, '_step': 529, 'epoch': 9, '_runtime': 330.36346793174744, 'test/recall': 0.803921568627451} {'_step': 529, '_runtime': 330.36346793174744, '_timestamp': 1680686834.80723, 'test/recall': 0.803921568627451, 'test/f1-score': 0.845360824742268, 'train/epoch_acc': 0.8955773955773956, 'train/epoch_loss': 0.3055295220024756, 'epoch': 9, '_wandb': {'runtime': 327}, 'test/epoch_acc': 0.8333333333333334, 'test/precision': 0.8913043478260869, 'test/epoch_loss': 0.3831123087141249, 'train/batch_loss': 0.34334877133369446} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.0003} dutiful-sweep-8
21 19 {'epoch': 2, '_runtime': 99.40804982185364, '_timestamp': 1680686491.634724, 'test/epoch_acc': 0.45555555555555555, 'test/precision': 0.45555555555555555, 'test/epoch_loss': 6.554853016439314e+29, 'train/batch_loss': 'NaN', '_step': 157, '_wandb': {'runtime': 99}, 'test/recall': 1, 'test/f1-score': 0.6259541984732825, 'train/epoch_acc': 0.484029484029484, 'train/epoch_loss': 'NaN'} {'epoch': 2, '_runtime': 99.40804982185364, '_timestamp': 1680686491.634724, 'test/recall': 1, 'test/epoch_acc': 0.45555555555555555, 'train/epoch_acc': 0.484029484029484, 'train/batch_loss': 'NaN', '_step': 157, '_wandb': {'runtime': 99}, 'test/f1-score': 0.6259541984732825, 'test/precision': 0.45555555555555555, 'test/epoch_loss': 6.554853016439314e+29, 'train/epoch_loss': 'NaN'} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.1} olive-sweep-7
22 20 {'_wandb': {'runtime': 334}, '_runtime': 337.17863941192627, 'test/recall': 0.8888888888888888, 'test/f1-score': 0.8695652173913044, 'test/epoch_acc': 0.8666666666666667, 'test/precision': 0.851063829787234, 'test/epoch_loss': 0.35141510632303025, 'train/epoch_acc': 0.9103194103194104, 'train/batch_loss': 0.3707323968410492, '_step': 279, 'epoch': 9, '_timestamp': 1680686383.3591404, 'train/epoch_loss': 0.3219767680771521} {'_timestamp': 1680686383.3591404, 'test/epoch_acc': 0.8666666666666667, 'test/precision': 0.851063829787234, 'train/epoch_loss': 0.3219767680771521, 'test/recall': 0.8888888888888888, 'test/f1-score': 0.8695652173913044, 'test/epoch_loss': 0.35141510632303025, 'train/epoch_acc': 0.9103194103194104, '_step': 279, 'epoch': 9, '_wandb': {'runtime': 334}, '_runtime': 337.17863941192627, 'train/batch_loss': 0.3707323968410492} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 32, 'learning_rate': 0.001} good-sweep-6
23 21 {'test/recall': 0.6938775510204082, 'test/f1-score': 0.6601941747572815, 'test/epoch_acc': 0.6111111111111112, 'train/epoch_acc': 0.5196560196560196, '_wandb': {'runtime': 342}, '_runtime': 344.80718994140625, '_timestamp': 1680686028.304971, 'test/precision': 0.6296296296296297, 'test/epoch_loss': 0.6818753732575311, 'train/batch_loss': 0.7027227878570557, 'train/epoch_loss': 0.6907664721955246, '_step': 149, 'epoch': 9} {'train/batch_loss': 0.7027227878570557, 'epoch': 9, '_wandb': {'runtime': 342}, '_runtime': 344.80718994140625, 'test/recall': 0.6938775510204082, 'test/f1-score': 0.6601941747572815, 'train/epoch_acc': 0.5196560196560196, '_step': 149, '_timestamp': 1680686028.304971, 'test/epoch_acc': 0.6111111111111112, 'test/precision': 0.6296296296296297, 'test/epoch_loss': 0.6818753732575311, 'train/epoch_loss': 0.6907664721955246} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 64, 'learning_rate': 0.0003} summer-sweep-5
24 22 {'epoch': 9, '_timestamp': 1680685671.7387648, 'test/epoch_acc': 0.9222222222222224, 'test/epoch_loss': 0.22382020586066775, 'train/epoch_acc': 0.9864864864864864, '_step': 529, '_runtime': 333.9663326740265, 'test/recall': 0.8717948717948718, 'test/f1-score': 0.9066666666666668, 'test/precision': 0.9444444444444444, 'train/batch_loss': 0.15035715699195862, 'train/epoch_loss': 0.10497688309859292, '_wandb': {'runtime': 331}} {'train/batch_loss': 0.15035715699195862, 'train/epoch_loss': 0.10497688309859292, 'epoch': 9, '_wandb': {'runtime': 331}, '_runtime': 333.9663326740265, 'test/recall': 0.8717948717948718, 'test/epoch_acc': 0.9222222222222224, 'train/epoch_acc': 0.9864864864864864, '_step': 529, '_timestamp': 1680685671.7387648, 'test/f1-score': 0.9066666666666668, 'test/precision': 0.9444444444444444, 'test/epoch_loss': 0.22382020586066775} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 16, 'learning_rate': 0.001} firm-sweep-4
25 23 {'_step': 149, '_runtime': 335.79468297958374, 'test/recall': 0.925, 'test/f1-score': 0.6379310344827587, 'test/precision': 0.4868421052631579, 'test/epoch_loss': 0.6597137530644734, 'train/batch_loss': 0.652446985244751, 'epoch': 9, '_wandb': {'runtime': 333}, '_timestamp': 1680685319.453976, 'test/epoch_acc': 0.5333333333333333, 'train/epoch_acc': 0.5909090909090909, 'train/epoch_loss': 0.6564877619028677} {'_timestamp': 1680685319.453976, 'test/recall': 0.925, 'epoch': 9, '_wandb': {'runtime': 333}, '_runtime': 335.79468297958374, 'test/precision': 0.4868421052631579, 'test/epoch_loss': 0.6597137530644734, 'train/epoch_acc': 0.5909090909090909, 'train/batch_loss': 0.652446985244751, 'train/epoch_loss': 0.6564877619028677, '_step': 149, 'test/f1-score': 0.6379310344827587, 'test/epoch_acc': 0.5333333333333333} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 64, 'learning_rate': 0.0001} genial-sweep-3
26 24 {'_step': 529, 'test/recall': 0.9736842105263158, 'test/f1-score': 0.7628865979381443, 'test/precision': 0.6271186440677966, 'test/epoch_loss': 0.5467572536733415, 'train/epoch_acc': 0.7899262899262899, 'epoch': 9, '_wandb': {'runtime': 329}, '_runtime': 331.50625491142273, '_timestamp': 1680684975.004809, 'test/epoch_acc': 0.7444444444444445, 'train/batch_loss': 0.5583129525184631, 'train/epoch_loss': 0.4703364581675143} {'test/epoch_loss': 0.5467572536733415, 'train/batch_loss': 0.5583129525184631, '_wandb': {'runtime': 329}, 'test/recall': 0.9736842105263158, 'test/epoch_acc': 0.7444444444444445, '_timestamp': 1680684975.004809, 'test/f1-score': 0.7628865979381443, 'test/precision': 0.6271186440677966, 'train/epoch_acc': 0.7899262899262899, 'train/epoch_loss': 0.4703364581675143, '_step': 529, 'epoch': 9, '_runtime': 331.50625491142273} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.1} fine-sweep-2
27 25 {'_timestamp': 1680684633.811369, 'test/f1-score': 0.896551724137931, 'test/epoch_acc': 0.9, 'test/epoch_loss': 0.30911533037821454, '_step': 529, 'epoch': 9, '_wandb': {'runtime': 447}, '_runtime': 450.5545320510864, 'train/epoch_acc': 0.9987714987714988, 'train/batch_loss': 0.005764181260019541, 'test/recall': 0.8863636363636364, 'test/precision': 0.9069767441860463, 'train/epoch_loss': 0.007131033717467008} {'test/epoch_acc': 0.9, 'test/precision': 0.9069767441860463, 'train/epoch_acc': 0.9987714987714988, 'train/batch_loss': 0.005764181260019541, '_step': 529, '_runtime': 450.5545320510864, '_timestamp': 1680684633.811369, 'test/f1-score': 0.896551724137931, 'train/epoch_loss': 0.007131033717467008, 'epoch': 9, '_wandb': {'runtime': 447}, 'test/recall': 0.8863636363636364, 'test/epoch_loss': 0.30911533037821454} {'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 16, 'learning_rate': 0.01} visionary-sweep-1
28 26 {'_step': 239, 'epoch': 1, '_timestamp': 1680629962.8990817, 'train/epoch_acc': 0.8931203931203932, 'train/batch_loss': 0.08615076541900635, '_wandb': {'runtime': 83}, '_runtime': 83.58446168899536, 'test/recall': 0.9047619047619048, 'test/f1-score': 0.8735632183908046, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.8444444444444444, 'test/epoch_loss': 0.29840316110187104, 'train/epoch_loss': 0.2428556958016658} {'_step': 239, '_wandb': {'runtime': 83}, '_runtime': 83.58446168899536, 'test/recall': 0.9047619047619048, 'test/precision': 0.8444444444444444, 'train/epoch_acc': 0.8931203931203932, 'train/batch_loss': 0.08615076541900635, 'train/epoch_loss': 0.2428556958016658, 'epoch': 1, '_timestamp': 1680629962.8990817, 'test/f1-score': 0.8735632183908046, 'test/epoch_acc': 0.8777777777777778, 'test/epoch_loss': 0.29840316110187104} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.1} stoic-sweep-14
29 27 {'_timestamp': 1680629872.8401277, 'test/recall': 0.975, 'test/f1-score': 0.951219512195122, 'test/epoch_loss': 0.20102048052681817, 'train/epoch_acc': 0.9803439803439804, '_step': 149, '_wandb': {'runtime': 347}, '_runtime': 348.9410927295685, 'train/batch_loss': 0.10338585078716278, 'train/epoch_loss': 0.1163152276517718, 'epoch': 9, 'test/epoch_acc': 0.9555555555555556, 'test/precision': 0.9285714285714286} {'test/epoch_loss': 0.20102048052681817, 'train/batch_loss': 0.10338585078716278, '_wandb': {'runtime': 347}, 'test/recall': 0.975, 'test/f1-score': 0.951219512195122, 'test/epoch_acc': 0.9555555555555556, 'test/precision': 0.9285714285714286, 'train/epoch_loss': 0.1163152276517718, '_step': 149, 'epoch': 9, '_runtime': 348.9410927295685, '_timestamp': 1680629872.8401277, 'train/epoch_acc': 0.9803439803439804} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 64, 'learning_rate': 0.01} rich-sweep-13
30 28 {'_timestamp': 1680629513.1781075, 'test/epoch_loss': 3.395405118153546e+20, 'train/batch_loss': 82027960, 'train/epoch_loss': 60563307.6520902, 'epoch': 3, '_wandb': {'runtime': 135}, '_runtime': 132.22715950012207, 'test/recall': 0.9111111111111112, 'test/f1-score': 0.6721311475409836, 'test/epoch_acc': 0.5555555555555556, 'test/precision': 0.5324675324675324, 'train/epoch_acc': 0.5282555282555282, '_step': 210} {'train/epoch_acc': 0.5282555282555282, 'train/batch_loss': 82027960.0, 'train/epoch_loss': 60563307.6520902, 'epoch': 3, 'test/recall': 0.9111111111111112, 'test/f1-score': 0.6721311475409836, 'test/precision': 0.5324675324675324, 'test/epoch_acc': 0.5555555555555556, 'test/epoch_loss': 3.395405118153546e+20, '_step': 210, '_wandb': {'runtime': 135}, '_runtime': 132.22715950012207, '_timestamp': 1680629513.1781075} {'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.003} smooth-sweep-12
31 29 {'test/recall': 0.8888888888888888, 'test/f1-score': 0.6597938144329897, 'test/precision': 0.5245901639344263, 'test/epoch_loss': 0.6240786300765143, '_step': 279, '_runtime': 327.2181556224823, '_timestamp': 1680629374.0562296, 'test/epoch_acc': 0.6333333333333333, 'train/epoch_acc': 0.7469287469287469, 'train/batch_loss': 0.5836847424507141, 'train/epoch_loss': 0.6072891213970044, 'epoch': 9, '_wandb': {'runtime': 326}} {'_timestamp': 1680629374.0562296, 'test/epoch_acc': 0.6333333333333333, 'train/batch_loss': 0.5836847424507141, 'train/epoch_loss': 0.6072891213970044, '_step': 279, '_wandb': {'runtime': 326}, '_runtime': 327.2181556224823, 'test/recall': 0.8888888888888888, 'test/f1-score': 0.6597938144329897, 'test/precision': 0.5245901639344263, 'test/epoch_loss': 0.6240786300765143, 'train/epoch_acc': 0.7469287469287469, 'epoch': 9} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 32, 'learning_rate': 0.0003} resilient-sweep-11
32 30 {'_wandb': {'runtime': 330}, '_timestamp': 1680629038.456323, 'test/epoch_loss': 0.2657569663392173, 'train/epoch_loss': 0.12745249926751018, '_step': 529, '_runtime': 332.23273372650146, 'test/recall': 0.8269230769230769, 'test/f1-score': 0.8958333333333334, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.9772727272727272, 'train/epoch_acc': 0.9717444717444718, 'train/batch_loss': 0.13025684654712677, 'epoch': 9} {'_step': 529, 'epoch': 9, '_runtime': 332.23273372650146, 'test/f1-score': 0.8958333333333334, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.9772727272727272, 'test/epoch_loss': 0.2657569663392173, 'train/batch_loss': 0.13025684654712677, 'train/epoch_loss': 0.12745249926751018, '_wandb': {'runtime': 330}, '_timestamp': 1680629038.456323, 'test/recall': 0.8269230769230769, 'train/epoch_acc': 0.9717444717444718} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.001} serene-sweep-10
33 31 {'test/f1-score': 0.9, 'test/epoch_acc': 0.9111111111111112, 'test/precision': 0.972972972972973, 'test/epoch_loss': 0.23338710864384968, 'train/epoch_acc': 0.9275184275184276, 'train/batch_loss': 0.11391787976026536, 'epoch': 9, '_wandb': {'runtime': 334}, 'train/epoch_loss': 0.2116023584907412, '_timestamp': 1680628699.1189623, 'test/recall': 0.8372093023255814, '_step': 1039, '_runtime': 335.94198656082153} {'test/epoch_loss': 0.23338710864384968, 'train/epoch_loss': 0.2116023584907412, '_wandb': {'runtime': 334}, '_timestamp': 1680628699.1189623, 'test/precision': 0.972972972972973, 'test/recall': 0.8372093023255814, 'test/f1-score': 0.9, 'test/epoch_acc': 0.9111111111111112, 'train/epoch_acc': 0.9275184275184276, 'train/batch_loss': 0.11391787976026536, '_step': 1039, 'epoch': 9, '_runtime': 335.94198656082153} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 8, 'learning_rate': 0.0003} cool-sweep-9
34 32 {'_timestamp': 1680628351.790065, 'test/recall': 0.8863636363636364, 'test/epoch_acc': 0.7777777777777778, 'train/epoch_acc': 0.7702702702702703, 'train/epoch_loss': 0.6034659886828805, 'epoch': 9, '_wandb': {'runtime': 326}, '_runtime': 327.29265093803406, 'test/epoch_loss': 0.5824494547314114, 'train/batch_loss': 0.5777762532234192, '_step': 529, 'test/f1-score': 0.7959183673469388, 'test/precision': 0.7222222222222222} {'_step': 529, 'epoch': 9, '_timestamp': 1680628351.790065, 'test/f1-score': 0.7959183673469388, 'test/epoch_acc': 0.7777777777777778, 'test/epoch_loss': 0.5824494547314114, 'train/epoch_acc': 0.7702702702702703, '_wandb': {'runtime': 326}, '_runtime': 327.29265093803406, 'test/recall': 0.8863636363636364, 'test/precision': 0.7222222222222222, 'train/batch_loss': 0.5777762532234192, 'train/epoch_loss': 0.6034659886828805} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.0001} lilac-sweep-8
35 33 {'epoch': 9, '_runtime': 337.11313247680664, 'test/f1-score': 0.717391304347826, 'test/epoch_acc': 0.7111111111111111, 'test/epoch_loss': 0.6369305915302701, 'train/batch_loss': 0.5935282111167908, '_step': 149, '_timestamp': 1680628016.5942774, 'test/recall': 0.8048780487804879, 'test/precision': 0.6470588235294118, 'train/epoch_acc': 0.7199017199017199, 'train/epoch_loss': 0.618001790392311, '_wandb': {'runtime': 335}} {'_step': 149, '_wandb': {'runtime': 335}, '_timestamp': 1680628016.5942774, 'test/recall': 0.8048780487804879, 'test/f1-score': 0.717391304347826, 'train/epoch_loss': 0.618001790392311, 'train/batch_loss': 0.5935282111167908, 'epoch': 9, '_runtime': 337.11313247680664, 'test/epoch_acc': 0.7111111111111111, 'test/precision': 0.6470588235294118, 'test/epoch_loss': 0.6369305915302701, 'train/epoch_acc': 0.7199017199017199} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 64, 'learning_rate': 0.001} warm-sweep-7
36 34 {'_step': 2059, 'epoch': 9, '_wandb': {'runtime': 354}, '_runtime': 355.7423675060272, '_timestamp': 1680627667.6215644, 'test/epoch_acc': 0.6333333333333333, 'test/epoch_loss': 0.6619265423880683, 'train/epoch_acc': 0.6498771498771498, 'test/recall': 0.8, 'test/f1-score': 0.6857142857142857, 'test/precision': 0.6, 'train/batch_loss': 0.6662057638168335, 'train/epoch_loss': 0.6663250732773353} {'_step': 2059, 'test/f1-score': 0.6857142857142857, 'test/precision': 0.6, 'test/epoch_loss': 0.6619265423880683, 'train/batch_loss': 0.6662057638168335, 'epoch': 9, '_wandb': {'runtime': 354}, '_runtime': 355.7423675060272, '_timestamp': 1680627667.6215644, 'test/recall': 0.8, 'test/epoch_acc': 0.6333333333333333, 'train/epoch_acc': 0.6498771498771498, 'train/epoch_loss': 0.6663250732773353} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.0001} giddy-sweep-6
37 35 {'test/recall': 0.8163265306122449, 'test/f1-score': 0.7766990291262137, 'test/precision': 0.7407407407407407, 'test/epoch_loss': 0.6307997491624621, 'train/epoch_acc': 0.7125307125307125, 'train/batch_loss': 0.6531811356544495, '_wandb': {'runtime': 343}, '_runtime': 344.59358406066895, '_timestamp': 1680627305.434523, 'test/epoch_acc': 0.7444444444444445, 'train/epoch_loss': 0.6398702088093582, '_step': 149, 'epoch': 9} {'epoch': 9, '_runtime': 344.59358406066895, '_timestamp': 1680627305.434523, 'test/precision': 0.7407407407407407, 'train/batch_loss': 0.6531811356544495, 'train/epoch_loss': 0.6398702088093582, 'train/epoch_acc': 0.7125307125307125, '_step': 149, '_wandb': {'runtime': 343}, 'test/recall': 0.8163265306122449, 'test/f1-score': 0.7766990291262137, 'test/epoch_acc': 0.7444444444444445, 'test/epoch_loss': 0.6307997491624621} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 64, 'learning_rate': 0.0001} stellar-sweep-5
38 36 {'test/precision': 0.9705882352941176, 'test/epoch_loss': 0.1906787835785912, 'train/epoch_acc': 0.9975429975429976, '_step': 1039, 'epoch': 9, '_wandb': {'runtime': 334}, 'test/f1-score': 0.9041095890410958, 'train/batch_loss': 0.0006497434806078672, 'train/epoch_loss': 0.02095988139033052, '_runtime': 335.76391553878784, '_timestamp': 1680626951.0603056, 'test/recall': 0.8461538461538461, 'test/epoch_acc': 0.9222222222222224} {'test/recall': 0.8461538461538461, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 0.9705882352941176, 'test/epoch_loss': 0.1906787835785912, 'train/epoch_acc': 0.9975429975429976, '_step': 1039, '_runtime': 335.76391553878784, '_timestamp': 1680626951.0603056, 'train/batch_loss': 0.0006497434806078672, 'train/epoch_loss': 0.02095988139033052, 'epoch': 9, '_wandb': {'runtime': 334}, 'test/f1-score': 0.9041095890410958} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.003} olive-sweep-4
39 37 {'epoch': 9, '_runtime': 333.64992809295654, '_timestamp': 1680626608.419389, 'train/epoch_loss': 0.11751884335528429, 'train/epoch_acc': 0.984029484029484, '_step': 149, '_wandb': {'runtime': 332}, 'test/recall': 0.925, 'test/f1-score': 0.8705882352941177, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.8222222222222222, 'test/epoch_loss': 0.27919367684258356, 'train/batch_loss': 0.12675245106220245} {'test/precision': 0.8222222222222222, 'test/epoch_loss': 0.27919367684258356, '_step': 149, 'epoch': 9, '_runtime': 333.64992809295654, 'test/recall': 0.925, 'test/f1-score': 0.8705882352941177, 'test/epoch_acc': 0.8777777777777778, '_wandb': {'runtime': 332}, '_timestamp': 1680626608.419389, 'train/epoch_acc': 0.984029484029484, 'train/batch_loss': 0.12675245106220245, 'train/epoch_loss': 0.11751884335528429} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.003} dazzling-sweep-3
40 38 {'test/precision': 0.5306122448979592, '_wandb': {'runtime': 336}, '_timestamp': 1680626264.5954974, 'test/recall': 0.6842105263157895, 'test/epoch_acc': 0.6111111111111112, 'test/epoch_loss': 0.6708752089076572, 'train/epoch_acc': 0.6547911547911548, 'train/batch_loss': 0.5270536541938782, 'train/epoch_loss': 0.6389284106085868, '_step': 1039, 'epoch': 9, '_runtime': 337.19885444641113, 'test/f1-score': 0.5977011494252874} {'test/recall': 0.6842105263157895, 'test/precision': 0.5306122448979592, '_wandb': {'runtime': 336}, '_runtime': 337.19885444641113, '_timestamp': 1680626264.5954974, 'test/epoch_acc': 0.6111111111111112, 'test/epoch_loss': 0.6708752089076572, 'train/epoch_acc': 0.6547911547911548, 'train/batch_loss': 0.5270536541938782, 'train/epoch_loss': 0.6389284106085868, '_step': 1039, 'epoch': 9, 'test/f1-score': 0.5977011494252874} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.01} kind-sweep-2
41 39 {'train/epoch_loss': 0.3516608065117782, 'epoch': 9, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.8444444444444444, 'train/epoch_acc': 0.8746928746928747, 'train/batch_loss': 0.3848239779472351, 'test/f1-score': 0.853932584269663, 'test/epoch_loss': 0.38614972366227046, '_step': 529, '_wandb': {'runtime': 337}, '_runtime': 337.9836483001709, '_timestamp': 1680625919.9645753, 'test/recall': 0.8636363636363636} {'test/f1-score': 0.853932584269663, 'test/epoch_loss': 0.38614972366227046, 'train/epoch_acc': 0.8746928746928747, 'train/epoch_loss': 0.3516608065117782, '_step': 529, 'epoch': 9, '_runtime': 337.9836483001709, 'test/recall': 0.8636363636363636, 'train/batch_loss': 0.3848239779472351, '_wandb': {'runtime': 337}, '_timestamp': 1680625919.9645753, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.8444444444444444} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 16, 'learning_rate': 0.003} morning-sweep-1
42 40 {'train/epoch_loss': 0.02368298517580857, 'epoch': 9, 'test/recall': 0.8653846153846154, 'test/f1-score': 0.9, 'test/precision': 0.9375, 'test/epoch_acc': 0.888888888888889, 'test/epoch_loss': 0.25786760796585845, 'train/epoch_acc': 0.9975429975429976, 'train/batch_loss': 0.05631007254123688, '_step': 2059, '_wandb': {'runtime': 346}, '_runtime': 347.9354045391083, '_timestamp': 1680624250.2654595} {'_runtime': 347.9354045391083, '_timestamp': 1680624250.2654595, 'test/recall': 0.8653846153846154, 'test/epoch_loss': 0.25786760796585845, 'train/batch_loss': 0.05631007254123688, 'train/epoch_loss': 0.02368298517580857, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 346}, 'test/f1-score': 0.9, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.9375, 'train/epoch_acc': 0.9975429975429976} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 4, 'learning_rate': 0.1} valiant-sweep-23
43 41 {'train/batch_loss': 0.5639374256134033, '_timestamp': 1680623895.362503, 'test/recall': 0.8936170212765957, 'test/f1-score': 0.8571428571428571, 'test/epoch_acc': 0.8444444444444444, 'test/precision': 0.8235294117647058, 'test/epoch_loss': 0.490613665845659, 'train/epoch_acc': 0.8243243243243243, '_step': 1039, 'epoch': 9, '_wandb': {'runtime': 327}, '_runtime': 329.4802031517029, 'train/epoch_loss': 0.48581602795996887} {'test/epoch_acc': 0.8444444444444444, 'test/epoch_loss': 0.490613665845659, 'train/epoch_acc': 0.8243243243243243, '_step': 1039, '_timestamp': 1680623895.362503, 'test/recall': 0.8936170212765957, 'test/f1-score': 0.8571428571428571, 'test/precision': 0.8235294117647058, 'train/batch_loss': 0.5639374256134033, 'train/epoch_loss': 0.48581602795996887, 'epoch': 9, '_wandb': {'runtime': 327}, '_runtime': 329.4802031517029} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0003} earnest-sweep-22
44 42 {'_timestamp': 1680623556.4586525, 'test/recall': 0.9148936170212766, 'test/f1-score': 0.9052631578947368, 'test/epoch_acc': 0.9, 'test/precision': 0.8958333333333334, 'test/epoch_loss': 0.2318242397573259, 'train/epoch_acc': 0.995085995085995, 'epoch': 9, '_wandb': {'runtime': 326}, '_runtime': 328.0050995349884, 'train/batch_loss': 0.06110217794775963, 'train/epoch_loss': 0.05107141801451289, '_step': 149} {'test/epoch_acc': 0.9, 'test/precision': 0.8958333333333334, 'train/batch_loss': 0.06110217794775963, 'train/epoch_loss': 0.05107141801451289, 'epoch': 9, '_wandb': {'runtime': 326}, 'test/recall': 0.9148936170212766, 'test/f1-score': 0.9052631578947368, 'train/epoch_acc': 0.995085995085995, '_step': 149, '_runtime': 328.0050995349884, '_timestamp': 1680623556.4586525, 'test/epoch_loss': 0.2318242397573259} {'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 64, 'learning_rate': 0.003} genial-sweep-21
45 43 {'_runtime': 327.10622239112854, '_timestamp': 1680623221.0825984, 'test/recall': 0.8723404255319149, 'test/epoch_acc': 0.7444444444444445, 'test/epoch_loss': 0.5943129923608568, 'train/epoch_acc': 0.7911547911547911, '_step': 149, '_wandb': {'runtime': 325}, 'train/epoch_loss': 0.5714027147914034, 'test/precision': 0.7068965517241379, 'train/batch_loss': 0.6166229844093323, 'epoch': 9, 'test/f1-score': 0.780952380952381} {'_runtime': 327.10622239112854, 'test/f1-score': 0.780952380952381, 'test/epoch_acc': 0.7444444444444445, 'test/epoch_loss': 0.5943129923608568, 'train/batch_loss': 0.6166229844093323, 'train/epoch_loss': 0.5714027147914034, '_step': 149, 'epoch': 9, '_wandb': {'runtime': 325}, '_timestamp': 1680623221.0825984, 'test/recall': 0.8723404255319149, 'test/precision': 0.7068965517241379, 'train/epoch_acc': 0.7911547911547911} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 64, 'learning_rate': 0.001} lemon-sweep-20
46 44 {'_runtime': 331.60892701148987, 'test/recall': 0.7021276595744681, 'test/epoch_acc': 0.6, 'test/precision': 0.6, 'test/epoch_loss': 0.6746161646313138, 'train/batch_loss': 0.7205827236175537, '_step': 1039, '_wandb': {'runtime': 330}, '_timestamp': 1680622885.059607, 'test/f1-score': 0.6470588235294118, 'train/epoch_acc': 0.6277641277641277, 'train/epoch_loss': 0.6722187732302879, 'epoch': 9} {'train/epoch_acc': 0.6277641277641277, 'train/epoch_loss': 0.6722187732302879, '_runtime': 331.60892701148987, 'test/recall': 0.7021276595744681, 'test/epoch_acc': 0.6, 'test/epoch_loss': 0.6746161646313138, 'test/f1-score': 0.6470588235294118, 'test/precision': 0.6, 'train/batch_loss': 0.7205827236175537, '_step': 1039, 'epoch': 9, '_wandb': {'runtime': 330}, '_timestamp': 1680622885.059607} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0001} ancient-sweep-19
47 45 {'_wandb': {'runtime': 347}, '_runtime': 348.9979507923126, '_timestamp': 1680622545.2735748, 'test/f1-score': 0.898876404494382, 'test/epoch_acc': 0.9, 'test/epoch_loss': 0.24883262103216516, '_step': 2059, 'epoch': 9, 'train/epoch_acc': 0.9877149877149876, 'train/epoch_loss': 0.0466749508011656, 'train/batch_loss': 0.015468262135982512, 'test/recall': 0.8695652173913043, 'test/precision': 0.9302325581395348} {'train/epoch_loss': 0.0466749508011656, '_step': 2059, 'epoch': 9, 'test/recall': 0.8695652173913043, 'test/precision': 0.9302325581395348, 'test/epoch_loss': 0.24883262103216516, 'train/batch_loss': 0.015468262135982512, '_wandb': {'runtime': 347}, '_runtime': 348.9979507923126, '_timestamp': 1680622545.2735748, 'test/f1-score': 0.898876404494382, 'test/epoch_acc': 0.9, 'train/epoch_acc': 0.9877149877149876} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.01} smart-sweep-18
48 46 {'test/precision': 0.945945945945946, 'epoch': 9, '_wandb': {'runtime': 328}, '_runtime': 329.3028633594513, '_timestamp': 1680622188.8210304, 'test/recall': 0.8536585365853658, 'test/f1-score': 0.8974358974358975, '_step': 1039, 'test/epoch_acc': 0.9111111111111112, 'test/epoch_loss': 0.2015038196825319, 'train/epoch_acc': 0.9815724815724816, 'train/batch_loss': 0.007225348148494959, 'train/epoch_loss': 0.07856258183731457} {'train/epoch_acc': 0.9815724815724816, 'train/batch_loss': 0.007225348148494959, 'train/epoch_loss': 0.07856258183731457, '_step': 1039, 'epoch': 9, '_runtime': 329.3028633594513, '_timestamp': 1680622188.8210304, 'test/recall': 0.8536585365853658, '_wandb': {'runtime': 328}, 'test/f1-score': 0.8974358974358975, 'test/epoch_acc': 0.9111111111111112, 'test/precision': 0.945945945945946, 'test/epoch_loss': 0.2015038196825319} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.003} sleek-sweep-17
49 47 {'_step': 279, 'epoch': 9, '_wandb': {'runtime': 321}, '_timestamp': 1680621849.979658, 'train/epoch_acc': 0.828009828009828, 'train/batch_loss': 0.6047794222831726, 'train/epoch_loss': 0.5808350268101516, '_runtime': 323.3842430114746, 'test/recall': 0.8301886792452831, 'test/f1-score': 0.8543689320388349, 'test/epoch_acc': 0.8333333333333334, 'test/precision': 0.88, 'test/epoch_loss': 0.5843977000978258} {'test/precision': 0.88, 'test/epoch_loss': 0.5843977000978258, 'train/epoch_acc': 0.828009828009828, 'train/batch_loss': 0.6047794222831726, '_wandb': {'runtime': 321}, 'test/recall': 0.8301886792452831, '_runtime': 323.3842430114746, '_timestamp': 1680621849.979658, 'test/f1-score': 0.8543689320388349, 'test/epoch_acc': 0.8333333333333334, 'train/epoch_loss': 0.5808350268101516, '_step': 279, 'epoch': 9} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 32, 'learning_rate': 0.0001} winter-sweep-16
50 48 {'test/recall': 0.85, 'train/batch_loss': 0.001602485659532249, 'epoch': 9, '_wandb': {'runtime': 346}, '_timestamp': 1680621511.323635, 'test/f1-score': 0.85, 'test/epoch_acc': 0.8666666666666667, 'test/precision': 0.85, 'test/epoch_loss': 0.5281610590923164, 'train/epoch_acc': 0.995085995085995, '_step': 2059, '_runtime': 347.8050694465637, 'train/epoch_loss': 0.029015880939893934} {'epoch': 9, '_runtime': 347.8050694465637, '_timestamp': 1680621511.323635, 'test/recall': 0.85, 'test/precision': 0.85, 'test/epoch_loss': 0.5281610590923164, 'train/epoch_acc': 0.995085995085995, 'train/batch_loss': 0.001602485659532249, 'train/epoch_loss': 0.029015880939893934, '_step': 2059, '_wandb': {'runtime': 346}, 'test/f1-score': 0.85, 'test/epoch_acc': 0.8666666666666667} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.1} rare-sweep-15
51 49 {'_step': 2059, 'epoch': 9, '_wandb': {'runtime': 346}, '_runtime': 347.7671456336975, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 0.9487179487179488, 'train/epoch_loss': 0.04606454834343147, '_timestamp': 1680621147.5604067, 'test/recall': 0.8809523809523809, 'test/f1-score': 0.9135802469135802, 'test/epoch_loss': 0.22225395898438163, 'train/epoch_acc': 0.9864864864864864, 'train/batch_loss': 0.010366588830947876} {'_step': 2059, '_wandb': {'runtime': 346}, '_runtime': 347.7671456336975, 'test/recall': 0.8809523809523809, 'test/f1-score': 0.9135802469135802, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 0.9487179487179488, 'train/batch_loss': 0.010366588830947876, 'epoch': 9, '_timestamp': 1680621147.5604067, 'test/epoch_loss': 0.22225395898438163, 'train/epoch_acc': 0.9864864864864864, 'train/epoch_loss': 0.04606454834343147} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.001} stoic-sweep-14
52 50 {'train/epoch_acc': 0.6523341523341524, 'train/batch_loss': 0.6023905277252197, '_wandb': {'runtime': 351}, '_timestamp': 1680620790.920825, 'test/recall': 0.675, 'test/f1-score': 0.6585365853658537, 'test/precision': 0.6428571428571429, 'test/epoch_loss': 0.661226307021247, 'train/epoch_loss': 0.6673213337211703, '_step': 2059, 'epoch': 9, '_runtime': 352.6435329914093, 'test/epoch_acc': 0.6888888888888889} {'test/precision': 0.6428571428571429, 'test/epoch_loss': 0.661226307021247, 'train/epoch_acc': 0.6523341523341524, 'train/batch_loss': 0.6023905277252197, '_step': 2059, '_wandb': {'runtime': 351}, 'test/epoch_acc': 0.6888888888888889, 'test/recall': 0.675, 'test/f1-score': 0.6585365853658537, 'train/epoch_loss': 0.6673213337211703, 'epoch': 9, '_runtime': 352.6435329914093, '_timestamp': 1680620790.920825} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.0001} glorious-sweep-13
53 51 {'epoch': 9, '_wandb': {'runtime': 329}, 'test/recall': 0.9574468085106383, 'test/f1-score': 0.9782608695652174, 'test/precision': 1, 'train/batch_loss': 0.004083937965333462, 'train/epoch_loss': 0.0071195896911716286, '_step': 149, '_runtime': 330.7649688720703, '_timestamp': 1680620431.024078, 'test/epoch_acc': 0.977777777777778, 'test/epoch_loss': 0.1352142873737547, 'train/epoch_acc': 1} {'epoch': 9, '_wandb': {'runtime': 329}, 'test/recall': 0.9574468085106383, 'test/f1-score': 0.9782608695652174, 'train/epoch_acc': 1, 'train/batch_loss': 0.004083937965333462, 'train/epoch_loss': 0.0071195896911716286, '_step': 149, '_runtime': 330.7649688720703, '_timestamp': 1680620431.024078, 'test/epoch_acc': 0.977777777777778, 'test/precision': 1, 'test/epoch_loss': 0.1352142873737547} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.01} chocolate-sweep-12
54 52 {'train/epoch_loss': 0.5577488642652731, '_step': 149, '_wandb': {'runtime': 328}, 'test/recall': 0.926829268292683, 'test/f1-score': 0.8636363636363636, 'test/precision': 0.8085106382978723, 'train/epoch_acc': 0.800982800982801, 'train/batch_loss': 0.5299303531646729, 'epoch': 9, '_runtime': 329.12984681129456, '_timestamp': 1680620092.0697718, 'test/epoch_acc': 0.8666666666666667, 'test/epoch_loss': 0.5375637359089321} {'test/epoch_loss': 0.5375637359089321, 'train/epoch_acc': 0.800982800982801, '_step': 149, '_timestamp': 1680620092.0697718, 'test/recall': 0.926829268292683, 'test/f1-score': 0.8636363636363636, 'test/epoch_acc': 0.8666666666666667, 'test/precision': 0.8085106382978723, 'train/batch_loss': 0.5299303531646729, 'train/epoch_loss': 0.5577488642652731, 'epoch': 9, '_wandb': {'runtime': 328}, '_runtime': 329.12984681129456} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.0003} glowing-sweep-11
55 53 {'train/epoch_acc': 0.8611793611793611, '_step': 279, 'epoch': 9, '_wandb': {'runtime': 322}, '_timestamp': 1680619755.0191748, 'test/f1-score': 0.7659574468085105, 'train/batch_loss': 0.5281365513801575, 'train/epoch_loss': 0.46212616409072127, '_runtime': 324.3058567047119, 'test/recall': 0.7659574468085106, 'test/epoch_acc': 0.7555555555555555, 'test/precision': 0.7659574468085106, 'test/epoch_loss': 0.5337554746203952} {'test/epoch_acc': 0.7555555555555555, 'test/precision': 0.7659574468085106, 'test/epoch_loss': 0.5337554746203952, 'train/epoch_loss': 0.46212616409072127, '_runtime': 324.3058567047119, '_timestamp': 1680619755.0191748, 'test/recall': 0.7659574468085106, 'test/f1-score': 0.7659574468085105, 'train/batch_loss': 0.5281365513801575, '_step': 279, 'epoch': 9, '_wandb': {'runtime': 322}, 'train/epoch_acc': 0.8611793611793611} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 32, 'learning_rate': 0.003} different-sweep-10
56 54 {'test/epoch_loss': 0.5470490535100301, 'train/batch_loss': 0.6183260083198547, '_step': 279, 'epoch': 9, '_runtime': 327.0705659389496, '_timestamp': 1680619423.656795, 'test/recall': 0.9523809523809524, 'test/precision': 0.7843137254901961, '_wandb': {'runtime': 325}, 'test/f1-score': 0.8602150537634408, 'test/epoch_acc': 0.8555555555555556, 'train/epoch_acc': 0.8058968058968059, 'train/epoch_loss': 0.5580001385557564} {'_step': 279, '_wandb': {'runtime': 325}, '_runtime': 327.0705659389496, 'train/epoch_loss': 0.5580001385557564, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.7843137254901961, 'test/epoch_loss': 0.5470490535100301, 'train/epoch_acc': 0.8058968058968059, 'epoch': 9, '_timestamp': 1680619423.656795, 'test/recall': 0.9523809523809524, 'test/f1-score': 0.8602150537634408, 'train/batch_loss': 0.6183260083198547} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.003} lilac-sweep-9
57 55 {'test/f1-score': 0.7956989247311828, 'test/precision': 0.8409090909090909, 'train/batch_loss': 0.6300776600837708, '_step': 529, 'epoch': 9, '_runtime': 328.68579959869385, '_timestamp': 1680619089.5332966, 'test/recall': 0.7551020408163265, 'train/epoch_loss': 0.46969629490990605, '_wandb': {'runtime': 327}, 'test/epoch_acc': 0.788888888888889, 'test/epoch_loss': 0.46168507006433274, 'train/epoch_acc': 0.773955773955774} {'test/recall': 0.7551020408163265, 'test/f1-score': 0.7956989247311828, 'test/epoch_acc': 0.788888888888889, 'train/epoch_acc': 0.773955773955774, 'train/batch_loss': 0.6300776600837708, '_step': 529, 'epoch': 9, '_wandb': {'runtime': 327}, 'train/epoch_loss': 0.46969629490990605, 'test/epoch_loss': 0.46168507006433274, '_runtime': 328.68579959869385, '_timestamp': 1680619089.5332966, 'test/precision': 0.8409090909090909} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.1} crimson-sweep-8
58 56 {'test/recall': 0.8181818181818182, 'test/epoch_loss': 0.44089303129391433, 'train/epoch_acc': 0.9938574938574938, 'train/epoch_loss': 0.02176519967463292, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.9375, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 349}, '_runtime': 350.2308712005615, '_timestamp': 1680618753.2361271, 'test/f1-score': 0.8737864077669902, 'train/batch_loss': 0.011611333116889} {'test/precision': 0.9375, 'test/epoch_loss': 0.44089303129391433, 'train/batch_loss': 0.011611333116889, 'epoch': 9, '_runtime': 350.2308712005615, 'test/f1-score': 0.8737864077669902, 'test/recall': 0.8181818181818182, 'test/epoch_acc': 0.8555555555555556, 'train/epoch_acc': 0.9938574938574938, 'train/epoch_loss': 0.02176519967463292, '_step': 2059, '_wandb': {'runtime': 349}, '_timestamp': 1680618753.2361271} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.003} still-sweep-7
59 57 {'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.85, 'test/epoch_loss': 0.24035142682841976, 'train/epoch_acc': 0.9938574938574938, 'epoch': 9, '_wandb': {'runtime': 333}, 'test/recall': 0.8717948717948718, 'test/f1-score': 0.8607594936708861, 'train/epoch_loss': 0.02099113287724536, '_step': 1039, '_runtime': 334.69481587409973, '_timestamp': 1680618396.0194488, 'train/batch_loss': 0.030084805563092232} {'train/epoch_loss': 0.02099113287724536, '_timestamp': 1680618396.0194488, 'test/f1-score': 0.8607594936708861, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.85, 'train/batch_loss': 0.030084805563092232, 'test/epoch_loss': 0.24035142682841976, 'train/epoch_acc': 0.9938574938574938, '_step': 1039, 'epoch': 9, '_wandb': {'runtime': 333}, '_runtime': 334.69481587409973, 'test/recall': 0.8717948717948718} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.01} charmed-sweep-6
60 58 {'epoch': 9, '_wandb': {'runtime': 335}, '_timestamp': 1680618051.044084, 'train/epoch_acc': 0.9963144963144964, 'train/epoch_loss': 0.010693324584853135, '_step': 1039, 'test/recall': 0.8780487804878049, 'test/f1-score': 0.8674698795180722, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.8571428571428571, 'test/epoch_loss': 0.5385394818252988, 'train/batch_loss': 0.001848929445259273, '_runtime': 336.1621870994568} {'train/epoch_acc': 0.9963144963144964, 'train/batch_loss': 0.001848929445259273, 'train/epoch_loss': 0.010693324584853135, '_wandb': {'runtime': 335}, '_runtime': 336.1621870994568, 'test/recall': 0.8780487804878049, 'test/f1-score': 0.8674698795180722, 'test/precision': 0.8571428571428571, '_step': 1039, 'epoch': 9, '_timestamp': 1680618051.044084, 'test/epoch_acc': 0.8777777777777778, 'test/epoch_loss': 0.5385394818252988} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 8, 'learning_rate': 0.0003} restful-sweep-5
61 59 {'train/epoch_acc': 1, 'train/batch_loss': 0.004928763955831528, 'train/epoch_loss': 0.004462716538065481, '_step': 149, '_runtime': 334.4848310947418, 'test/f1-score': 0.8409090909090909, 'test/epoch_acc': 0.8444444444444444, 'test/precision': 0.8409090909090909, 'epoch': 9, '_wandb': {'runtime': 333}, '_timestamp': 1680617708.075962, 'test/recall': 0.8409090909090909, 'test/epoch_loss': 0.6238909363746643} {'test/recall': 0.8409090909090909, 'test/f1-score': 0.8409090909090909, 'test/epoch_acc': 0.8444444444444444, '_wandb': {'runtime': 333}, '_timestamp': 1680617708.075962, '_runtime': 334.4848310947418, 'test/precision': 0.8409090909090909, 'test/epoch_loss': 0.6238909363746643, 'train/epoch_acc': 1, 'train/batch_loss': 0.004928763955831528, 'train/epoch_loss': 0.004462716538065481, '_step': 149, 'epoch': 9} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.1} proud-sweep-4
62 60 {'epoch': 9, '_runtime': 338.4922821521759, '_timestamp': 1680617365.2791553, 'test/recall': 0.75, 'test/f1-score': 0.4778761061946903, 'test/precision': 0.35064935064935066, 'test/epoch_loss': 0.7233364171451993, 'train/epoch_acc': 0.5626535626535626, 'train/batch_loss': 0.6750851273536682, 'train/epoch_loss': 0.6796711432845938, '_step': 149, '_wandb': {'runtime': 337}, 'test/epoch_acc': 0.34444444444444444} {'_step': 149, 'epoch': 9, '_wandb': {'runtime': 337}, 'test/f1-score': 0.4778761061946903, 'test/precision': 0.35064935064935066, 'train/epoch_acc': 0.5626535626535626, 'train/epoch_loss': 0.6796711432845938, '_runtime': 338.4922821521759, '_timestamp': 1680617365.2791553, 'test/recall': 0.75, 'test/epoch_acc': 0.34444444444444444, 'test/epoch_loss': 0.7233364171451993, 'train/batch_loss': 0.6750851273536682} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 64, 'learning_rate': 0.0001} visionary-sweep-3
63 61 {'test/recall': 1, 'test/f1-score': 0.59375, 'test/epoch_loss': 109.22879723442924, 'train/epoch_acc': 0.5147420147420148, '_step': 110, 'epoch': 3, '_runtime': 129.48883533477783, '_timestamp': 1680617007.4126654, 'train/batch_loss': 1.2695436477661133, 'train/epoch_loss': 3.225923076601521, '_wandb': {'runtime': 132}, 'test/epoch_acc': 0.4222222222222222, 'test/precision': 0.4222222222222222} {'epoch': 3, '_wandb': {'runtime': 132}, '_runtime': 129.48883533477783, 'test/f1-score': 0.59375, 'test/epoch_acc': 0.4222222222222222, 'test/precision': 0.4222222222222222, 'train/epoch_acc': 0.5147420147420148, 'train/batch_loss': 1.2695436477661133, '_step': 110, '_timestamp': 1680617007.4126654, 'test/recall': 1, 'test/epoch_loss': 109.22879723442924, 'train/epoch_loss': 3.225923076601521} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 32, 'learning_rate': 0.1} splendid-sweep-2
64 62 {'train/epoch_loss': 0.5949591096554693, '_step': 1039, 'epoch': 9, 'test/recall': 0.8636363636363636, 'test/f1-score': 0.8172043010752688, 'test/precision': 0.7755102040816326, 'test/epoch_loss': 0.6018742865986294, '_wandb': {'runtime': 372}, '_runtime': 373.84231185913086, '_timestamp': 1680616870.0621138, 'test/epoch_acc': 0.8111111111111111, 'train/epoch_acc': 0.7727272727272727, 'train/batch_loss': 0.563504695892334} {'test/precision': 0.7755102040816326, 'test/epoch_loss': 0.6018742865986294, 'train/epoch_loss': 0.5949591096554693, '_step': 1039, 'epoch': 9, 'test/recall': 0.8636363636363636, 'test/f1-score': 0.8172043010752688, 'test/epoch_acc': 0.8111111111111111, '_wandb': {'runtime': 372}, '_runtime': 373.84231185913086, '_timestamp': 1680616870.0621138, 'train/epoch_acc': 0.7727272727272727, 'train/batch_loss': 0.563504695892334} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0001} snowy-sweep-1
65 63 {'_timestamp': 1678798635.5359335, 'test/recall': 0.5813953488372093, 'test/epoch_acc': 0.6333333333333333, 'test/precision': 0.625, 'train/epoch_loss': 0.684732110699506, '_step': 529, '_runtime': 333.6077947616577, 'test/f1-score': 0.6024096385542168, 'test/epoch_loss': 0.6787986318270366, 'train/epoch_acc': 0.5552825552825553, 'train/batch_loss': 0.7118003964424133, 'epoch': 9, '_wandb': {'runtime': 327}} {'epoch': 9, '_wandb': {'runtime': 327}, 'test/precision': 0.625, 'train/epoch_acc': 0.5552825552825553, 'train/epoch_loss': 0.684732110699506, '_step': 529, '_runtime': 333.6077947616577, '_timestamp': 1678798635.5359335, 'test/recall': 0.5813953488372093, 'test/f1-score': 0.6024096385542168, 'test/epoch_acc': 0.6333333333333333, 'test/epoch_loss': 0.6787986318270366, 'train/batch_loss': 0.7118003964424133} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.0001} comic-sweep-38
66 64 {'test/epoch_loss': 0.5120628664890925, 'train/epoch_acc': 1, '_wandb': {'runtime': 337}, '_runtime': 342.7867271900177, '_timestamp': 1678798288.876002, 'test/recall': 1, 'test/f1-score': 0.888888888888889, 'test/precision': 0.8, 'train/epoch_loss': 0.001254009526264133, '_step': 149, 'epoch': 9, 'test/epoch_acc': 0.888888888888889, 'train/batch_loss': 0.0015535189304500818} {'_step': 149, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.8, 'train/epoch_acc': 1, 'test/epoch_loss': 0.5120628664890925, 'train/batch_loss': 0.0015535189304500818, 'epoch': 9, '_wandb': {'runtime': 337}, '_runtime': 342.7867271900177, '_timestamp': 1678798288.876002, 'test/recall': 1, 'test/f1-score': 0.888888888888889, 'train/epoch_loss': 0.001254009526264133} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.1} magic-sweep-37
67 65 {'test/f1-score': 0.6190476190476191, 'test/epoch_loss': 0.6593369828330146, 'train/batch_loss': 0.6705241203308105, 'train/epoch_loss': 0.659313001562395, 'epoch': 9, '_runtime': 338.4290623664856, '_timestamp': 1678797929.8979273, 'test/recall': 0.6341463414634146, 'test/epoch_acc': 0.6444444444444445, 'test/precision': 0.6046511627906976, 'train/epoch_acc': 0.6572481572481572, '_step': 279, '_wandb': {'runtime': 332}} {'test/epoch_loss': 0.6593369828330146, '_step': 279, '_wandb': {'runtime': 332}, '_runtime': 338.4290623664856, '_timestamp': 1678797929.8979273, 'test/recall': 0.6341463414634146, 'test/f1-score': 0.6190476190476191, 'test/epoch_acc': 0.6444444444444445, 'train/epoch_acc': 0.6572481572481572, 'train/batch_loss': 0.6705241203308105, 'epoch': 9, 'test/precision': 0.6046511627906976, 'train/epoch_loss': 0.659313001562395} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 32, 'learning_rate': 0.0003} azure-sweep-36
68 66 {'test/epoch_acc': 0.9, 'test/epoch_loss': 0.5167779392666287, '_step': 1039, '_wandb': {'runtime': 343}, '_timestamp': 1678797575.4461255, 'test/recall': 0.8703703703703703, 'test/f1-score': 0.912621359223301, 'test/precision': 0.9591836734693876, 'train/epoch_acc': 0.7911547911547911, 'train/batch_loss': 0.5475739240646362, 'epoch': 9, '_runtime': 349.1018385887146, 'train/epoch_loss': 0.542006236622316} {'test/precision': 0.9591836734693876, 'train/epoch_acc': 0.7911547911547911, 'train/batch_loss': 0.5475739240646362, 'epoch': 9, '_wandb': {'runtime': 343}, 'test/recall': 0.8703703703703703, 'test/f1-score': 0.912621359223301, 'test/epoch_acc': 0.9, '_step': 1039, '_runtime': 349.1018385887146, '_timestamp': 1678797575.4461255, 'test/epoch_loss': 0.5167779392666287, 'train/epoch_loss': 0.542006236622316} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.001} easy-sweep-35
69 67 {'test/epoch_loss': 0.27850865055532065, 'train/batch_loss': 4.9947026127483696e-05, 'train/epoch_loss': 0.012833298822080874, '_timestamp': 1678797212.2311337, 'test/recall': 0.8611111111111112, '_wandb': {'runtime': 362}, '_runtime': 367.9372293949127, 'test/f1-score': 0.8611111111111112, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.8611111111111112, 'train/epoch_acc': 0.9987714987714988, '_step': 2059, 'epoch': 9} {'_timestamp': 1678797212.2311337, 'test/f1-score': 0.8611111111111112, 'test/epoch_loss': 0.27850865055532065, 'train/batch_loss': 4.9947026127483696e-05, 'train/epoch_loss': 0.012833298822080874, 'train/epoch_acc': 0.9987714987714988, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 362}, '_runtime': 367.9372293949127, 'test/recall': 0.8611111111111112, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.8611111111111112} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.003} usual-sweep-34
70 68 {'_step': 529, '_runtime': 335.99687933921814, 'test/f1-score': 0.903846153846154, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.8392857142857143, 'test/epoch_loss': 0.6554473309053315, 'epoch': 9, '_wandb': {'runtime': 330}, '_timestamp': 1678796827.8409674, 'test/recall': 0.9791666666666666, 'train/epoch_acc': 0.9742014742014742, 'train/batch_loss': 0.17918632924556732, 'train/epoch_loss': 0.07036763163974523} {'_step': 529, 'test/f1-score': 0.903846153846154, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.8392857142857143, 'train/epoch_acc': 0.9742014742014742, 'test/epoch_loss': 0.6554473309053315, 'train/batch_loss': 0.17918632924556732, 'train/epoch_loss': 0.07036763163974523, 'epoch': 9, '_wandb': {'runtime': 330}, '_runtime': 335.99687933921814, '_timestamp': 1678796827.8409674, 'test/recall': 0.9791666666666666} {'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 16, 'learning_rate': 0.0003} polar-sweep-33
71 69 {'epoch': 9, '_runtime': 336.63737440109253, 'test/f1-score': 0.7356321839080459, 'test/epoch_acc': 0.7444444444444445, 'test/precision': 0.64, 'test/epoch_loss': 0.5271965821584066, 'train/epoch_acc': 0.8660933660933661, 'train/epoch_loss': 0.47513497564072105, '_step': 149, '_wandb': {'runtime': 330}, '_timestamp': 1678796468.9253614, 'test/recall': 0.8648648648648649, 'train/batch_loss': 0.4695126414299011} {'_step': 149, '_runtime': 336.63737440109253, '_timestamp': 1678796468.9253614, 'test/recall': 0.8648648648648649, 'test/precision': 0.64, 'train/epoch_acc': 0.8660933660933661, 'train/epoch_loss': 0.47513497564072105, 'epoch': 9, '_wandb': {'runtime': 330}, 'test/f1-score': 0.7356321839080459, 'test/epoch_acc': 0.7444444444444445, 'test/epoch_loss': 0.5271965821584066, 'train/batch_loss': 0.4695126414299011} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 64, 'learning_rate': 0.001} still-sweep-32
72 70 {'train/batch_loss': 0.711412787437439, 'train/epoch_loss': 0.09577267487700432, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 372}, '_timestamp': 1678796117.3062005, 'test/f1-score': 0.868421052631579, 'test/epoch_acc': 0.888888888888889, '_runtime': 378.4032835960388, 'test/recall': 0.8048780487804879, 'test/precision': 0.9428571428571428, 'test/epoch_loss': 0.2378266812198692, 'train/epoch_acc': 0.9705159705159704} {'_runtime': 378.4032835960388, '_timestamp': 1678796117.3062005, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.9428571428571428, 'train/epoch_acc': 0.9705159705159704, 'train/batch_loss': 0.711412787437439, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 372}, 'test/recall': 0.8048780487804879, 'test/f1-score': 0.868421052631579, 'test/epoch_loss': 0.2378266812198692, 'train/epoch_loss': 0.09577267487700432} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.001} misty-sweep-31
73 71 {'_step': 529, 'epoch': 9, '_wandb': {'runtime': 333}, '_runtime': 336.8808288574219, '_timestamp': 1678795725.918603, 'test/recall': 0.8260869565217391, 'test/f1-score': 0.8636363636363636, 'test/epoch_acc': 0.8666666666666667, 'train/epoch_acc': 0.9926289926289926, 'test/precision': 0.9047619047619048, 'test/epoch_loss': 0.27924135790930854, 'train/batch_loss': 0.04936826974153519, 'train/epoch_loss': 0.05967479737370254} {'train/epoch_acc': 0.9926289926289926, 'train/epoch_loss': 0.05967479737370254, '_step': 529, 'epoch': 9, '_runtime': 336.8808288574219, 'test/recall': 0.8260869565217391, 'test/f1-score': 0.8636363636363636, 'test/precision': 0.9047619047619048, '_wandb': {'runtime': 333}, '_timestamp': 1678795725.918603, 'test/epoch_acc': 0.8666666666666667, 'test/epoch_loss': 0.27924135790930854, 'train/batch_loss': 0.04936826974153519} {'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.001} flowing-sweep-30
74 72 {'_step': 279, 'epoch': 9, '_wandb': {'runtime': 336}, '_runtime': 339.73244285583496, 'test/f1-score': 0.898876404494382, 'test/epoch_acc': 0.9, 'test/precision': 0.9523809523809524, 'test/epoch_loss': 0.37525106337335373, 'train/epoch_loss': 0.3784469199122024, '_timestamp': 1678795319.518895, 'test/recall': 0.851063829787234, 'train/epoch_acc': 0.8722358722358722, 'train/batch_loss': 0.4592914581298828} {'epoch': 9, 'test/recall': 0.851063829787234, 'test/epoch_loss': 0.37525106337335373, 'train/epoch_acc': 0.8722358722358722, 'train/batch_loss': 0.4592914581298828, 'train/epoch_loss': 0.3784469199122024, 'test/precision': 0.9523809523809524, '_step': 279, '_wandb': {'runtime': 336}, '_runtime': 339.73244285583496, '_timestamp': 1678795319.518895, 'test/f1-score': 0.898876404494382, 'test/epoch_acc': 0.9} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.001} deep-sweep-28
75 73 {'_timestamp': 1678794965.2675128, 'test/f1-score': 0.6849315068493151, 'test/epoch_acc': 0.7444444444444445, 'test/precision': 0.7575757575757576, 'test/epoch_loss': 0.5484810524516636, 'epoch': 9, '_wandb': {'runtime': 377}, '_runtime': 381.0768678188324, 'train/epoch_acc': 0.7899262899262899, 'train/batch_loss': 0.6763702630996704, 'train/epoch_loss': 0.5319552311733255, '_step': 2059, 'test/recall': 0.625} {'test/precision': 0.7575757575757576, 'train/epoch_acc': 0.7899262899262899, 'train/epoch_loss': 0.5319552311733255, '_wandb': {'runtime': 377}, '_timestamp': 1678794965.2675128, 'test/recall': 0.625, 'test/f1-score': 0.6849315068493151, 'test/epoch_acc': 0.7444444444444445, 'test/epoch_loss': 0.5484810524516636, 'train/batch_loss': 0.6763702630996704, '_step': 2059, 'epoch': 9, '_runtime': 381.0768678188324} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.0001} glorious-sweep-27
76 74 {'_step': 529, 'epoch': 9, '_wandb': {'runtime': 334}, '_runtime': 338.11463618278503, '_timestamp': 1678794572.9156363, 'test/recall': 0.813953488372093, 'test/epoch_acc': 0.7555555555555555, 'test/epoch_loss': 0.5729872869120703, 'train/epoch_acc': 0.8968058968058967, 'train/batch_loss': 0.4391788542270661, 'test/f1-score': 0.7608695652173914, 'test/precision': 0.7142857142857143, 'train/epoch_loss': 0.2699748155379471} {'epoch': 9, '_runtime': 338.11463618278503, 'test/recall': 0.813953488372093, 'test/precision': 0.7142857142857143, 'train/epoch_loss': 0.2699748155379471, '_step': 529, '_timestamp': 1678794572.9156363, 'test/f1-score': 0.7608695652173914, 'test/epoch_acc': 0.7555555555555555, 'test/epoch_loss': 0.5729872869120703, 'train/epoch_acc': 0.8968058968058967, 'train/batch_loss': 0.4391788542270661, '_wandb': {'runtime': 334}} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.1} stoic-sweep-26
77 75 {'test/epoch_loss': 0.3083995895563728, '_step': 2059, '_wandb': {'runtime': 377}, '_timestamp': 1678794222.848524, 'test/recall': 0.8863636363636364, 'test/f1-score': 0.8666666666666666, 'test/precision': 0.8478260869565217, 'epoch': 9, '_runtime': 380.8983037471771, 'test/epoch_acc': 0.8666666666666667, 'train/epoch_acc': 0.9877149877149876, 'train/batch_loss': 0.025906365364789963, 'train/epoch_loss': 0.04955068614813831} {'test/epoch_loss': 0.3083995895563728, 'train/epoch_acc': 0.9877149877149876, 'train/batch_loss': 0.025906365364789963, '_step': 2059, 'epoch': 9, 'test/f1-score': 0.8666666666666666, 'test/epoch_acc': 0.8666666666666667, 'test/precision': 0.8478260869565217, 'train/epoch_loss': 0.04955068614813831, '_wandb': {'runtime': 377}, '_runtime': 380.8983037471771, '_timestamp': 1678794222.848524, 'test/recall': 0.8863636363636364} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.01} vibrant-sweep-25
78 76 {'test/f1-score': 0.8867924528301887, 'test/precision': 0.8545454545454545, 'test/epoch_loss': 0.7976957665549385, '_step': 149, 'epoch': 9, '_wandb': {'runtime': 340}, '_timestamp': 1678793829.5489533, 'test/recall': 0.9215686274509804, 'train/epoch_acc': 1, '_runtime': 343.4739582538605, 'test/epoch_acc': 0.8666666666666667, 'train/batch_loss': 0.0010389955714344978, 'train/epoch_loss': 0.002287556243378495} {'_step': 149, '_wandb': {'runtime': 340}, '_runtime': 343.4739582538605, 'test/f1-score': 0.8867924528301887, 'test/precision': 0.8545454545454545, 'test/epoch_loss': 0.7976957665549385, 'train/batch_loss': 0.0010389955714344978, 'train/epoch_loss': 0.002287556243378495, 'epoch': 9, '_timestamp': 1678793829.5489533, 'test/recall': 0.9215686274509804, 'test/epoch_acc': 0.8666666666666667, 'train/epoch_acc': 1} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.1} valiant-sweep-24
79 77 {'test/f1-score': 0.8571428571428571, 'test/precision': 0.8666666666666667, 'test/epoch_loss': 0.4112878143787384, 'train/batch_loss': 0.3762533664703369, 'train/epoch_loss': 0.3862068348493272, 'epoch': 9, '_runtime': 344.0598545074463, 'test/recall': 0.8478260869565217, 'test/epoch_acc': 0.8555555555555556, 'train/epoch_acc': 0.8857493857493858, '_step': 149, '_wandb': {'runtime': 340}, '_timestamp': 1678793464.5180786} {'_timestamp': 1678793464.5180786, 'test/f1-score': 0.8571428571428571, 'test/epoch_acc': 0.8555555555555556, 'test/epoch_loss': 0.4112878143787384, 'train/epoch_acc': 0.8857493857493858, 'epoch': 9, '_wandb': {'runtime': 340}, '_runtime': 344.0598545074463, 'train/batch_loss': 0.3762533664703369, 'train/epoch_loss': 0.3862068348493272, '_step': 149, 'test/recall': 0.8478260869565217, 'test/precision': 0.8666666666666667} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.001} polished-sweep-23
80 78 {'_timestamp': 1678793108.7606344, 'test/recall': 0.8837209302325582, 'test/epoch_loss': 0.6097042110231188, 'train/epoch_acc': 0.6756756756756757, 'train/batch_loss': 0.7007869482040405, 'epoch': 9, '_wandb': {'runtime': 336}, '_runtime': 339.41979336738586, 'test/f1-score': 0.7102803738317758, 'test/epoch_acc': 0.6555555555555556, 'test/precision': 0.59375, 'train/epoch_loss': 0.6115244123215171, '_step': 529} {'_wandb': {'runtime': 336}, '_runtime': 339.41979336738586, 'test/epoch_acc': 0.6555555555555556, 'test/precision': 0.59375, 'test/epoch_loss': 0.6097042110231188, 'epoch': 9, '_timestamp': 1678793108.7606344, 'test/recall': 0.8837209302325582, 'test/f1-score': 0.7102803738317758, 'train/epoch_acc': 0.6756756756756757, 'train/batch_loss': 0.7007869482040405, 'train/epoch_loss': 0.6115244123215171, '_step': 529} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.01} clear-sweep-22
81 79 {'test/precision': 0.9393939393939394, 'train/epoch_loss': 0.07462231436439994, 'epoch': 9, '_runtime': 381.0477261543274, 'test/epoch_acc': 0.9, 'test/recall': 0.8157894736842105, 'test/f1-score': 0.8732394366197183, 'test/epoch_loss': 0.23743902287549443, 'train/epoch_acc': 0.9815724815724816, 'train/batch_loss': 0.5061427354812622, '_step': 2059, '_wandb': {'runtime': 377}, '_timestamp': 1678792758.596286} {'test/precision': 0.9393939393939394, 'train/epoch_acc': 0.9815724815724816, 'train/batch_loss': 0.5061427354812622, '_runtime': 381.0477261543274, 'test/recall': 0.8157894736842105, 'test/f1-score': 0.8732394366197183, '_timestamp': 1678792758.596286, 'test/epoch_acc': 0.9, 'test/epoch_loss': 0.23743902287549443, 'train/epoch_loss': 0.07462231436439994, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 377}} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01} sage-sweep-21
82 80 {'_wandb': {'runtime': 331}, '_timestamp': 1678792364.5292609, 'test/f1-score': 0.8505747126436782, 'test/precision': 0.902439024390244, 'train/epoch_acc': 0.9791154791154792, 'train/batch_loss': 0.24579545855522156, 'train/epoch_loss': 0.12095561367287976, '_step': 529, 'epoch': 9, '_runtime': 335.3731348514557, 'test/recall': 0.8043478260869565, 'test/epoch_acc': 0.8555555555555556, 'test/epoch_loss': 0.28035063776705} {'_step': 529, '_timestamp': 1678792364.5292609, 'test/f1-score': 0.8505747126436782, 'train/epoch_acc': 0.9791154791154792, 'train/epoch_loss': 0.12095561367287976, 'train/batch_loss': 0.24579545855522156, 'epoch': 9, '_wandb': {'runtime': 331}, '_runtime': 335.3731348514557, 'test/recall': 0.8043478260869565, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.902439024390244, 'test/epoch_loss': 0.28035063776705} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 16, 'learning_rate': 0.001} olive-sweep-20
83 81 {'_wandb': {'runtime': 337}, 'test/recall': 0.9111111111111112, 'test/f1-score': 0.931818181818182, 'test/epoch_acc': 0.9333333333333332, 'test/precision': 0.9534883720930232, 'test/epoch_loss': 0.17397157057291932, 'epoch': 9, '_runtime': 340.5063774585724, '_timestamp': 1678792015.2579195, 'train/epoch_acc': 0.995085995085995, 'train/batch_loss': 0.0077079650945961475, 'train/epoch_loss': 0.018187719287696302, '_step': 1039} {'test/epoch_acc': 0.9333333333333332, 'test/epoch_loss': 0.17397157057291932, 'train/epoch_acc': 0.995085995085995, '_step': 1039, 'epoch': 9, '_timestamp': 1678792015.2579195, 'test/recall': 0.9111111111111112, 'test/f1-score': 0.931818181818182, '_wandb': {'runtime': 337}, '_runtime': 340.5063774585724, 'test/precision': 0.9534883720930232, 'train/batch_loss': 0.0077079650945961475, 'train/epoch_loss': 0.018187719287696302} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.003} autumn-sweep-19
84 82 {'epoch': 9, '_wandb': {'runtime': 344}, 'test/recall': 0.8205128205128205, 'train/epoch_loss': 0.4784781006542412, 'test/epoch_loss': 0.4940012666914198, 'train/epoch_acc': 0.8218673218673218, '_step': 1039, '_runtime': 347.40152740478516, '_timestamp': 1678791661.9692383, 'test/f1-score': 0.7804878048780488, 'test/epoch_acc': 0.8, 'test/precision': 0.7441860465116279, 'train/batch_loss': 0.4317986071109772} {'train/epoch_loss': 0.4784781006542412, '_step': 1039, 'epoch': 9, '_wandb': {'runtime': 344}, '_timestamp': 1678791661.9692383, 'test/f1-score': 0.7804878048780488, 'train/epoch_acc': 0.8218673218673218, 'train/batch_loss': 0.4317986071109772, '_runtime': 347.40152740478516, 'test/recall': 0.8205128205128205, 'test/epoch_acc': 0.8, 'test/precision': 0.7441860465116279, 'test/epoch_loss': 0.4940012666914198} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 8, 'learning_rate': 0.0001} crisp-sweep-18
85 83 {'_runtime': 337.956387758255, 'test/recall': 0.9090909090909092, 'test/f1-score': 0.9090909090909092, 'test/precision': 0.9090909090909092, 'test/epoch_loss': 0.19624250796106127, '_step': 279, '_wandb': {'runtime': 335}, '_timestamp': 1678791236.6172178, 'test/epoch_acc': 0.9111111111111112, 'train/epoch_acc': 0.9828009828009828, 'train/batch_loss': 0.15555259585380554, 'train/epoch_loss': 0.08830470366618558, 'epoch': 9} {'_step': 279, 'epoch': 9, '_wandb': {'runtime': 335}, '_runtime': 337.956387758255, 'test/recall': 0.9090909090909092, 'test/f1-score': 0.9090909090909092, 'train/epoch_loss': 0.08830470366618558, '_timestamp': 1678791236.6172178, 'test/epoch_acc': 0.9111111111111112, 'test/precision': 0.9090909090909092, 'test/epoch_loss': 0.19624250796106127, 'train/epoch_acc': 0.9828009828009828, 'train/batch_loss': 0.15555259585380554} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 32, 'learning_rate': 0.003} deep-sweep-16
86 84 {'_step': 279, '_timestamp': 1678790886.952144, 'test/f1-score': 0.7818181818181819, 'test/precision': 0.7049180327868853, 'test/epoch_loss': 0.6228035251299541, 'train/epoch_acc': 0.7493857493857494, 'train/batch_loss': 0.6377201080322266, 'epoch': 9, '_wandb': {'runtime': 331}, '_runtime': 334.2993712425232, 'test/recall': 0.8775510204081632, 'test/epoch_acc': 0.7333333333333334, 'train/epoch_loss': 0.6127705679478751} {'test/recall': 0.8775510204081632, 'test/epoch_loss': 0.6228035251299541, 'train/epoch_acc': 0.7493857493857494, 'train/batch_loss': 0.6377201080322266, '_step': 279, '_runtime': 334.2993712425232, '_timestamp': 1678790886.952144, 'test/f1-score': 0.7818181818181819, 'test/epoch_acc': 0.7333333333333334, 'test/precision': 0.7049180327868853, 'train/epoch_loss': 0.6127705679478751, 'epoch': 9, '_wandb': {'runtime': 331}} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 32, 'learning_rate': 0.0003} confused-sweep-15
87 85 {'train/epoch_loss': 0.3545121966840594, '_step': 529, 'epoch': 9, '_runtime': 345.0617377758026, '_timestamp': 1678790542.286384, 'test/f1-score': 0.7809523809523811, 'train/epoch_acc': 0.8415233415233415, 'train/batch_loss': 0.1340156048536301, '_wandb': {'runtime': 342}, 'test/recall': 0.8541666666666666, 'test/epoch_acc': 0.7444444444444445, 'test/precision': 0.7192982456140351, 'test/epoch_loss': 0.6144241677390204} {'test/precision': 0.7192982456140351, 'test/epoch_loss': 0.6144241677390204, 'train/epoch_acc': 0.8415233415233415, 'train/batch_loss': 0.1340156048536301, 'train/epoch_loss': 0.3545121966840594, '_wandb': {'runtime': 342}, '_timestamp': 1678790542.286384, '_runtime': 345.0617377758026, 'test/recall': 0.8541666666666666, 'test/f1-score': 0.7809523809523811, 'test/epoch_acc': 0.7444444444444445, '_step': 529, 'epoch': 9} {'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.1} ancient-sweep-14
88 86 {'_step': 529, '_timestamp': 1678790183.7024884, 'test/f1-score': 0.7422680412371134, 'train/batch_loss': 0.6280461549758911, 'test/precision': 0.7058823529411765, 'test/epoch_loss': 0.6392196734746297, 'train/epoch_acc': 0.7457002457002457, 'epoch': 9, '_wandb': {'runtime': 344}, '_runtime': 346.86587953567505, 'test/recall': 0.782608695652174, 'test/epoch_acc': 0.7222222222222222, 'train/epoch_loss': 0.6374555861334836} {'test/epoch_loss': 0.6392196734746297, '_runtime': 346.86587953567505, '_timestamp': 1678790183.7024884, '_wandb': {'runtime': 344}, 'test/recall': 0.782608695652174, 'test/f1-score': 0.7422680412371134, 'test/epoch_acc': 0.7222222222222222, 'test/precision': 0.7058823529411765, 'train/epoch_acc': 0.7457002457002457, '_step': 529, 'epoch': 9, 'train/batch_loss': 0.6280461549758911, 'train/epoch_loss': 0.6374555861334836} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 16, 'learning_rate': 0.0003} revived-sweep-13
89 87 {'_wandb': {'runtime': 348}, '_runtime': 350.9660577774048, 'test/recall': 0.9111111111111112, 'train/epoch_acc': 0.9987714987714988, 'epoch': 9, '_timestamp': 1678789826.0085878, 'test/f1-score': 0.9010989010989012, 'test/epoch_acc': 0.9, 'test/precision': 0.8913043478260869, 'test/epoch_loss': 0.24115624560250176, 'train/batch_loss': 0.04231283441185951, 'train/epoch_loss': 0.02119528235872196, '_step': 149} {'_wandb': {'runtime': 348}, '_runtime': 350.9660577774048, '_timestamp': 1678789826.0085878, 'test/recall': 0.9111111111111112, 'train/epoch_acc': 0.9987714987714988, 'train/epoch_loss': 0.02119528235872196, '_step': 149, 'epoch': 9, 'test/f1-score': 0.9010989010989012, 'test/epoch_acc': 0.9, 'test/precision': 0.8913043478260869, 'test/epoch_loss': 0.24115624560250176, 'train/batch_loss': 0.04231283441185951} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 64, 'learning_rate': 0.0003} swift-sweep-12
90 88 {'test/recall': 0.8333333333333334, 'test/epoch_loss': 0.5769641452365452, 'train/batch_loss': 0.6127220392227173, 'train/epoch_loss': 0.5840219159676929, 'epoch': 9, '_wandb': {'runtime': 393}, '_timestamp': 1678789464.8040044, 'test/f1-score': 0.7894736842105262, 'test/epoch_acc': 0.8222222222222223, 'test/precision': 0.75, 'train/epoch_acc': 0.757985257985258, '_step': 2059, '_runtime': 397.1281135082245} {'_step': 2059, '_wandb': {'runtime': 393}, '_runtime': 397.1281135082245, '_timestamp': 1678789464.8040044, 'epoch': 9, 'test/recall': 0.8333333333333334, 'test/f1-score': 0.7894736842105262, 'test/epoch_acc': 0.8222222222222223, 'test/precision': 0.75, 'test/epoch_loss': 0.5769641452365452, 'train/epoch_acc': 0.757985257985258, 'train/batch_loss': 0.6127220392227173, 'train/epoch_loss': 0.5840219159676929} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.0001} rosy-sweep-11
91 89 {'epoch': 9, 'test/recall': 0.8076923076923077, 'test/f1-score': 0.8842105263157894, 'test/epoch_loss': 0.2696530275874668, 'train/epoch_acc': 0.9938574938574938, 'train/batch_loss': 0.11590295284986496, '_step': 149, '_wandb': {'runtime': 352}, '_runtime': 355.46944642066956, '_timestamp': 1678789057.5684297, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.9767441860465116, 'train/epoch_loss': 0.06967324825777176} {'_wandb': {'runtime': 352}, '_timestamp': 1678789057.5684297, 'test/f1-score': 0.8842105263157894, 'test/epoch_acc': 0.8777777777777778, 'train/epoch_acc': 0.9938574938574938, 'train/batch_loss': 0.11590295284986496, 'train/epoch_loss': 0.06967324825777176, '_step': 149, '_runtime': 355.46944642066956, 'test/recall': 0.8076923076923077, 'test/precision': 0.9767441860465116, 'test/epoch_loss': 0.2696530275874668, 'epoch': 9} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 64, 'learning_rate': 0.003} deft-sweep-10
92 90 {'_step': 279, '_wandb': {'runtime': 340}, '_runtime': 342.3234579563141, '_timestamp': 1678788683.006292, 'test/recall': 0.9069767441860463, 'test/f1-score': 0.7959183673469388, 'test/epoch_acc': 0.7777777777777778, 'test/precision': 0.7090909090909091, 'test/epoch_loss': 0.6248881856600443, 'train/epoch_acc': 0.7014742014742015, 'train/batch_loss': 0.5820533037185669, 'train/epoch_loss': 0.6400203514450599, 'epoch': 9} {'epoch': 9, '_wandb': {'runtime': 340}, '_runtime': 342.3234579563141, 'test/recall': 0.9069767441860463, 'test/f1-score': 0.7959183673469388, 'test/precision': 0.7090909090909091, 'test/epoch_loss': 0.6248881856600443, '_step': 279, 'train/batch_loss': 0.5820533037185669, 'train/epoch_acc': 0.7014742014742015, 'test/epoch_acc': 0.7777777777777778, 'train/epoch_loss': 0.6400203514450599, '_timestamp': 1678788683.006292} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.0001} atomic-sweep-9
93 91 {'_step': 1039, '_wandb': {'runtime': 351}, 'test/epoch_acc': 0.6555555555555556, 'test/precision': 0.6140350877192983, 'test/epoch_loss': 0.6175267219543457, 'train/epoch_acc': 0.7432432432432432, 'epoch': 9, '_runtime': 353.4816448688507, '_timestamp': 1678788328.1196988, 'test/recall': 0.7954545454545454, 'test/f1-score': 0.693069306930693, 'train/batch_loss': 0.3377891480922699, 'train/epoch_loss': 0.5329857344855841} {'_step': 1039, '_wandb': {'runtime': 351}, '_timestamp': 1678788328.1196988, 'test/f1-score': 0.693069306930693, 'test/epoch_loss': 0.6175267219543457, 'train/batch_loss': 0.3377891480922699, 'epoch': 9, '_runtime': 353.4816448688507, 'test/recall': 0.7954545454545454, 'test/epoch_acc': 0.6555555555555556, 'test/precision': 0.6140350877192983, 'train/epoch_acc': 0.7432432432432432, 'train/epoch_loss': 0.5329857344855841} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 8, 'learning_rate': 0.1} cosmic-sweep-8
94 92 {'epoch': 9, '_wandb': {'runtime': 390}, '_runtime': 392.4064960479736, '_timestamp': 1678787961.3400052, 'test/f1-score': 0.6999999999999998, 'test/precision': 0.5932203389830508, 'train/epoch_loss': 0.5631518808058498, '_step': 2059, 'test/recall': 0.8536585365853658, 'test/epoch_acc': 0.6666666666666667, 'test/epoch_loss': 0.6419186863634322, 'train/epoch_acc': 0.7186732186732187, 'train/batch_loss': 0.17200787365436554} {'test/epoch_acc': 0.6666666666666667, 'test/epoch_loss': 0.6419186863634322, 'train/epoch_acc': 0.7186732186732187, 'train/batch_loss': 0.17200787365436554, 'epoch': 9, '_runtime': 392.4064960479736, '_timestamp': 1678787961.3400052, 'test/recall': 0.8536585365853658, 'train/epoch_loss': 0.5631518808058498, '_step': 2059, '_wandb': {'runtime': 390}, 'test/f1-score': 0.6999999999999998, 'test/precision': 0.5932203389830508} {'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.01} lunar-sweep-7
95 93 {'train/epoch_acc': 0.9975429975429976, 'train/epoch_loss': 0.03237721893286529, 'epoch': 9, '_wandb': {'runtime': 343}, '_runtime': 345.9260220527649, 'test/f1-score': 0.8988764044943819, 'test/epoch_acc': 0.9, 'train/batch_loss': 0.04353119805455208, '_step': 529, '_timestamp': 1678787557.992564, 'test/recall': 0.8888888888888888, 'test/precision': 0.9090909090909092, 'test/epoch_loss': 0.24278527200222016} {'_runtime': 345.9260220527649, 'test/recall': 0.8888888888888888, 'test/epoch_acc': 0.9, 'test/epoch_loss': 0.24278527200222016, 'train/epoch_acc': 0.9975429975429976, 'train/epoch_loss': 0.03237721893286529, 'epoch': 9, '_wandb': {'runtime': 343}, 'test/f1-score': 0.8988764044943819, 'test/precision': 0.9090909090909092, 'train/batch_loss': 0.04353119805455208, '_step': 529, '_timestamp': 1678787557.992564} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.01} zany-sweep-6
96 94 {'_step': 529, '_runtime': 346.5414688587189, 'test/f1-score': 0.9130434782608696, 'train/epoch_acc': 0.9336609336609336, 'test/epoch_loss': 0.32114719019995797, 'train/batch_loss': 0.21811823546886444, 'epoch': 9, '_wandb': {'runtime': 344}, '_timestamp': 1678787192.9954038, 'test/recall': 0.8571428571428571, 'test/epoch_acc': 0.9111111111111112, 'test/precision': 0.9767441860465116, 'train/epoch_loss': 0.2347587838000103} {'test/epoch_acc': 0.9111111111111112, 'test/epoch_loss': 0.32114719019995797, 'train/batch_loss': 0.21811823546886444, '_step': 529, 'epoch': 9, '_wandb': {'runtime': 344}, '_runtime': 346.5414688587189, '_timestamp': 1678787192.9954038, 'test/recall': 0.8571428571428571, 'test/f1-score': 0.9130434782608696, 'test/precision': 0.9767441860465116, 'train/epoch_acc': 0.9336609336609336, 'train/epoch_loss': 0.2347587838000103} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.001} absurd-sweep-5
97 95 {'_timestamp': 1678786835.7254088, 'test/f1-score': 0.8799999999999999, 'test/epoch_loss': 0.22436124781767527, 'train/epoch_loss': 0.02646600444977348, 'epoch': 9, '_wandb': {'runtime': 344}, '_runtime': 345.9469966888428, 'test/precision': 0.9166666666666666, 'train/epoch_acc': 1, 'train/batch_loss': 0.06225413456559181, '_step': 279, 'test/recall': 0.8461538461538461, 'test/epoch_acc': 0.9} {'train/epoch_loss': 0.02646600444977348, '_timestamp': 1678786835.7254088, 'test/precision': 0.9166666666666666, 'test/epoch_loss': 0.22436124781767527, 'train/epoch_acc': 1, 'test/recall': 0.8461538461538461, 'test/f1-score': 0.8799999999999999, 'test/epoch_acc': 0.9, 'train/batch_loss': 0.06225413456559181, '_step': 279, 'epoch': 9, '_wandb': {'runtime': 344}, '_runtime': 345.9469966888428} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 32, 'learning_rate': 0.003} radiant-sweep-4
98 96 {'_wandb': {'runtime': 353}, '_runtime': 355.012455701828, 'test/recall': 0.875, 'test/f1-score': 0.8045977011494252, 'test/epoch_acc': 0.8111111111111111, 'test/precision': 0.7446808510638298, '_step': 1039, 'epoch': 9, 'train/epoch_loss': 0.45506354690476775, 'train/epoch_acc': 0.8341523341523341, 'train/batch_loss': 0.5456343293190002, '_timestamp': 1678786479.0865147, 'test/epoch_loss': 0.4459853092829386} {'test/f1-score': 0.8045977011494252, 'test/precision': 0.7446808510638298, 'train/epoch_acc': 0.8341523341523341, 'epoch': 9, '_wandb': {'runtime': 353}, 'test/recall': 0.875, 'test/epoch_acc': 0.8111111111111111, 'test/epoch_loss': 0.4459853092829386, 'train/batch_loss': 0.5456343293190002, 'train/epoch_loss': 0.45506354690476775, '_step': 1039, '_runtime': 355.012455701828, '_timestamp': 1678786479.0865147} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0003} sandy-sweep-3
99 97 {'_wandb': {'runtime': 342}, '_timestamp': 1678786112.108075, 'test/recall': 0.7894736842105263, 'test/precision': 0.9090909090909092, 'test/epoch_loss': 0.31915653232071134, 'train/batch_loss': 0.026765840128064156, 'train/epoch_loss': 0.045762457081668206, '_step': 529, 'epoch': 9, '_runtime': 344.01046657562256, 'test/f1-score': 0.8450704225352113, 'test/epoch_acc': 0.8777777777777778, 'train/epoch_acc': 0.9926289926289926} {'test/epoch_loss': 0.31915653232071134, 'train/epoch_acc': 0.9926289926289926, 'train/epoch_loss': 0.045762457081668206, '_wandb': {'runtime': 342}, '_runtime': 344.01046657562256, 'test/recall': 0.7894736842105263, 'test/f1-score': 0.8450704225352113, 'test/precision': 0.9090909090909092, '_step': 529, 'epoch': 9, '_timestamp': 1678786112.108075, 'test/epoch_acc': 0.8777777777777778, 'train/batch_loss': 0.026765840128064156} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.01} pretty-sweep-2
100 98 {'train/batch_loss': 0.7150550484657288, 'train/epoch_loss': 0.7011552195291262, '_step': 149, '_wandb': {'runtime': 357}, '_runtime': 359.66486382484436, '_timestamp': 1678785758.376562, 'test/f1-score': 0.379746835443038, 'test/precision': 0.42857142857142855, 'epoch': 9, 'test/recall': 0.3409090909090909, 'test/epoch_acc': 0.45555555555555555, 'test/epoch_loss': 0.7006691349877252, 'train/epoch_acc': 0.4815724815724816} {'_runtime': 359.66486382484436, 'test/f1-score': 0.379746835443038, 'test/precision': 0.42857142857142855, 'test/epoch_loss': 0.7006691349877252, 'train/batch_loss': 0.7150550484657288, '_step': 149, 'epoch': 9, '_wandb': {'runtime': 357}, '_timestamp': 1678785758.376562, 'test/recall': 0.3409090909090909, 'test/epoch_acc': 0.45555555555555555, 'train/epoch_acc': 0.4815724815724816, 'train/epoch_loss': 0.7011552195291262} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.0003} rose-sweep-1
101 99 {'train/epoch_loss': 0.023103852647056927, '_step': 74, 'test/recall': 0.9090909090909092, 'test/f1-score': 0.8791208791208791, 'train/batch_loss': 0.0016211483161896467, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.851063829787234, 'test/epoch_loss': 0.5091631063156657, 'train/epoch_acc': 0.995085995085995, 'epoch': 4, '_wandb': {'runtime': 181}, '_runtime': 180.05384421348572, '_timestamp': 1678785370.5563953} {'_timestamp': 1678785370.5563953, 'test/recall': 0.9090909090909092, 'train/epoch_acc': 0.995085995085995, 'train/epoch_loss': 0.023103852647056927, '_step': 74, '_runtime': 180.05384421348572, 'test/f1-score': 0.8791208791208791, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.851063829787234, 'test/epoch_loss': 0.5091631063156657, 'train/batch_loss': 0.0016211483161896467, 'epoch': 4, '_wandb': {'runtime': 181}} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 64, 'learning_rate': 0.1} cosmic-sweep-2
102 100 {'test/f1-score': 0.9166666666666666, 'test/precision': 0.9166666666666666, 'train/epoch_acc': 0.9828009828009828, 'train/batch_loss': 0.0724378228187561, 'train/epoch_loss': 0.11044558714297244, '_step': 279, '_runtime': 347.11417746543884, 'test/recall': 0.9166666666666666, 'test/epoch_acc': 0.9111111111111112, 'test/epoch_loss': 0.2461573594146305, 'epoch': 9, '_wandb': {'runtime': 344}, '_timestamp': 1678743707.9633043} {'test/f1-score': 0.9166666666666666, 'test/epoch_acc': 0.9111111111111112, 'test/precision': 0.9166666666666666, 'test/epoch_loss': 0.2461573594146305, 'train/epoch_acc': 0.9828009828009828, 'train/batch_loss': 0.0724378228187561, '_step': 279, 'test/recall': 0.9166666666666666, '_runtime': 347.11417746543884, '_timestamp': 1678743707.9633043, 'train/epoch_loss': 0.11044558714297244, 'epoch': 9, '_wandb': {'runtime': 344}} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.003} ethereal-sweep-14
103 101 {'_step': 149, 'epoch': 9, '_wandb': {'runtime': 346}, 'test/recall': 0.9130434782608696, 'test/precision': 0.9545454545454546, 'train/batch_loss': 0.05796322599053383, 'train/epoch_loss': 0.043383844352398226, '_runtime': 349.69085454940796, '_timestamp': 1678743349.8008895, 'test/f1-score': 0.9333333333333332, 'test/epoch_acc': 0.9333333333333332, 'test/epoch_loss': 0.16449517243438297, 'train/epoch_acc': 1} {'_timestamp': 1678743349.8008895, 'test/recall': 0.9130434782608696, 'test/f1-score': 0.9333333333333332, 'test/epoch_acc': 0.9333333333333332, 'test/precision': 0.9545454545454546, 'test/epoch_loss': 0.16449517243438297, '_step': 149, '_runtime': 349.69085454940796, 'train/epoch_loss': 0.043383844352398226, 'train/epoch_acc': 1, 'train/batch_loss': 0.05796322599053383, 'epoch': 9, '_wandb': {'runtime': 346}} {'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 64, 'learning_rate': 0.003} northern-sweep-13
104 102 {'_runtime': 560.5539684295654, '_timestamp': 1678743376.8770983, 'test/recall': 0.85, 'test/f1-score': 0.7816091954022989, 'train/epoch_acc': 0.8255528255528255, 'train/epoch_loss': 0.40511614706651, '_wandb': {'runtime': 559}, 'epoch': 9, 'test/epoch_acc': 0.788888888888889, 'test/precision': 0.723404255319149, 'test/epoch_loss': 0.5102662573258082, 'train/batch_loss': 0.42048144340515137, '_step': 2059} {'epoch': 9, 'test/precision': 0.723404255319149, 'train/epoch_loss': 0.40511614706651, '_step': 2059, '_wandb': {'runtime': 559}, '_runtime': 560.5539684295654, '_timestamp': 1678743376.8770983, 'test/recall': 0.85, 'test/f1-score': 0.7816091954022989, 'test/epoch_acc': 0.788888888888889, 'test/epoch_loss': 0.5102662573258082, 'train/epoch_acc': 0.8255528255528255, 'train/batch_loss': 0.42048144340515137} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.001} faithful-sweep-12
105 103 {'_timestamp': 1678742986.9751594, 'test/recall': 0.7777777777777778, 'test/epoch_loss': 0.3378064884079827, 'epoch': 9, '_runtime': 358.3485324382782, 'test/f1-score': 0.8536585365853658, 'test/epoch_acc': 0.8666666666666667, 'test/precision': 0.945945945945946, 'train/epoch_acc': 0.8955773955773956, 'train/batch_loss': 0.5923706889152527, 'train/epoch_loss': 0.27216847456936755, '_step': 1039, '_wandb': {'runtime': 355}} {'epoch': 9, '_wandb': {'runtime': 355}, '_timestamp': 1678742986.9751594, 'test/epoch_loss': 0.3378064884079827, 'train/epoch_acc': 0.8955773955773956, '_step': 1039, '_runtime': 358.3485324382782, 'test/recall': 0.7777777777777778, 'test/f1-score': 0.8536585365853658, 'test/epoch_acc': 0.8666666666666667, 'test/precision': 0.945945945945946, 'train/batch_loss': 0.5923706889152527, 'train/epoch_loss': 0.27216847456936755} {'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.0003} zany-sweep-12
106 104 {'test/epoch_acc': 0.7444444444444445, 'test/precision': 0.6226415094339622, '_step': 1039, 'epoch': 9, '_wandb': {'runtime': 358}, '_timestamp': 1678742619.1453717, 'test/recall': 0.9166666666666666, 'test/f1-score': 0.7415730337078651, 'train/epoch_loss': 0.613342459283824, '_runtime': 362.78373169898987, 'test/epoch_loss': 0.615033131175571, 'train/epoch_acc': 0.7481572481572482, 'train/batch_loss': 0.6421169638633728} {'train/epoch_acc': 0.7481572481572482, 'train/epoch_loss': 0.613342459283824, 'epoch': 9, '_wandb': {'runtime': 358}, '_runtime': 362.78373169898987, 'test/precision': 0.6226415094339622, 'test/epoch_loss': 0.615033131175571, 'train/batch_loss': 0.6421169638633728, '_step': 1039, '_timestamp': 1678742619.1453717, 'test/recall': 0.9166666666666666, 'test/f1-score': 0.7415730337078651, 'test/epoch_acc': 0.7444444444444445} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0001} ruby-sweep-11
107 105 {'_wandb': {'runtime': 531}, 'test/epoch_acc': 0.8666666666666667, 'test/precision': 0.9545454545454546, 'train/batch_loss': 0.07699991017580032, '_step': 2059, '_runtime': 531.6082515716553, '_timestamp': 1678742643.2100165, 'test/recall': 0.8076923076923077, 'test/f1-score': 0.875, 'test/epoch_loss': 0.3795760815549228, 'train/epoch_acc': 0.9656019656019657, 'train/epoch_loss': 0.09796744051757808, 'epoch': 9} {'_timestamp': 1678742643.2100165, 'test/f1-score': 0.875, '_step': 2059, '_wandb': {'runtime': 531}, 'test/recall': 0.8076923076923077, 'test/epoch_acc': 0.8666666666666667, 'test/precision': 0.9545454545454546, 'test/epoch_loss': 0.3795760815549228, 'train/epoch_acc': 0.9656019656019657, 'train/batch_loss': 0.07699991017580032, 'epoch': 9, '_runtime': 531.6082515716553, 'train/epoch_loss': 0.09796744051757808} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 4, 'learning_rate': 0.001} fallen-sweep-10
108 106 {'test/f1-score': 0.875, 'test/precision': 0.9545454545454546, 'test/epoch_loss': 0.2956610471010208, 'train/batch_loss': 0.1150113120675087, '_step': 1039, 'epoch': 9, '_timestamp': 1678742242.6362762, 'test/recall': 0.8076923076923077, 'train/epoch_loss': 0.24495647845821825, '_wandb': {'runtime': 359}, '_runtime': 361.6978232860565, 'test/epoch_acc': 0.8666666666666667, 'train/epoch_acc': 0.9103194103194104} {'_step': 1039, 'epoch': 9, '_wandb': {'runtime': 359}, '_runtime': 361.6978232860565, 'train/batch_loss': 0.1150113120675087, 'train/epoch_loss': 0.24495647845821825, '_timestamp': 1678742242.6362762, 'test/recall': 0.8076923076923077, 'test/f1-score': 0.875, 'test/epoch_acc': 0.8666666666666667, 'test/precision': 0.9545454545454546, 'test/epoch_loss': 0.2956610471010208, 'train/epoch_acc': 0.9103194103194104} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 7, 'batch_size': 8, 'learning_rate': 0.003} rare-sweep-10
109 107 {'_runtime': 471.6707801818848, 'test/precision': 0.9714285714285714, '_wandb': {'runtime': 471}, '_timestamp': 1678742103.7627492, 'test/recall': 0.7906976744186046, 'test/f1-score': 0.8717948717948717, 'test/epoch_acc': 0.888888888888889, 'test/epoch_loss': 0.26282389760017394, '_step': 1039, 'epoch': 9, 'train/epoch_loss': 0.310643073711407, 'train/epoch_acc': 0.8869778869778869, 'train/batch_loss': 0.14859537780284882} {'train/epoch_acc': 0.8869778869778869, 'train/batch_loss': 0.14859537780284882, 'train/epoch_loss': 0.310643073711407, 'epoch': 9, '_timestamp': 1678742103.7627492, 'test/recall': 0.7906976744186046, 'test/epoch_acc': 0.888888888888889, 'test/epoch_loss': 0.26282389760017394, '_step': 1039, '_wandb': {'runtime': 471}, '_runtime': 471.6707801818848, 'test/f1-score': 0.8717948717948717, 'test/precision': 0.9714285714285714} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.003} major-sweep-9
110 108 {'test/epoch_acc': 0.6333333333333333, 'test/precision': 0.6, 'train/epoch_acc': 0.5921375921375921, 'train/batch_loss': 0.6228023767471313, '_step': 279, '_runtime': 344.49258494377136, 'test/f1-score': 0.6451612903225806, 'test/recall': 0.6976744186046512, 'test/epoch_loss': 0.6676742302046882, 'train/epoch_loss': 0.6766868150204932, 'epoch': 9, '_wandb': {'runtime': 341}, '_timestamp': 1678741869.828495} {'train/epoch_loss': 0.6766868150204932, '_step': 279, 'epoch': 9, '_wandb': {'runtime': 341}, '_timestamp': 1678741869.828495, 'test/epoch_loss': 0.6676742302046882, 'train/epoch_acc': 0.5921375921375921, 'train/batch_loss': 0.6228023767471313, '_runtime': 344.49258494377136, 'test/recall': 0.6976744186046512, 'test/f1-score': 0.6451612903225806, 'test/epoch_acc': 0.6333333333333333, 'test/precision': 0.6} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.0001} spring-sweep-9
111 109 {'test/epoch_loss': 0.16872049139605627, 'train/epoch_acc': 0.9987714987714988, '_step': 1039, 'epoch': 9, '_wandb': {'runtime': 451}, '_runtime': 452.4322986602783, 'test/f1-score': 0.9213483146067416, 'test/precision': 0.9111111111111112, 'train/epoch_loss': 0.02303326028314504, '_timestamp': 1678741623.0662856, 'test/recall': 0.9318181818181818, 'test/epoch_acc': 0.9222222222222224, 'train/batch_loss': 0.0022799931466579437} {'train/epoch_acc': 0.9987714987714988, 'train/batch_loss': 0.0022799931466579437, '_step': 1039, 'test/recall': 0.9318181818181818, 'test/f1-score': 0.9213483146067416, '_timestamp': 1678741623.0662856, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 0.9111111111111112, 'test/epoch_loss': 0.16872049139605627, 'train/epoch_loss': 0.02303326028314504, 'epoch': 9, '_wandb': {'runtime': 451}, '_runtime': 452.4322986602783} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.003} elated-sweep-8
112 110 {'_step': 149, '_runtime': 345.3405177593231, 'test/f1-score': 0.9534883720930232, 'test/precision': 0.9761904761904762, 'test/epoch_loss': 0.2148759490913815, 'train/epoch_acc': 0.9606879606879608, 'epoch': 9, '_wandb': {'runtime': 342}, '_timestamp': 1678741511.9070578, 'test/recall': 0.9318181818181818, 'test/epoch_acc': 0.9555555555555556, 'train/batch_loss': 0.11643347889184952, 'train/epoch_loss': 0.1359616077759049} {'test/recall': 0.9318181818181818, 'test/epoch_acc': 0.9555555555555556, 'test/epoch_loss': 0.2148759490913815, 'train/epoch_loss': 0.1359616077759049, 'epoch': 9, '_wandb': {'runtime': 342}, '_runtime': 345.3405177593231, 'test/precision': 0.9761904761904762, 'train/epoch_acc': 0.9606879606879608, 'train/batch_loss': 0.11643347889184952, '_step': 149, '_timestamp': 1678741511.9070578, 'test/f1-score': 0.9534883720930232} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.003} hardy-sweep-8
113 111 {'epoch': 9, '_wandb': {'runtime': 342}, '_runtime': 345.1732180118561, '_timestamp': 1678741156.130327, 'test/recall': 0.8048780487804879, 'test/epoch_acc': 0.888888888888889, 'train/epoch_acc': 1, '_step': 279, 'train/epoch_loss': 0.008645273717600824, 'test/precision': 0.9428571428571428, 'test/epoch_loss': 0.2181672462158733, 'train/batch_loss': 0.042314428836107254, 'test/f1-score': 0.868421052631579} {'test/recall': 0.8048780487804879, 'test/epoch_loss': 0.2181672462158733, 'train/epoch_acc': 1, 'train/epoch_loss': 0.008645273717600824, '_wandb': {'runtime': 342}, '_runtime': 345.1732180118561, '_timestamp': 1678741156.130327, 'test/f1-score': 0.868421052631579, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.9428571428571428, 'train/batch_loss': 0.042314428836107254, '_step': 279, 'epoch': 9} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 32, 'learning_rate': 0.1} sweepy-sweep-7
114 112 {'_step': 1039, 'test/f1-score': 0.7222222222222222, 'test/epoch_acc': 0.7777777777777778, 'test/precision': 0.8387096774193549, 'test/epoch_loss': 0.4768455002042982, 'train/epoch_acc': 0.8292383292383292, 'train/epoch_loss': 0.45283343838825274, 'epoch': 9, '_wandb': {'runtime': 453}, '_runtime': 454.0593776702881, '_timestamp': 1678741159.4683807, 'test/recall': 0.6341463414634146, 'train/batch_loss': 0.3791900873184204} {'test/recall': 0.6341463414634146, 'test/epoch_acc': 0.7777777777777778, 'train/epoch_acc': 0.8292383292383292, 'train/batch_loss': 0.3791900873184204, 'epoch': 9, '_wandb': {'runtime': 453}, '_runtime': 454.0593776702881, '_timestamp': 1678741159.4683807, 'test/f1-score': 0.7222222222222222, 'test/precision': 0.8387096774193549, 'test/epoch_loss': 0.4768455002042982, 'train/epoch_loss': 0.45283343838825274, '_step': 1039} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 8, 'learning_rate': 0.0001} glorious-sweep-7
115 113 {'test/epoch_loss': 0.1931780371401045, 'epoch': 9, '_wandb': {'runtime': 346}, 'test/f1-score': 0.9333333333333332, 'test/precision': 0.9333333333333332, 'test/epoch_acc': 0.9333333333333332, 'train/epoch_acc': 1, 'train/batch_loss': 0.001889266073703766, 'train/epoch_loss': 0.0030514685945077376, '_step': 149, '_runtime': 348.53755164146423, '_timestamp': 1678740798.1400597, 'test/recall': 0.9333333333333332} {'_runtime': 348.53755164146423, '_timestamp': 1678740798.1400597, 'test/recall': 0.9333333333333332, 'test/f1-score': 0.9333333333333332, 'test/epoch_acc': 0.9333333333333332, 'test/precision': 0.9333333333333332, 'test/epoch_loss': 0.1931780371401045, '_wandb': {'runtime': 346}, 'train/epoch_loss': 0.0030514685945077376, 'train/epoch_acc': 1, 'epoch': 9, 'train/batch_loss': 0.001889266073703766, '_step': 149} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 64, 'learning_rate': 0.01} rural-sweep-6
116 114 {'epoch': 9, 'test/recall': 0.8666666666666667, 'test/f1-score': 0.896551724137931, 'test/epoch_acc': 0.9, 'train/batch_loss': 0.1385842263698578, '_step': 2059, '_runtime': 560.7404127120972, '_timestamp': 1678740696.0305526, 'test/precision': 0.9285714285714286, 'test/epoch_loss': 0.22745563416845269, 'train/epoch_acc': 0.984029484029484, 'train/epoch_loss': 0.07075482415817952, '_wandb': {'runtime': 560}} {'train/epoch_acc': 0.984029484029484, 'train/batch_loss': 0.1385842263698578, 'train/epoch_loss': 0.07075482415817952, '_step': 2059, '_timestamp': 1678740696.0305526, 'test/recall': 0.8666666666666667, 'test/f1-score': 0.896551724137931, 'test/epoch_loss': 0.22745563416845269, 'epoch': 9, '_wandb': {'runtime': 560}, '_runtime': 560.7404127120972, 'test/epoch_acc': 0.9, 'test/precision': 0.9285714285714286} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01} smart-sweep-6
117 115 {'_runtime': 345.5716743469238, '_timestamp': 1678740438.4959724, 'test/recall': 0.7755102040816326, 'test/f1-score': 0.8172043010752688, 'train/epoch_acc': 0.7616707616707616, 'train/epoch_loss': 0.5191410552225183, 'epoch': 9, '_wandb': {'runtime': 342}, 'test/precision': 0.8636363636363636, 'test/epoch_loss': 0.507676590151257, 'train/batch_loss': 0.44296249747276306, '_step': 529, 'test/epoch_acc': 0.8111111111111111} {'test/epoch_acc': 0.8111111111111111, 'test/epoch_loss': 0.507676590151257, '_runtime': 345.5716743469238, 'test/f1-score': 0.8172043010752688, '_wandb': {'runtime': 342}, '_timestamp': 1678740438.4959724, 'test/recall': 0.7755102040816326, 'test/precision': 0.8636363636363636, 'train/epoch_acc': 0.7616707616707616, 'train/batch_loss': 0.44296249747276306, '_step': 529, 'epoch': 9, 'train/epoch_loss': 0.5191410552225183} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 16, 'learning_rate': 0.1} giddy-sweep-5
118 116 {'_step': 529, 'epoch': 9, '_runtime': 345.28623247146606, 'test/f1-score': 0.6842105263157895, 'train/epoch_acc': 0.8538083538083537, 'train/batch_loss': 0.4066888689994812, 'train/epoch_loss': 0.32492415251837314, '_wandb': {'runtime': 342}, '_timestamp': 1678740073.5443084, 'test/recall': 0.6666666666666666, 'test/epoch_acc': 0.7333333333333334, 'test/precision': 0.7027027027027027, 'test/epoch_loss': 0.6657861550649007} {'_step': 529, '_wandb': {'runtime': 342}, '_runtime': 345.28623247146606, '_timestamp': 1678740073.5443084, 'test/recall': 0.6666666666666666, 'test/precision': 0.7027027027027027, 'test/epoch_loss': 0.6657861550649007, 'epoch': 9, 'test/f1-score': 0.6842105263157895, 'test/epoch_acc': 0.7333333333333334, 'train/epoch_acc': 0.8538083538083537, 'train/batch_loss': 0.4066888689994812, 'train/epoch_loss': 0.32492415251837314} {'eps': 1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.1} lilac-sweep-4
119 117 {'_step': 1039, 'epoch': 9, '_wandb': {'runtime': 454}, '_runtime': 454.98564982414246, 'test/epoch_acc': 0.888888888888889, 'test/epoch_loss': 0.2600655794143677, 'train/batch_loss': 0.01167443674057722, '_timestamp': 1678740126.212114, 'test/recall': 0.8367346938775511, 'test/f1-score': 0.8913043478260869, 'test/precision': 0.9534883720930232, 'train/epoch_acc': 0.9803439803439804, 'train/epoch_loss': 0.08152788232426166} {'_timestamp': 1678740126.212114, 'test/f1-score': 0.8913043478260869, 'test/epoch_loss': 0.2600655794143677, 'train/epoch_acc': 0.9803439803439804, '_step': 1039, 'epoch': 9, '_runtime': 454.98564982414246, 'test/precision': 0.9534883720930232, 'train/batch_loss': 0.01167443674057722, 'train/epoch_loss': 0.08152788232426166, '_wandb': {'runtime': 454}, 'test/recall': 0.8367346938775511, 'test/epoch_acc': 0.888888888888889} {'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.001} hearty-sweep-5
120 118 {'train/epoch_acc': 0.8144963144963144, 'epoch': 9, '_wandb': {'runtime': 354}, '_timestamp': 1678739717.8250418, 'test/epoch_acc': 0.788888888888889, 'test/epoch_loss': 0.4899995631641812, 'train/batch_loss': 0.6180618405342102, 'train/epoch_loss': 0.5079173609724209, '_step': 1039, '_runtime': 356.9382667541504, 'test/recall': 0.875, 'test/f1-score': 0.7865168539325842, 'test/precision': 0.7142857142857143} {'epoch': 9, '_wandb': {'runtime': 354}, '_timestamp': 1678739717.8250418, 'test/f1-score': 0.7865168539325842, 'test/epoch_acc': 0.788888888888889, 'test/epoch_loss': 0.4899995631641812, 'train/epoch_acc': 0.8144963144963144, '_step': 1039, 'train/epoch_loss': 0.5079173609724209, 'test/recall': 0.875, 'test/precision': 0.7142857142857143, 'train/batch_loss': 0.6180618405342102, '_runtime': 356.9382667541504} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.0001} silvery-sweep-3
121 119 {'_wandb': {'runtime': 453}, 'test/precision': 0.9142857142857144, 'train/epoch_acc': 0.8968058968058967, 'train/batch_loss': 0.2711101472377777, 'test/epoch_loss': 0.3028925802972582, '_step': 1039, 'epoch': 9, '_runtime': 454.2519624233246, '_timestamp': 1678739662.5458224, 'test/recall': 0.8205128205128205, 'test/f1-score': 0.8648648648648648, 'test/epoch_acc': 0.888888888888889, 'train/epoch_loss': 0.28549219298128414} {'_runtime': 454.2519624233246, 'test/recall': 0.8205128205128205, 'epoch': 9, '_wandb': {'runtime': 453}, 'test/f1-score': 0.8648648648648648, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.9142857142857144, 'test/epoch_loss': 0.3028925802972582, 'train/epoch_acc': 0.8968058968058967, 'train/batch_loss': 0.2711101472377777, '_step': 1039, '_timestamp': 1678739662.5458224, 'train/epoch_loss': 0.28549219298128414} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.99, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 8, 'learning_rate': 0.0003} dulcet-sweep-4
122 120 {'train/epoch_loss': 0.6479796424544707, '_step': 529, '_runtime': 343.88807487487793, 'test/f1-score': 0.6451612903225806, 'test/epoch_acc': 0.6333333333333333, 'test/precision': 0.5454545454545454, 'test/epoch_loss': 0.6651701913939582, 'train/epoch_acc': 0.6928746928746928, 'train/batch_loss': 0.6685948967933655, 'epoch': 9, '_wandb': {'runtime': 341}, '_timestamp': 1678739351.1315958, 'test/recall': 0.7894736842105263} {'train/epoch_loss': 0.6479796424544707, '_step': 529, '_wandb': {'runtime': 341}, '_runtime': 343.88807487487793, 'test/recall': 0.7894736842105263, 'test/precision': 0.5454545454545454, 'test/epoch_loss': 0.6651701913939582, 'epoch': 9, '_timestamp': 1678739351.1315958, 'test/f1-score': 0.6451612903225806, 'test/epoch_acc': 0.6333333333333333, 'train/epoch_acc': 0.6928746928746928, 'train/batch_loss': 0.6685948967933655} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.999, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.001} glamorous-sweep-2
123 121 {'_runtime': 469.65283608436584, 'epoch': 9, '_wandb': {'runtime': 469}, 'test/recall': 0.875, 'test/f1-score': 0.7608695652173914, 'test/epoch_acc': 0.7555555555555555, 'test/precision': 0.6730769230769231, 'test/epoch_loss': 0.6144020875295003, 'train/epoch_acc': 0.7542997542997543, '_step': 1039, '_timestamp': 1678739200.083605, 'train/batch_loss': 0.6510805487632751, 'train/epoch_loss': 0.6267796501480684} {'test/precision': 0.6730769230769231, 'train/epoch_acc': 0.7542997542997543, 'train/batch_loss': 0.6510805487632751, 'train/epoch_loss': 0.6267796501480684, '_step': 1039, '_runtime': 469.65283608436584, 'test/f1-score': 0.7608695652173914, 'test/recall': 0.875, 'test/epoch_acc': 0.7555555555555555, 'test/epoch_loss': 0.6144020875295003, 'epoch': 9, '_wandb': {'runtime': 469}, '_timestamp': 1678739200.083605} {'eps': 0.1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0001} hopeful-sweep-3
124 122 {'test/precision': 0.8409090909090909, 'train/epoch_acc': 0.9975429975429976, 'train/batch_loss': 0.0980801358819008, '_step': 279, '_wandb': {'runtime': 353}, '_runtime': 357.5890119075775, 'test/f1-score': 0.8409090909090909, 'test/epoch_acc': 0.8444444444444444, 'train/epoch_loss': 0.03763626415181805, 'epoch': 9, '_timestamp': 1678738994.027642, 'test/recall': 0.8409090909090909, 'test/epoch_loss': 0.3028163850307465} {'_timestamp': 1678738994.027642, 'test/recall': 0.8409090909090909, 'test/f1-score': 0.8409090909090909, 'test/epoch_acc': 0.8444444444444444, '_step': 279, 'epoch': 9, '_wandb': {'runtime': 353}, '_runtime': 357.5890119075775, 'test/epoch_loss': 0.3028163850307465, 'train/epoch_acc': 0.9975429975429976, 'test/precision': 0.8409090909090909, 'train/batch_loss': 0.0980801358819008, 'train/epoch_loss': 0.03763626415181805} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 5, 'batch_size': 32, 'learning_rate': 0.003} lunar-sweep-1
125 123 {'test/f1-score': 0.7157894736842105, 'test/epoch_loss': 0.5541173484590318, '_timestamp': 1678738720.9443874, 'test/recall': 0.8947368421052632, 'test/epoch_acc': 0.7000000000000001, 'test/precision': 0.5964912280701754, '_step': 2059, 'epoch': 9, '_wandb': {'runtime': 529}, '_runtime': 529.6096863746643, 'train/epoch_acc': 0.6658476658476659, 'train/batch_loss': 0.7896618843078613, 'train/epoch_loss': 0.618659178367118} {'test/recall': 0.8947368421052632, 'test/f1-score': 0.7157894736842105, '_wandb': {'runtime': 529}, '_timestamp': 1678738720.9443874, '_runtime': 529.6096863746643, 'test/epoch_acc': 0.7000000000000001, 'test/precision': 0.5964912280701754, 'test/epoch_loss': 0.5541173484590318, 'train/epoch_acc': 0.6658476658476659, 'train/batch_loss': 0.7896618843078613, '_step': 2059, 'epoch': 9, 'train/epoch_loss': 0.618659178367118} {'eps': 1e-08, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.9, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 4, 'learning_rate': 0.1} stoic-sweep-2
126 124 {'train/epoch_loss': 0.016353931551580648, 'epoch': 9, '_wandb': {'runtime': 353}, '_runtime': 355.4184715747833, '_timestamp': 1678738469.1834886, 'test/recall': 0.6578947368421053, 'train/epoch_acc': 0.995085995085995, 'train/batch_loss': 0.0014543599681928754, '_step': 529, 'test/f1-score': 0.7575757575757577, 'test/epoch_acc': 0.8222222222222223, 'test/precision': 0.8928571428571429, 'test/epoch_loss': 0.4269479903909895} {'_runtime': 355.4184715747833, 'test/epoch_acc': 0.8222222222222223, 'test/precision': 0.8928571428571429, 'test/epoch_loss': 0.4269479903909895, 'epoch': 9, '_wandb': {'runtime': 353}, '_timestamp': 1678738469.1834886, 'test/recall': 0.6578947368421053, 'test/f1-score': 0.7575757575757577, 'train/epoch_acc': 0.995085995085995, 'train/batch_loss': 0.0014543599681928754, 'train/epoch_loss': 0.016353931551580648, '_step': 529} {'eps': 1e-08, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 16, 'learning_rate': 0.0001} dark-sweep-2
127 125 {'_wandb': {'runtime': 381}, '_timestamp': 1678738101.018471, 'test/f1-score': 0.8470588235294119, 'test/epoch_acc': 0.8555555555555556, 'test/epoch_loss': 0.40116495291392007, 'epoch': 9, '_runtime': 384.5172441005707, 'test/recall': 0.8181818181818182, 'test/precision': 0.8780487804878049, 'train/epoch_acc': 0.8673218673218673, 'train/batch_loss': 0.31195682287216187, 'train/epoch_loss': 0.3623260387038716, '_step': 1039} {'_step': 1039, '_runtime': 384.5172441005707, 'test/recall': 0.8181818181818182, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.8780487804878049, 'test/epoch_loss': 0.40116495291392007, 'train/epoch_acc': 0.8673218673218673, 'epoch': 9, '_wandb': {'runtime': 381}, '_timestamp': 1678738101.018471, 'test/f1-score': 0.8470588235294119, 'train/batch_loss': 0.31195682287216187, 'train/epoch_loss': 0.3623260387038716} {'eps': 0.1, 'gamma': 0.5, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0003} trim-sweep-1
128 126 {'epoch': 9, '_runtime': 560.7235152721405, 'test/f1-score': 0.8602150537634408, 'test/precision': 0.8163265306122449, 'train/epoch_acc': 0.7567567567567567, 'train/batch_loss': 0.6653294563293457, '_step': 2059, '_wandb': {'runtime': 560}, '_timestamp': 1678738182.1088202, 'test/recall': 0.9090909090909092, 'test/epoch_acc': 0.8555555555555556, 'test/epoch_loss': 0.6165981186760796, 'train/epoch_loss': 0.6107166709712448} {'epoch': 9, '_wandb': {'runtime': 560}, '_runtime': 560.7235152721405, 'test/recall': 0.9090909090909092, 'test/f1-score': 0.8602150537634408, 'train/epoch_loss': 0.6107166709712448, '_step': 2059, '_timestamp': 1678738182.1088202, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.8163265306122449, 'test/epoch_loss': 0.6165981186760796, 'train/epoch_acc': 0.7567567567567567, 'train/batch_loss': 0.6653294563293457} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.9, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 2, 'batch_size': 4, 'learning_rate': 0.001} sparkling-sweep-1
129 127 {'_step': 555, 'epoch': 1, '_timestamp': 1678737059.0375042, 'test/recall': 0.6818181818181818, 'test/epoch_acc': 0.6555555555555556, 'test/precision': 0.6382978723404256, '_wandb': {'runtime': 118}, '_runtime': 122.13349413871764, 'test/f1-score': 0.6593406593406593, 'test/epoch_loss': 0.6796493821673923, 'train/epoch_acc': 0.5515970515970516, 'train/batch_loss': 0.6759337782859802, 'train/epoch_loss': 0.6851893525744539} {'test/epoch_loss': 0.6796493821673923, 'train/epoch_acc': 0.5515970515970516, 'train/batch_loss': 0.6759337782859802, 'epoch': 1, '_wandb': {'runtime': 118}, '_runtime': 122.13349413871764, 'test/recall': 0.6818181818181818, 'test/precision': 0.6382978723404256, '_step': 555, '_timestamp': 1678737059.0375042, 'test/f1-score': 0.6593406593406593, 'test/epoch_acc': 0.6555555555555556, 'train/epoch_loss': 0.6851893525744539} {'eps': 1, 'gamma': 0.1, 'epochs': 10, 'beta_one': 0.99, 'beta_two': 0.5, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.0003} serene-sweep-1
130 128 {'_wandb': {'runtime': 455}, 'train/epoch_acc': 0.9914004914004914, 'test/precision': 0.9361702127659576, 'test/batch_loss': 0.1311825066804886, 'train/epoch_loss': 0.032788554922144414, '_runtime': 456.3002746105194, '_timestamp': 1678734250.8076646, 'test/f1-score': 0.8888888888888888, 'train/batch_loss': 0.003167948452755809, '_step': 1159, 'test/recall': 0.8461538461538461, 'test/epoch_loss': 0.45068282733360926, 'epoch': 9, 'test/epoch_acc': 0.8777777777777778} {'_runtime': 456.3002746105194, 'train/epoch_acc': 0.9914004914004914, 'train/epoch_loss': 0.032788554922144414, 'test/epoch_loss': 0.45068282733360926, 'train/batch_loss': 0.003167948452755809, 'test/f1-score': 0.8888888888888888, 'test/epoch_acc': 0.8777777777777778, 'test/batch_loss': 0.1311825066804886, 'test/precision': 0.9361702127659576, 'epoch': 9, '_wandb': {'runtime': 455}, 'test/recall': 0.8461538461538461, '_step': 1159, '_timestamp': 1678734250.8076646} {'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.003} super-sweep-10
131 129 {'_wandb': {'runtime': 563}, '_runtime': 564.230875492096, 'test/f1-score': 0.7173913043478259, 'test/batch_loss': 0.9658783674240112, 'train/epoch_loss': 0.5984233345387902, '_step': 2289, 'test/precision': 0.673469387755102, 'test/recall': 0.7674418604651163, 'train/epoch_acc': 0.687960687960688, 'train/batch_loss': 0.3260266184806824, 'epoch': 9, 'test/epoch_acc': 0.7111111111111111, 'test/epoch_loss': 0.5302444166607327, '_timestamp': 1678733784.6976814} {'test/epoch_loss': 0.5302444166607327, '_wandb': {'runtime': 563}, '_runtime': 564.230875492096, '_timestamp': 1678733784.6976814, 'test/precision': 0.673469387755102, 'train/epoch_acc': 0.687960687960688, '_step': 2289, 'epoch': 9, 'test/epoch_acc': 0.7111111111111111, 'train/epoch_loss': 0.5984233345387902, 'test/batch_loss': 0.9658783674240112, 'train/batch_loss': 0.3260266184806824, 'test/recall': 0.7674418604651163, 'test/f1-score': 0.7173913043478259} {'gamma': 0.1, 'epochs': 10, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01} distinctive-sweep-9
132 130 {'_step': 2289, 'test/f1-score': 0.9268292682926828, '_timestamp': 1678733210.1129615, 'test/epoch_acc': 0.9333333333333332, 'test/epoch_loss': 0.17092165086004468, 'epoch': 9, 'train/batch_loss': 0.007875862531363964, 'train/epoch_loss': 0.1743801347293527, 'test/precision': 1, 'test/batch_loss': 0.1419784128665924, 'train/epoch_acc': 0.9496314496314496, '_wandb': {'runtime': 527}, '_runtime': 527.6160025596619, 'test/recall': 0.8636363636363636} {'test/epoch_loss': 0.17092165086004468, '_step': 2289, '_wandb': {'runtime': 527}, '_timestamp': 1678733210.1129615, 'test/batch_loss': 0.1419784128665924, 'train/epoch_acc': 0.9496314496314496, 'epoch': 9, '_runtime': 527.6160025596619, 'test/recall': 0.8636363636363636, 'test/precision': 1, 'train/batch_loss': 0.007875862531363964, 'test/f1-score': 0.9268292682926828, 'test/epoch_acc': 0.9333333333333332, 'train/epoch_loss': 0.1743801347293527} {'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.0003} winter-sweep-8
133 131 {'test/f1-score': 0.9066666666666668, '_runtime': 453.52900218963623, 'test/recall': 0.8292682926829268, 'test/precision': 1, 'test/batch_loss': 0.27116066217422485, '_step': 1159, '_wandb': {'runtime': 452}, 'test/epoch_loss': 0.21558621691332924, 'train/epoch_loss': 0.07730489082323246, 'epoch': 9, '_timestamp': 1678732673.1225052, 'test/epoch_acc': 0.9222222222222224, 'train/epoch_acc': 0.9791154791154792, 'train/batch_loss': 0.04383014515042305} {'epoch': 9, '_runtime': 453.52900218963623, '_timestamp': 1678732673.1225052, 'train/epoch_loss': 0.07730489082323246, 'test/epoch_loss': 0.21558621691332924, 'train/batch_loss': 0.04383014515042305, '_step': 1159, 'test/recall': 0.8292682926829268, 'test/f1-score': 0.9066666666666668, 'train/epoch_acc': 0.9791154791154792, '_wandb': {'runtime': 452}, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 1, 'test/batch_loss': 0.27116066217422485} {'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.001} stilted-sweep-7
134 132 {'_timestamp': 1678732212.5530572, 'test/f1-score': 0.7010309278350516, 'test/epoch_acc': 0.6777777777777778, 'epoch': 9, 'test/batch_loss': 0.4716488718986511, 'train/batch_loss': 0.48304444551467896, '_step': 2289, '_wandb': {'runtime': 561}, '_runtime': 561.7993631362915, 'test/precision': 0.6538461538461539, 'test/recall': 0.7555555555555555, 'test/epoch_loss': 0.6190193812052409, 'train/epoch_acc': 0.7272727272727273, 'train/epoch_loss': 0.5549268187263967} {'_timestamp': 1678732212.5530572, 'test/recall': 0.7555555555555555, 'test/precision': 0.6538461538461539, '_wandb': {'runtime': 561}, '_runtime': 561.7993631362915, 'test/epoch_loss': 0.6190193812052409, '_step': 2289, 'test/epoch_acc': 0.6777777777777778, 'test/f1-score': 0.7010309278350516, 'test/batch_loss': 0.4716488718986511, 'train/epoch_acc': 0.7272727272727273, 'train/batch_loss': 0.48304444551467896, 'train/epoch_loss': 0.5549268187263967, 'epoch': 9} {'gamma': 0.5, 'epochs': 10, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01} summer-sweep-6
135 133 {'test/epoch_acc': 0.8222222222222223, 'test/batch_loss': 0.5068956017494202, 'train/epoch_loss': 0.5186349417126442, '_step': 1159, '_wandb': {'runtime': 453}, 'test/f1-score': 0.813953488372093, 'test/epoch_loss': 0.4936415394147237, 'train/batch_loss': 0.4434223175048828, 'test/recall': 0.7142857142857143, 'test/precision': 0.945945945945946, 'train/epoch_acc': 0.8218673218673218, 'epoch': 9, '_runtime': 454.3645238876343, '_timestamp': 1678731639.156168} {'epoch': 9, '_wandb': {'runtime': 453}, 'test/batch_loss': 0.5068956017494202, '_step': 1159, '_runtime': 454.3645238876343, 'test/f1-score': 0.813953488372093, 'test/epoch_acc': 0.8222222222222223, 'train/batch_loss': 0.4434223175048828, 'train/epoch_loss': 0.5186349417126442, 'test/recall': 0.7142857142857143, 'train/epoch_acc': 0.8218673218673218, '_timestamp': 1678731639.156168, 'test/precision': 0.945945945945946, 'test/epoch_loss': 0.4936415394147237} {'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0001} different-sweep-5
136 134 {'_wandb': {'runtime': 453}, '_runtime': 454.26038885116577, 'test/batch_loss': 0.5159374475479126, 'test/epoch_loss': 0.5482642173767089, '_step': 1159, 'epoch': 9, 'train/batch_loss': 0.5655931830406189, '_timestamp': 1678731176.111379, 'test/f1-score': 0.8354430379746836, 'test/epoch_acc': 0.8555555555555556, 'test/precision': 0.825, 'train/epoch_acc': 0.812039312039312, 'train/epoch_loss': 0.5429200196149016, 'test/recall': 0.8461538461538461} {'_runtime': 454.26038885116577, 'test/f1-score': 0.8354430379746836, 'test/batch_loss': 0.5159374475479126, 'test/epoch_loss': 0.5482642173767089, 'epoch': 9, '_wandb': {'runtime': 453}, 'test/epoch_acc': 0.8555555555555556, 'train/epoch_acc': 0.812039312039312, 'train/epoch_loss': 0.5429200196149016, '_step': 1159, '_timestamp': 1678731176.111379, 'test/recall': 0.8461538461538461, 'test/precision': 0.825, 'train/batch_loss': 0.5655931830406189} {'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0001} wise-sweep-4
137 135 {'test/batch_loss': 1.7588363885879517, 'train/batch_loss': 0.00470334617421031, 'train/epoch_loss': 0.02060394324720534, '_step': 2289, 'epoch': 9, 'test/f1-score': 0.8493150684931509, 'train/epoch_acc': 0.9963144963144964, '_runtime': 528.9760706424713, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.9393939393939394, 'test/epoch_loss': 0.24194780117250048, '_wandb': {'runtime': 528}, '_timestamp': 1678730714.7711067, 'test/recall': 0.775} {'_runtime': 528.9760706424713, 'test/f1-score': 0.8493150684931509, 'train/epoch_loss': 0.02060394324720534, 'epoch': 9, '_timestamp': 1678730714.7711067, 'test/batch_loss': 1.7588363885879517, 'train/batch_loss': 0.00470334617421031, '_wandb': {'runtime': 528}, 'test/recall': 0.775, 'test/epoch_acc': 0.8777777777777778, 'test/epoch_loss': 0.24194780117250048, '_step': 2289, 'train/epoch_acc': 0.9963144963144964, 'test/precision': 0.9393939393939394} {'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.003} misty-sweep-3
138 136 {'test/batch_loss': 0.455120325088501, 'train/batch_loss': 0.5347514748573303, 'test/precision': 0.8387096774193549, 'train/epoch_acc': 0.8329238329238329, '_runtime': 455.41485929489136, 'test/recall': 0.6842105263157895, 'test/epoch_acc': 0.8111111111111111, 'test/f1-score': 0.7536231884057972, 'train/epoch_loss': 0.42904984072326735, 'epoch': 9, '_wandb': {'runtime': 454}, '_timestamp': 1678730177.1362092, '_step': 1159, 'test/epoch_loss': 0.4792341656155056} {'epoch': 9, '_wandb': {'runtime': 454}, 'test/epoch_acc': 0.8111111111111111, 'test/precision': 0.8387096774193549, 'test/epoch_loss': 0.4792341656155056, '_step': 1159, 'test/recall': 0.6842105263157895, 'train/batch_loss': 0.5347514748573303, 'train/epoch_loss': 0.42904984072326735, 'train/epoch_acc': 0.8329238329238329, '_runtime': 455.41485929489136, '_timestamp': 1678730177.1362092, 'test/f1-score': 0.7536231884057972, 'test/batch_loss': 0.455120325088501} {'gamma': 0.1, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0003} unique-sweep-2
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@ -1774,7 +1516,7 @@
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[tool.poetry]
name = "thesis"
version = "0.1.0"
description = ""
authors = ["Tobias Eidelpes <e1527193@student.tuwien.ac.at>"]
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.10"
flask = "^2.0.3"
apscheduler = "^3.10.0"
albumentations = "^1.3.0"
pandas = "^1.1.5"
onnxruntime = "^1.8.0"
opencv-python = "^4.7.0"
torch = "^2.1.2"
torchvision = "^0.16.2"
numpy = "^1.18.0"
scipy = "^1.11.4"
scikit-learn = "^1.3.2"
Pillow = "^10.1.0"
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matplotlib = "^3.3.4"
jupyter = "^1.0.0"
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seaborn = "^0.13.0"
onnx = "^1.15.0"
tqdm = "^4.66.1"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

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{ pkgs ? import <nixpkgs> {} }:
pkgs.mkShell {
buildInputs = [
pkgs.python3
pkgs.poetry
pkgs.libGL
pkgs.glib
];
LD_LIBRARY_PATH = "$LD_LIBRARY_PATH:${pkgs.stdenv.cc.cc.lib}/lib:${pkgs.glib.out}/lib:${pkgs.libGL}/lib";
}

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\beamer@slide {fig:design}{15}
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\documentclass{beamer}
\beamertemplatenavigationsymbolsempty
\usetheme{default}
\usecolortheme{dolphin}
\usepackage{graphicx}
\usepackage{caption}
\usepackage{tikz}
\usepackage{dsfont}
\usepackage{siunitx}
\usepackage{booktabs}
\usepackage[labelformat=empty]{caption}
\usetikzlibrary{shapes,arrows}
% Define block styles
\tikzstyle{decision} = [diamond, draw, fill=blue!20, text width=4.5em, text badly centered, node distance=3cm, inner sep=0pt]
\tikzstyle{block} = [rectangle, draw, fill=blue!20, text width=5em, text centered, rounded corners, minimum height=4em]
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\setbeamerfont{caption}{size=\tiny}
\begin{document}
\title[Plant Detection and State Classification]{Plant Detection and
State Classification with Machine Learning}
\author{Tobias Eidelpes}
\date{March 12, 2024}
\begin{frame}
\maketitle
\end{frame}
\section{Introduction}
\begin{frame}
\frametitle{Problem Statement}
\begin{itemize}
\setlength{\itemsep}{1.1\baselineskip}
\item Automated detection of water stress \pause
\item Automated watering of household plants \pause
\item Decision-making \emph{in the field} \pause
\item No research so far in this context
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{Research Questions}
\begin{enumerate}
\setlength{\itemsep}{1.1\baselineskip}
\item How well does the model work in theory and how well in
practice? \pause
\item What are possible reasons for it to work/not work? \pause
\item What are possible improvements to the system in the future?
\end{enumerate}
\end{frame}
\section{Methodological Approach}
\begin{frame}
\frametitle{Methods}
\begin{columns}[c]
\column{.5\textwidth}
\begin{enumerate}
\setlength{\itemsep}{1.1\baselineskip}
\item Literature Review
\item Dataset Curation
\item Model Training
\item Optimization
\item Deployment
\item Evaluation
\end{enumerate}
\column{.5\textwidth}
\begin{center}
\includegraphics[width=\textwidth]{graphics/wilted\_007.jpg}
\end{center}
\end{columns}
\end{frame}
\section{Prototype Design}
\begin{frame}
\frametitle{Prototype Design: Requirements} \pause
\begin{itemize}
\setlength{\itemsep}{1.1\baselineskip}
\item Detect and Classify \pause
\item Publish Results via REST-API \pause
\item Reasonable Inference Time \pause
\item Reasonable Model Performance
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{Prototype Design}
\begin{figure}[htbp]
\centerline{\includegraphics[width=0.9\textwidth]{graphics/setup.pdf}}
\label{fig:design}
\end{figure}
\end{frame}
\section{Prototype Implementation}
\begin{frame}
\frametitle{Prototype Implementation: YOLOv7n}
\begin{minipage}[bt]{.49\textwidth}
\begin{itemize}
\setlength{\itemsep}{1.1\baselineskip}
\item Pretrained on COCO
\item OID classes \emph{Houseplant} and \emph{Plant}
\item Training Set
\begin{itemize}
\item \num{79204} images
\item \num{284130} bounding boxes
\end{itemize}
\item Validation Set
\begin{itemize}
\item \num{3091} images
\item \num{4092} bounding boxes
\end{itemize}
\end{itemize}
\end{minipage}
\begin{minipage}[bt]{.49\textwidth}
\vspace{.5cm}
\begin{figure}
\begin{center}
\includegraphics[width=.85\textwidth]{graphics/houseplant.jpg}
\caption{Earthy Tones For Fallsurlevif by Flickr User decor8
under CC BY 2.0}
\end{center}
\end{figure}
\end{minipage}
\end{frame}
\begin{frame}
\frametitle{Prototype Implementation: YOLOv7n}
\begin{figure}[htbp]
\begin{center}
\includegraphics[width=\textwidth]{graphics/model_fitness.pdf}
\end{center}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{Prototype Implementation: YOLOv7n}
\begin{figure}[htbp]
\begin{center}
\includegraphics[width=\textwidth]{graphics/val\_box\_obj\_loss.pdf}
\end{center}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{YOLOv7n Hyperparameter Optimization} \pause
\begin{itemize}
\setlength{\itemsep}{1.1\baselineskip}
\item Mutate 26 out of 30 hyperparameters \pause
\item Parent chosen randomly from top five previous generations \pause
\item 3 epochs per iteration \pause
\item 87 iterations \pause
\item Best with 0.6076 fitness
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{YOLOv7n Hyperparameter Optimization}
\begin{figure}[htbp]
\begin{center}
\includegraphics[width=\textwidth]{graphics/model_fitness\_final.pdf}
\end{center}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{Prototype Implementation: ResNet-50}
\begin{minipage}[bt]{.49\textwidth}
\begin{itemize}
\setlength{\itemsep}{1.1\baselineskip}
\item Pretrained on ImageNet
\item Training Set
\begin{itemize}
\item \num{384} healthy
\item \num{384} stressed
\end{itemize}
\item Validation Set
\begin{itemize}
\item \num{68} healthy
\item \num{68} stressed
\end{itemize}
\end{itemize}
\end{minipage}
\begin{minipage}[bt]{.49\textwidth}
\begin{center}
\includegraphics[width=\textwidth]{graphics/classifier-cam-cropped.pdf}
\end{center}
\end{minipage}
\end{frame}
\begin{frame}
\frametitle{Prototype Implementation: ResNet-50 Accuracy}
\begin{figure}[htbp]
\begin{center}
\includegraphics[width=\textwidth]{graphics/classifier-metrics-acc.pdf}
\caption{\normalsize Maximum validation accuracy of 0.9118 at epoch 27}
\end{center}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{Prototype Implementation: ResNet-50 Loss}
\begin{figure}[htbp]
\begin{center}
\includegraphics[width=\textwidth]{graphics/classifier-metrics-loss.pdf}
\end{center}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{ResNet-50 Hyperparameter Optimization}
\begin{itemize}
\setlength{\itemsep}{1.1\baselineskip}
\item Random search \pause
\item 10 epochs per iteration \pause
\item 138 iterations \pause
\item Best with 0.9783 $\mathrm{F}_{1}$-score
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{ResNet-50 Hyperparameter Optimization}
\begin{figure}[htbp]
\centerline{\includegraphics[width=\textwidth]{graphics/classifier-hyp-metrics.pdf}}
\caption{\normalsize Learning rate and batch size effect on
$\mathrm{F}_{1}$-score}
\end{figure}
\end{frame}
\section{Evaluation}
\begin{frame}
\frametitle{YOLOv7n Evaluation}
\begin{itemize}
\setlength{\itemsep}{1.1\baselineskip}
\item Test Set
\begin{itemize}
\item \num{9000} images
\item \num{12238} bounding boxes \pause
\end{itemize}
\end{itemize}
\begin{table}[h]
\centering
\begin{tabular}{lrrrr}
\toprule
{} & Precision & Recall & $\mathrm{F}_1$-score & Support \\
\midrule
Plant & \num{0.5476} & \num{0.7379} & \num{0.6286} & \num{12238} \\
\bottomrule
\end{tabular}
\caption{\scriptsize Results for the non-optimized object detection model}
\label{tab:yolo-metrics}
\end{table}
\begin{table}[h]
\centering
\begin{tabular}{lrrrr}
\toprule
{} & Precision & Recall & $\mathrm{F}_1$-score & Support \\
\midrule
Plant & \num{0.6334} & \num{0.7028} & \num{0.6663} & \num{12238} \\
\bottomrule
\end{tabular}
\caption{\scriptsize Results for the optimized object detection model}
\label{tab:yolo-metrics-hyp}
\end{table}
\end{frame}
\begin{frame}
\frametitle{ResNet-50 Evaluation}
\begin{center}
\begin{figure}[htbp]
\includegraphics[width=0.65\textwidth]{graphics/classifier-hyp-folds.pdf}
\caption{\scriptsize ROC curves and AUC for classifier 10-fold
cross-validation}
\end{figure}
\end{center}
\end{frame}
\begin{frame}
\frametitle{Aggregate Model Evaluation}
\begin{itemize}
\setlength{\itemsep}{1.1\baselineskip}
\item Pre-annotated Test Set
\begin{itemize}
\item \num{640} images
\item \num{766} bounding boxes healthy
\item \num{494} bounding boxes stressed \pause
\end{itemize}
\item Non-optimized model $\mathrm{mAP} = 0.3581$ \pause
\item Optimized model $\mathrm{mAP} = 0.3838$
\end{itemize}
\end{frame}
\section{Conclusion}
\begin{frame}
\frametitle{Limitations and Conclusions}
\begin{itemize}
\setlength{\itemsep}{0.75\baselineskip}
\item I am \emph{not} an expert labeler! \pause
\item Object detection performs well (mAP 0.5727) \pause
\item Optimized detector worse than non-optimized \pause
\item Inconsistent ground truth \pause
\item Robust classification
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{Research Questions Revisited}
\begin{enumerate}
\setlength{\itemsep}{1.1\baselineskip}
\item How well does the model work in theory and how well in practice? \pause
\begin{itemize}
\item Plant detection comparable to benchmarks \pause
\item Impressive stress classification \pause
\end{itemize}
\item What are possible reasons for it to work/not work? \pause
\begin{itemize}
\item Dataset curation \pause
\end{itemize}
\item What are possible improvements to the system in the future? \pause
\begin{itemize}
\item Use more computational resources \pause
\item Expert labeling
\end{itemize}
\end{enumerate}
\end{frame}
\begin{frame}
\centering
\Large
Thank you for your attention!
\end{frame}
\begin{frame}
\frametitle{ResNet-50 CAM}
\begin{figure}[htbp]
\centerline{\includegraphics[width=0.9\textwidth]{graphics/classifier-cam.pdf}}
\caption[]{\label{fig:classifier-cam} Top-right: CAM for
\emph{healthy}. Bot-right: CAM for \emph{stressed}}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{Aggregate Model Evaluation}
\begin{table}
\centering
\begin{tabular}{lrrrr}
\toprule
{} & Precision & Recall & $\mathrm{F}_{1}$-score & Support \\
\midrule
Healthy & \num{0.665} & \num{0.554} & \num{0.604} & \num{766} \\
Stressed & \num{0.639} & \num{0.502} & \num{0.562} & \num{494} \\
Weighted Avg & \num{0.655} & \num{0.533} & \num{0.588} & \num{1260} \\
\bottomrule
\end{tabular}
\caption{Metrics for the non-optimized aggregate model}
\label{tab:model-metrics}
\end{table}
\begin{table}
\centering
\begin{tabular}{lrrrr}
\toprule
{} & Precision & Recall & $\mathrm{F}_{1}$-score & Support \\
\midrule
Healthy & 0.711 & 0.555 & 0.623 & 766 \\
Stressed & 0.570 & 0.623 & 0.596 & 494 \\
Weighted Avg & 0.656 & 0.582 & 0.612 & 1260 \\
\bottomrule
\end{tabular}
\caption{Metrics for the optimized aggregate model}
\label{tab:model-metrics-hyp}
\end{table}
\end{frame}
\end{document}
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@ -64,7 +64,7 @@
\setadvisor{Ao.Univ.-Prof. Dr.}{Horst Eidenberger}{}{male}
\setregnumber{01527193}
\setdate{27}{12}{2023} % Set date with 3 arguments: {day}{month}{year}.
\setdate{30}{12}{2023} % Set date with 3 arguments: {day}{month}{year}.
\settitle{\thesistitle}{Plant Detection and State Classification with Machine Learning} % Sets English and German version of the title (both can be English or German).
% Select the thesis type: bachelor / master / doctor / phd-school.
@ -190,36 +190,90 @@ Challenge}
\begin{kurzfassung}
Wassermangel in Zimmerpflanzen kann ihr Wachstum negativ
beeinflussen. Derzeitige Lösungen zur Überwachung von Wasserstress
sind hauptsächlich für landwirtschaftliche Anwendungen
vorgesehen. Wir präsentieren den ersten Deep-Learning-basierten
Prototyp zur Klassifizierung des Wasserstresslevels gängiger
Zimmerpflanzen. Unser zweistufiger Ansatz besteht aus einem
Erkennungs- und einem Klassifizierungsschritt und wird anhand eines
eigens erstellten Datensatzes evaluiert. Die Parameter des Modells
werden mit gängigen Methoden der Hyperparameteroptimierung
ausgewählt. Der Prototyp wird auf einem embedded Computer
bereitgestellt, der eine autonome Pflanzenüberwachung
ermöglicht. Die Vorhersagen unseres Modells werden kontinuierlich
über eine API veröffentlicht, wodurch nachgelagerte
Bewässerungssysteme automatisch Zimmerpflanzen ohne menschliche
Intervention bewässern können. Unser optimiertes Modell erreicht
einen mAP-Wert von \num{0.3838}.
beeinflussen. Bestehende Lösungen zur Überwachung von Wasserstress
sind in erster Linie für landwirtschaftliche Kontexte gedacht, bei
denen nur eine kleine Auswahl an Pflanzen von Interesse ist. Bislang
gab es keine Forschung im Haushaltskontext, wo die Vielfalt der
Pflanzen wesentlich größer ist und es daher schwieriger ist,
Wasserstress zu erfassen. Außerdem beinhalten derzeitige Ansätze
entweder keinen eigenen Pflanzenerkennungsschritt oder es kommt
traditionelle Feature Extraction zur Anwendung. Wir entwickeln einen
Prototyp zur Erkennung und nachfolgender Klassifizierung des
Wasserstresses von Pflanzen, der ausschließlich auf Deep Learning
basiert.
Unser zweistufiger Ansatz besteht aus einem Erkennungs- und einem
Klassifizierungsschritt. In der Erkennungsphase werden die Pflanzen
identifiziert und aus dem Originalbild ausgeschnitten. Die
Ausschnitte werden an das Klassifizierungsmodell weitergeleitet, das
die Wahrscheinlichkeit für Wasserstress ausgibt. Wir verwenden
Transfer Learning und führen die Feinabstimmung der beiden Modelle
anhand zweier Datensätze durch. Jedes Modell wird mithilfe einer
Hyperparameter-Suche optimiert und zunächst einzeln und dann im
Aggregat auf einem eigens erstellten Datensatz evaluiert. Wir
stellen beide Modelle auf einem Nvidia Jetson Nano bereit, der in
der Lage ist, Pflanzen autonom über eine angeschlossene Kamera zu
klassifizieren. Die Ergebnisse der Pipeline werden kontinuierlich
über eine API veröffentlicht. Nachgeschaltete Bewässerungssysteme
können die Vorhersagen zum Wasserstress nutzen, um die Hauspflanzen
ohne menschliches Zutun zu bewässern.
Die beiden Modelle zusammengenommen erreichen einen mAP-Wert von
\num{0.3581} in der nicht optimierten Version. Beide Modelle sind in
der Lage, mit verschiedenen Lichtverhältnissen, verschiedenen
Blickwinkeln und einer Vielfalt an Pflanzen umzugehen. Die
optimierte Pipeline erreicht einen mAP-Wert von \num{0.3838}. Im
Vergleich zur nicht optimierten Version ist die Genauigkeit für
nicht gestresste Pflanzen höher, aber geringer für die gestresste
Klasse. Die Spezifität für die nicht gestresste Klasse bleibt im
Vergleich zur nicht optimierten Basislinie auf demselben Niveau, ist
aber um \num{12.1} Prozentpunkte höher für die gestresste
Klasse. Das gewichtete harmonische Mittel ($F_{1}$-score) für beide
Klassen konnte um \num{2.4} Prozentpunkte verbessert werden. Diese
Ergebnisse zeigen, dass unser zweistufiger Ansatz funktioniert und
ein vielversprechender erster Schritt zur Klassifizierung des
Zustands von Zimmerpflanzen ist.
\end{kurzfassung}
\begin{abstract}
Water deficiency in household plants can adversely affect
growth. Existing solutions to monitor water stress are primarily
intended for agricultural contexts. We present the first deep
learning based prototype to classify water stress of common
household plants. Our two-stage approach consists of a detection and
a classification step and is evaluated on a new dataset. The model
parameters are optimized with a hyperparameter search. The prototype
is deployed to an embedded device enabling autonomous plant
monitoring. The predictions of our model are published continuously
via an API, allowing downstream watering systems to automatically
water household plants without human intervention. Our optimized
model achieves a mAP of \num{0.3838} on unseen images.
intended for agricultural contexts where only a small selection of
plants are of interest. To date, there has been no research in
household settings where the variety of plants is considerably
higher and it is thus more difficult to obtain accurate measures of
water stress. Furthermore, current approaches either do not detect
plants in images first or use traditional feature extraction for
plant detection. We develop a prototype to detect plants and
classify them into water-stressed or not using deep learning based
methods exclusively.
Our two-stage approach consists of a detection and a classification
step. In the detection step, plants are identified and cut out from
the original image. The cutouts are passed to the classifier which
outputs a probability for water stress. We use transfer learning to
start from a robust base and fine-tune both models for their
respective tasks. Each model is optimized using hyperparameter
optimization and first evaluated individually and then in aggregate
on a custom dataset. We deploy both models to an Nvidia Jetson Nano
which is able to survey plants autonomously via an attached
camera. The results of the pipeline are published continuously via
an API. Downstream watering systems can use the water stress
predictions to water the plants without human intervention.
The two models in aggregate achieve a mAP of \num{0.3581} for the
non-optimized version. Both constituent models have robust feature
extraction capabilities and are able to cope with various lighting
conditions, different angles and a wide variety of household
plants. The optimized pipeline achieves a mAP of \num{0.3838} on
unseen images with higher precision for the non-stressed but lower
precision for the stressed class. Recall for the non-stressed class
remains at the same level compared to the non-optimized baseline but
is \num{12.1} percentage points higher for the stressed class. The
weighted $F_{1}$-score across both classes was improved by \num{2.4}
percentage points. These results show that our two-stage approach is
viable and a promising first step for plant state classification for
household plants.
\end{abstract}
% Select the language of the thesis, e.g., english or naustrian.
@ -466,7 +520,7 @@ models. Chapter~\ref{chap:implementation} expands on how the datasets
are used during training as well as how the prototype publishes its
classification results. Chapter~\ref{chap:evaluation} shows the
results of the testing phases as well as the performance of the
aggregate model. Futhermore, the results are compared with the
aggregate model. Furthermore, the results are compared with the
expectations and it is discussed whether they are explainable in the
context of the task at hand as well as benchmark results from other
datasets (\gls{coco} \cite{lin2015}). Chapter~\ref{chap:conclusion}
@ -496,12 +550,14 @@ The term machine learning was first used by \textcite{samuel1959} in
1959 in the context of teaching a machine how to play the game
Checkers. \textcite{mitchell1997a} defines learning in the context of
programs as:
\begin{quote}
\begin{quote}{\cite[p.2]{mitchell1997a}}
A computer program is said to \textbf{learn} from experience $E$
with respect to some class of tasks $T$ and performance measure $P$,
if its performance at tasks in $T$, as measured by $P$, improves
with experience $E$. \cite[p.2]{mitchell1997a}
with experience $E$.
\end{quote}
In other words, if the aim is to learn to win at a game, the
performance measure $P$ is defined as the ability to win at that
game. The tasks in $T$ are playing the game multiple times, and the
@ -509,7 +565,7 @@ experience $E$ is gained by letting the program play the game against
itself.
Machine learning is thought to be a sub-field of \gls{ai}. \gls{ai} is
a more general term for the scientific endeavour of creating things
a more general term for the scientific endeavor of creating things
which possess the kind of intelligence we humans have. Since those
things will not have been created \emph{naturally}, their intelligence
is termed \emph{artificial}. Within the field of \gls{ai} there have
@ -628,7 +684,7 @@ The earliest attempts at describing learning machines were by
\textcite{mcculloch1943} with the idea of the \emph{perceptron}. This
idea was implemented in a more general sense by
\textcite{rosenblatt1957,rosenblatt1962} as a physical machine. At its
core, the perceptron is the simplest artifical neural network with
core, the perceptron is the simplest artificial neural network with
only one neuron in the center. The neuron takes all its inputs,
aggregates them with a weighted sum and outputs 1 if the result is
above some threshold $\theta$ and 0 if it is not (see
@ -648,7 +704,7 @@ variables.
Due to the inherent limitations of perceptrons to only be able to
classify linearly separable data, \glspl{mlp} are the bedrock of
modern artifical neural networks. By adding an input layer, a hidden
modern artificial neural networks. By adding an input layer, a hidden
layer, and an output layer as well as requiring the activation
function of each neuron to be non-linear, a \gls{mlp} can classify
also non-linear data. Every neuron in each layer is fully connected to
@ -657,7 +713,7 @@ straightforward case of a feedforward
network. Figure~\ref{fig:neural-network} shows the skeleton of a
\gls{mlp}.
There are two types of artifical neural networks: feedforward and
There are two types of artificial neural networks: feedforward and
recurrent networks. Their names refer to the way information flows
through the network. In a feedforward network, the information enters
the network and flows only uni-directionally to the output nodes. In a
@ -700,7 +756,7 @@ The simplest activation function is the identity function. It is defined as
\begin{equation}
\label{eq:identity}
g(x) = x
g(x) = x.
\end{equation}
If all layers in an artificial neural network use the identity
@ -750,14 +806,14 @@ logistic function in machine learning. It is defined as
\begin{equation}
\label{eq:sigmoid}
\sigma(x) = \frac{1}{1 + e^{-x}}
\sigma(x) = \frac{1}{1 + e^{-x}}.
\end{equation}
It has a characteristic S-shaped curve, mapping each input value to a
number between $0$ and $1$, regardless of input size. This
\emph{squashing} property is particularly desirable for binary
classification problems because the outputs can be interpreted as
probabilities. Additionally to the squashing propery, it is also a
probabilities. Additionally to the squashing property, it is also a
saturating function because large values map to $1$ and very small
values to $0$. If a learning algorithm has to update the weights in
the network, saturated neurons are very inefficient and difficult to
@ -805,8 +861,8 @@ state, the model's capability of learning new patterns is
diminished. To address this problem, there are two possibilities. One
solution is to make sure that the learning rate is not set too high,
which reduces the problem but does not fully remove it. Another
solution is to use one of the several variants of the ReLU function
such as leaky \gls{relu}, \gls{elu}, and \gls{silu}.
solution is to use one of the several variants of the \gls{relu}
function such as leaky \gls{relu}, \gls{elu}, and \gls{silu}.
In recent years, the \gls{relu} function has become the most popular
activation function for deep neural networks and is recommended as the
@ -959,15 +1015,15 @@ summation of the pixels above and to the left of it. This
representation allows them to quickly and efficiently calculate
Haar-like features.
The Haar-like features are passed to a modified AdaBoost
algorithm \cite{freund1995} which only selects the (presumably) most
important features. At the end there is a cascading stage of
classifiers where regions are only considered further if they are
promising. Every additional classifier adds complexity, but once a
classifier rejects a sub-window, the processing stops and the
algorithm moves on to the next window. Despite their final structure
containing 32 classifiers, the sliding-window approach is fast and
achieves comparable results to the state of the art in 2001.
The Haar-like features are passed to a modified AdaBoost algorithm
\cite{freund1995} which only selects the (presumably) most important
features. At the end there is a cascading stage of classifiers where
regions are only considered further if they are promising. Every
additional classifier adds complexity, but once a classifier rejects a
sub-window, the processing stops and the algorithm moves on to the
next window. Despite their final structure containing \num{32}
classifiers, the sliding-window approach is fast and achieves
comparable results to the state of the art in 2001.
\subsubsection{HOG Detector}
\label{sssec:obj-hog}
@ -987,12 +1043,13 @@ are then passed as a feature vector to a classifier.
\textcite{dalal2005} successfully use the \gls{hog} with a linear
\gls{svm} for classification to detect humans in images. They work
with images of 64 by 128 pixels and make sure that the image contains
a margin of 16 pixels around the person. Decreasing the border by
either enlarging the person or reducing the overall image size results
in worse performance. Unfortunately, their method is far from being
able to process images in real time—a $320$ by $240$ image takes
roughly a second to process.
with images of \num{64} by \num{128} pixels and make sure that the
image contains a margin of \num{16} pixels around the
person. Decreasing the border by either enlarging the person or
reducing the overall image size results in worse
performance. Unfortunately, their method is far from being able to
process images in real time—a $320$ by $240$ image takes roughly a
second to process.
\subsubsection{Deformable Part-Based Model}
\label{sssec:obj-dpm}
@ -1028,20 +1085,21 @@ corresponding \gls{cnn} layer.
\label{ssec:theory-dl-based}
After the publication of the \gls{dpm}, the field of object detection
did not make significant advances regarding speed or accuracy. Only
the (re-)introduction of \glspl{cnn} by \textcite{krizhevsky2012} with
their AlexNet architecture and their subsequent win of the
\gls{ilsvrc} 2012 gave the field a new influx of ideas. The
availability of the 12 million labeled images in the ImageNet dataset
\cite{deng2009} allowed a shift from focusing on better methods to
being able to use more data to train models. Earlier models had
difficulties with making use of the large dataset since training was
unfeasible. AlexNet, however, provided an architecture which was able
to be trained on two \glspl{gpu} within 6 days. For an in depth
overview of AlexNet see section~\ref{sssec:theory-alexnet}. Object
detection networks from 2014 onward either follow a \emph{one-stage}
or \emph{two-stage} detection approach. The following sections go into
detail about each model category.
did not make significant advances regarding speed or accuracy until
2012. Only the (re-)introduction of \glspl{cnn} by
\textcite{krizhevsky2012} with their AlexNet architecture and their
subsequent win of the \gls{ilsvrc} 2012 gave the field a new influx of
ideas. The availability of the \num{12e6} labeled images in the
ImageNet dataset \cite{deng2009} allowed a shift from focusing on
better methods to being able to use more data to train models. Earlier
models had difficulties with making use of the large dataset since
training was unfeasible. AlexNet, however, provided an architecture
which was able to be trained on two \glspl{gpu} within six days. For
an in depth overview of AlexNet see
section~\ref{sssec:theory-alexnet}. Object detection networks from
2014 onward either follow a \emph{one-stage} or \emph{two-stage}
detection approach. The following sections go into detail about each
model category.
\subsection{Two-Stage Detectors}
\label{ssec:theory-two-stage}
@ -1059,7 +1117,7 @@ often not as efficient as one-stage detectors.
\textcite{girshick2014} were the first to propose using feature
representations of \glspl{cnn} for object detection. Their approach
consists of generating around $2000$ region proposals and passing
consists of generating around \num{2000} region proposals and passing
these on to a \gls{cnn} for feature extraction. The fixed-length
feature vector is used as input for a linear \gls{svm} which
classifies the region. They name their method R-\gls{cnn}, where the R
@ -1067,17 +1125,17 @@ stands for region.
R-\gls{cnn} uses selective search to generate region proposals
\cite{uijlings2013}.The authors use selective search's \emph{fast
mode} to generate the $2000$ proposals and warp (i.e. aspect ratios
are not retained) each proposal into the image dimensions required by
the \gls{cnn}. The \gls{cnn}, which matches the architecture of
AlexNet \cite{krizhevsky2012}, generates a $4096$-dimensional feature
vector and each feature vector is scored by a linear \gls{svm} for
each class. Scored regions are selected/discarded by comparing each
region to other regions within the same class and rejecting them if
there exists another region with a higher score and greater \gls{iou}
than a threshold. The linear \gls{svm} classifiers are trained to only
label a region as positive if the overlap, as measured by \gls{iou},
is above $0.3$.
mode} to generate the \num{2000} proposals and warp (i.e. aspect
ratios are not retained) each proposal into the image dimensions
required by the \gls{cnn}. The \gls{cnn}, which matches the
architecture of AlexNet \cite{krizhevsky2012}, generates a
\num{4096}-dimensional feature vector and each feature vector is
scored by a linear \gls{svm} for each class. Scored regions are
selected/discarded by comparing each region to other regions within
the same class and rejecting them if there exists another region with
a higher score and greater \gls{iou} than a threshold. The linear
\gls{svm} classifiers are trained to only label a region as positive
if the overlap, as measured by \gls{iou}, is above $0.3$.
While the approach of generating region proposals is not new, using a
\gls{cnn} purely for feature extraction is. Unfortunately, R-\gls{cnn}
@ -1123,7 +1181,7 @@ set at a \gls{map} of 59.2\%.
Fast R-\gls{cnn} was proposed by \textcite{girshick2015a} to fix the
three main problems R-\gls{cnn} and \gls{spp}-net have. The first
problem is that the training for both models is
multi-stage. R-\gls{cnn} finetunes the convolutional network which is
multi-stage. R-\gls{cnn} fine-tunes the convolutional network which is
responsible for feature extraction and then trains \glspl{svm} to
classify the feature vectors. The third stage consists of training the
bounding box regressors. The second problem is the training time which
@ -1134,10 +1192,10 @@ the convolutional network) upwards of \qty{13}{\s\per image}.
Fast R-\gls{cnn} deals with these problems by having an architecture
which allows it to take in images and object proposals at once and
process them simultaneously to arrive at the results. The outputs of
the network are the class an object proposal belongs to and 4 scalar
values representing the bounding box of the object. Unfortunately,
this approach still requires a separate object proposal generator such
as selective search \cite{uijlings2013}.
the network are the class an object proposal belongs to and four
scalar values representing the bounding box of the
object. Unfortunately, this approach still requires a separate object
proposal generator such as selective search \cite{uijlings2013}.
\subsubsection{Faster R-\gls{cnn}}
\label{sssec:theory-faster-rcnn}
@ -1192,14 +1250,14 @@ with the layer beneath it via element-wise addition and convolved with
a one by one convolutional layer to reduce channel dimensions and to
smooth out potential artifacts introduced during the upsampling
step. The results of that operation constitute the new \emph{top
layer} and the process continues with the layer below it until the
layer} and the process continues with the layer below it until the
finest resolution feature map is generated. In this way, the features
of the different layers at different scales are fused to obtain a
feature map with high semantic information but also high spatial
information.
\textcite{lin2017} report results on \gls{coco} with a \gls{map}@0.5
of 59.1\% with a Faster R-\gls{cnn} structure and a ResNet-101
of 59.1\% with a Faster R-\gls{cnn} structure and a \gls{resnet}-101
backbone. Their submission does not include any specific improvements
such as hard negative mining \cite{shrivastava2016} or data
augmentation.
@ -1302,7 +1360,7 @@ on examples which are harder to achieve a good confidence score on.
\textcite{lin2017b} implement their focal loss with a simple one-stage
detector called \emph{RetinaNet}. It makes use of previous advances in
object detection and classification by including a \gls{fpn} on top of
a ResNet \cite{he2016} as the backbone and using anchors for the
a \gls{resnet} \cite{he2016} as the backbone and using anchors for the
different levels in the feature pyramid. Attached to the backbone are
two subnetworks which classify anchor boxes and regress them to the
ground truth boxes. The results are that the RetinaNet-101-500 version
@ -1416,23 +1474,23 @@ increases the amount of feature maps to $16$ which aims to increase
the richness of the learned representations. Another pooling layer
follows which reduces the size of each of the $16$ feature maps to
five by five pixels. A dense block of three fully-connected layers of
120, 84 and 10 neurons respectively serves as the actual classifier in
the network. The last layer uses the euclidean \gls{rbf} to compute
the class an image belongs to (0-9 digits).
120, 84 and 10 neurons serves as the actual classifier in the
network. The last layer uses the euclidean \gls{rbf} to compute the
class an image belongs to (0-9 digits).
The performance of LeNet-5 was measured on the \gls{mnist} database
which consists of $70000$ labeled images of handwritten digits. The
\gls{mse} on the test set is 0.95\%. This result is impressive
considering that character recognition with a \gls{cnn} had not been
done before. However, standard machine learning methods of the time,
such as manual feature engineering and \glspl{svm}, achieved a similar
error rate, even though they are much more memory-intensive. LeNet-5
was conceived to take advantage of the (then) large \gls{mnist}
database. Since there were not many datasets available at the time,
especially with more samples than in the \gls{mnist} database,
\glspl{cnn} were not widely used even after their viability had been
demonstrated by \textcite{lecun1998}. Only in 2012
\textcite{krizhevsky2012} reintroduced \glspl{cnn} (see
which consists of \num{70000} labeled images of handwritten
digits. The \gls{mse} on the test set is 0.95\%. This result is
impressive considering that character recognition with a \gls{cnn} had
not been done before. However, standard machine learning methods of
the time, such as manual feature engineering and \glspl{svm}, achieved
a similar error rate, even though they are much more
memory-intensive. LeNet-5 was conceived to take advantage of the
(then) large \gls{mnist} database. Since there were not many datasets
available at the time, especially with more samples than in the
\gls{mnist} database, \glspl{cnn} were not widely used even after
their viability had been demonstrated by \textcite{lecun1998}. Only in
2012 \textcite{krizhevsky2012} reintroduced \glspl{cnn} (see
section~\ref{ssec:theory-dl-based}) and since then most
state-of-the-art image classification methods have used them.
@ -1479,7 +1537,7 @@ maximum values are then put back into each two by two area (depending
on the kernel size). This process loses information because a
max-pooling layer is not invertible. The subsequent \gls{relu}
function can be easily inverted because negative values are squashed
to zero and and positive values are retained. The final deconvolution
to zero and positive values are retained. The final deconvolution
operation concerns the convolutional layer itself. In order to
\emph{reconstruct} the original spatial dimensions (before
convolution), a transposed convolution is performed. This process
@ -1520,7 +1578,7 @@ other and a \emph{stem} with convolutions at the beginning as well as
two auxiliary classifiers which help retain the gradient during
backpropagation. The auxiliary classifiers are only used during
training. The authors submitted multiple model versions to the 2004
\gls{ilsvrc} and their ensemble prediction model consisting of 7
\gls{ilsvrc} and their ensemble prediction model consisting of seven
GoogleNets achieved a top-5 error rate of 6.67\%, which resulted in
first place.
@ -1573,21 +1631,21 @@ section~\ref{sec:methods-classification}.
\label{sssec:theory-densenet}
The authors of DenseNet \cite{huang2017} go one step further than
ResNets by connecting every convolutional layer to every other layer
in the chain. Previously, each layer was connected in sequence with
the one before and the one after it. Residual connections establish a
link between the previous layer and the next one but still do not
always propagate enough information forward. These \emph{shortcut
connections} from earlier layers to later layers are thus only taking
place in an episodic way for short sections in the chain. DenseNets
are structured in a way such that every layer receives the feature map
of every previous layer as input. In ResNets, information from
previous layers is added on to the next layer via element-wise
addition. DenseNets concatenate the features of the previous
layers. The number of feature maps per layer has to be kept low so
that the subsequent layers can still process their inputs. Otherwise,
the last layer in each dense block would receive too many channels
which increases computational complexity.
\glspl{resnet} by connecting every convolutional layer to every other
layer in the chain. Previously, each layer was connected in sequence
with the one before and the one after it. Residual connections
establish a link between the previous layer and the next one but still
do not always propagate enough information forward. These
\emph{shortcut connections} from earlier layers to later layers are
thus only taking place in an episodic way for short sections in the
chain. DenseNets are structured in a way such that every layer
receives the feature map of every previous layer as input. In
\glspl{resnet}, information from previous layers is added on to the
next layer via element-wise addition. DenseNets concatenate the
features of the previous layers. The number of feature maps per layer
has to be kept low so that the subsequent layers can still process
their inputs. Otherwise, the last layer in each dense block would
receive too many channels which increases computational complexity.
The authors construct their network from multiple dense blocks which
are connected via a batch normalization layer, a one by one
@ -1919,14 +1977,14 @@ was trained with a dataset containing images of maize, okra, and
soybean at different stages of growth and under stress and no
stress. The researchers did not include an object detection step
before image classification and compiled a fairly small dataset of
$1200$ images. Of the three models, GoogLeNet beat the other two with
a sizable lead at a classification accuracy of >94\% for all three
types of crop. The authors attribute its success to its inherently
deeper structure and application of multiple convolutional layers at
different stages. Unfortunately, all of the images were taken at the
same $\ang{45}\pm\ang{5}$ angle and it stands to reason that the models
would perform significantly worse on images taken under different
conditions.
\num{1200} images. Of the three models, GoogLeNet beat the other two
with a sizable lead at a classification accuracy of >94\% for all
three types of crop. The authors attribute its success to its
inherently deeper structure and application of multiple convolutional
layers at different stages. Unfortunately, all of the images were
taken at the same $\ang{45}\pm\ang{5}$ angle and it stands to reason
that the models would perform significantly worse on images taken
under different conditions.
\textcite{ramos-giraldo2020} detected water stress in soybean and corn
crops with a pretrained model based on DenseNet-121 (see
@ -1949,7 +2007,7 @@ classification scores on corn and soybean with a low-cost setup.
\textcite{azimi2020} demonstrate the efficacy of deep learning models
versus classical machine learning models on chickpea plants. The
authors created their own dataset in a laboratory setting for stressed
and non-stressed plants. They acquired $8000$ images at eight
and non-stressed plants. They acquired \num{8000} images at eight
different angles in total. For the classical machine learning models,
they extracted feature vectors using \gls{sift} and \gls{hog}. The
features are fed into three classical machine learning models:
@ -1957,30 +2015,28 @@ features are fed into three classical machine learning models:
algorithm. On the deep learning side, they used their own \gls{cnn}
architecture and the pretrained ResNet-18 (see
section~\ref{sssec:theory-resnet}) model. The accuracy scores for the
classical models was in the range of $\qty{60}{\percent}$ to
$\qty{73}{\percent}$ with the \gls{svm} outperforming the two
others. The \gls{cnn} achieved higher scores at $\qty{72}{\percent}$
to $\qty{78}{\percent}$ and ResNet-18 achieved the highest scores at
$\qty{82}{\percent}$ to $\qty{86}{\percent}$. The results clearly show
the superiority of deep learning over classical machine learning. A
downside of their approach lies in the collection of the images. The
background in all images was uniformly white and the plants were
prominently placed in the center. It should, therefore, not be assumed
that the same classification scores can be achieved on plants in the
field with messy and noisy backgrounds as well as illumination changes
and so forth.
classical models was in the range of 60\% to 73\% with the \gls{svm}
outperforming the two others. The \gls{cnn} achieved higher scores at
72\% to 78\% and ResNet-18 achieved the highest scores at 82\% to
86\%. The results clearly show the superiority of deep learning over
classical machine learning. A downside of their approach lies in the
collection of the images. The background in all images was uniformly
white and the plants were prominently placed in the center. It should,
therefore, not be assumed that the same classification scores can be
achieved on plants in the field with messy and noisy backgrounds as
well as illumination changes and so forth.
\textcite{venal2019} combine a standard \gls{cnn} architecture with a
\gls{svm} for classification. The \gls{cnn} acts as a feature
extractor and instead of using the last fully-connected layers of an
off-the-shelf \gls{cnn}, they replace them with a \gls{svm}. They use
this classifier to determine which biotic or abiotic stresses soybeans
suffer from. Their dataset consists of $65184$ $64$ by $64$ RGB
images of which around $40000$ were used for training and $6000$ for
testing. All images show a close-up of a soybean leaf. Their \gls{cnn}
architecture makes use of three Inception modules (see
section~\ref{sssec:theory-googlenet}) with \gls{se} blocks and
\gls{bn} layers in-between. Their model achieves an average
suffer from. Their dataset consists of \num{65184} $64$ by $64$ RGB
images of which around \num{40000} were used for training and
\num{6000} for testing. All images show a close-up of a soybean
leaf. Their \gls{cnn} architecture makes use of three Inception
modules (see section~\ref{sssec:theory-googlenet}) with \gls{se}
blocks and \gls{bn} layers in-between. Their model achieves an average
$\mathrm{F}_1$-score of 97\% and an average accuracy of 97.11\% on the
test set. Overall, the hybrid structure of their model is promising,
but it is not clear why only using the \gls{cnn} as a feature
@ -2509,8 +2565,8 @@ phases, we will list a small selection of them.
\item[HSV-saturation] Randomly change the saturation of the color
channels.
\item[HSV-value] Randomly change the value of the color channels.
\item[Translation] Randomly \emph{translate}, that is, move the image
by a specified amount of pixels.
\item[Translation] Randomly \emph{translate}, i.e., move the image by
a specified amount of pixels.
\item[Scaling] Randomly scale the image up and down by a factor.
\item[Rotation] Randomly rotate the image.
\item[Inversion] Randomly flip the image along the $x$ or the
@ -2622,7 +2678,7 @@ nor recall change materially during training. In fact, precision
starts to decrease from the beginning, while recall experiences a
barely noticeable increase. Taken together with the box and object
loss from figure~\ref{fig:box-obj-loss}, we speculate that the
pre-trained model already generalizes well to plant detection because
pretrained model already generalizes well to plant detection because
one of the categories in the \gls{coco} \cite{lin2015} dataset is
\emph{potted plant}. Any further training solely impacts the
confidence of detection but does not lead to higher detection
@ -2840,14 +2896,14 @@ which is hyperparameter optimization \cite{bergstra2012}.
\toprule
Parameter & Values \\
\midrule
optimizer & adam, sgd \\
batch size & 4, 8, 16, 32, 64 \\
learning rate & 0.0001, 0.0003, 0.001, 0.003, 0.01, 0.1 \\
step size & 2, 3, 5, 7 \\
gamma & 0.1, 0.5 \\
beta one & 0.9, 0.99 \\
beta two & 0.5, 0.9, 0.99, 0.999 \\
eps & 0.00000001, 0.1, 1 \\
Optimizer & Adam, \gls{sgd} \\
Batch Size & 4, 8, 16, 32, 64 \\
Learning Rate & 0.0001, 0.0003, 0.001, 0.003, 0.01, 0.1 \\
Step Size & 2, 3, 5, 7 \\
Gamma & 0.1, 0.5 \\
Beta One & 0.9, 0.99 \\
Beta Two & 0.5, 0.9, 0.99, 0.999 \\
Eps & 0.00000001, 0.1, 1 \\
\bottomrule
\end{tabular}
\caption{Hyperparameters and their possible values during