Add report for hyper-parameter optimization
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,summary,config,name
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0,"{'test/epoch_loss': 0.5664619127909343, 'train/epoch_acc': 0.8230958230958231, 'train/batch_loss': 0.33577921986579895, 'epoch': 9, '_wandb': {'runtime': 363}, '_timestamp': 1680692970.2016854, 'test/recall': 0.6170212765957447, 'test/precision': 0.8285714285714286, '_step': 2059, '_runtime': 367.13677954673767, 'test/f1-score': 0.7073170731707318, 'test/epoch_acc': 0.7333333333333334, 'train/epoch_loss': 0.4241055610431793}","{'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
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1,"{'test/recall': 0.8222222222222222, 'test/precision': 0.6851851851851852, '_runtime': 341.8420207500458, '_timestamp': 1680692589.503975, '_wandb': {'runtime': 338}, 'test/f1-score': 0.7474747474747475, 'test/epoch_acc': 0.7222222222222222, 'test/epoch_loss': 0.6454579922888014, 'train/epoch_acc': 0.7125307125307125, 'train/batch_loss': 0.7014500498771667, '_step': 1039, 'epoch': 9, 'train/epoch_loss': 0.649790015355375}","{'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
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2,"{'test/recall': 0.7837837837837838, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.935483870967742, 'train/batch_loss': 0.01956617273390293, '_step': 1039, 'epoch': 9, '_wandb': {'runtime': 333}, '_runtime': 336.8275649547577, 'train/epoch_loss': 0.01614290558709019, '_timestamp': 1680692234.39516, 'test/f1-score': 0.8529411764705881, 'test/epoch_loss': 0.34812947780333664, 'train/epoch_acc': 0.9987714987714988}","{'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
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3,"{'test/epoch_acc': 0.8, 'train/batch_loss': 0.5222326517105103, 'train/epoch_loss': 0.5324229019572753, 'epoch': 9, '_wandb': {'runtime': 327}, '_runtime': 331.57809829711914, 'test/f1-score': 0.7954545454545455, 'test/epoch_loss': 0.5553177932898203, 'train/epoch_acc': 0.8353808353808354, '_step': 529, '_timestamp': 1680691883.3877182, 'test/recall': 0.8333333333333334, 'test/precision': 0.7608695652173914}","{'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
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4,"{'test/f1-score': 0.7076923076923076, 'train/epoch_acc': 0.5577395577395577, '_step': 410, 'epoch': 1, 'test/recall': 0.8846153846153846, 'test/epoch_acc': 0.5777777777777778, 'test/precision': 0.5897435897435898, 'test/epoch_loss': 1.5602711306677923, 'train/batch_loss': 0.5083656311035156, 'train/epoch_loss': 0.7508098256090057, '_wandb': {'runtime': 70}, '_runtime': 71.64615154266357, '_timestamp': 1680691538.7247725}","{'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
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5,"{'test/precision': 0.6885245901639344, 'test/epoch_loss': 0.4844042791260613, 'train/epoch_loss': 0.49390909720111537, '_step': 529, 'epoch': 9, '_timestamp': 1680691453.5148375, 'test/f1-score': 0.8, 'test/epoch_acc': 0.7666666666666667, 'train/epoch_acc': 0.769041769041769, 'train/batch_loss': 0.4559023082256317, '_wandb': {'runtime': 328}, '_runtime': 331.44886469841003, 'test/recall': 0.9545454545454546}","{'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
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6,"{'test/epoch_loss': 0.26263883135527266, 'train/epoch_acc': 0.9975429975429976, 'train/batch_loss': 0.0031523401848971844, 'train/epoch_loss': 0.018423480946079804, '_wandb': {'runtime': 355}, '_runtime': 358.66950702667236, '_timestamp': 1680691110.042932, 'test/recall': 0.8867924528301887, 'test/f1-score': 0.9306930693069309, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 0.9791666666666666, '_step': 2059, 'epoch': 9}","{'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
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7,"{'train/batch_loss': 0.003317732596769929, 'epoch': 9, '_wandb': {'runtime': 329}, '_runtime': 332.6156196594238, 'test/f1-score': 0.8865979381443299, 'test/epoch_loss': 0.3669874522421095, 'train/epoch_acc': 1, 'train/epoch_loss': 0.0014873178028192654, '_step': 279, '_timestamp': 1680690741.3215847, 'test/recall': 0.9148936170212766, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.86}","{'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
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8,"{'test/recall': 0.82, 'test/precision': 0.7592592592592593, 'test/epoch_loss': 0.5786970999505785, 'train/epoch_acc': 0.8206388206388207, '_step': 149, 'epoch': 9, '_runtime': 342.05230498313904, 'test/epoch_acc': 0.7555555555555555, 'train/batch_loss': 0.58731609582901, 'train/epoch_loss': 0.5623220165765842, '_wandb': {'runtime': 338}, '_timestamp': 1680690397.165603, 'test/f1-score': 0.7884615384615384}","{'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
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9,"{'test/precision': 0.8536585365853658, 'test/epoch_loss': 0.6037532766660054, 'train/epoch_acc': 0.7788697788697788, 'epoch': 9, '_wandb': {'runtime': 357}, '_runtime': 360.5366156101227, 'test/f1-score': 0.7865168539325843, 'test/epoch_acc': 0.788888888888889, 'train/batch_loss': 0.5736206769943237, '_step': 2059, '_timestamp': 1680690042.488695, 'test/recall': 0.7291666666666666, 'train/epoch_loss': 0.5984062318134074}","{'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
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10,"{'_wandb': {'runtime': 362}, '_runtime': 365.3367943763733, '_timestamp': 1680689670.8310964, 'test/f1-score': 0.8333333333333334, 'test/precision': 0.945945945945946, 'train/epoch_loss': 0.3086323318522451, '_step': 2059, 'epoch': 9, 'test/recall': 0.7446808510638298, 'test/epoch_acc': 0.8444444444444444, 'test/epoch_loss': 0.3740654948684904, 'train/epoch_acc': 0.8697788697788698, '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
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11,"{'train/epoch_acc': 1, 'train/batch_loss': 0.004256190732121468, '_step': 149, '_runtime': 340.39124369621277, '_timestamp': 1680689237.7951498, 'test/precision': 0.9069767441860463, 'test/epoch_loss': 0.18080708616309696, 'train/epoch_loss': 0.0053219743558098115, 'epoch': 9, '_wandb': {'runtime': 337}, 'test/recall': 0.9285714285714286, 'test/f1-score': 0.9176470588235294, 'test/epoch_acc': 0.9222222222222224}","{'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
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12,"{'_step': 2059, '_runtime': 359.0396990776062, '_timestamp': 1680688886.363035, 'test/recall': 0.8222222222222222, 'test/f1-score': 0.8705882352941177, 'test/precision': 0.925, 'train/batch_loss': 0.21692615747451785, 'epoch': 9, '_wandb': {'runtime': 356}, 'test/epoch_acc': 0.8777777777777778, 'test/epoch_loss': 0.23811448697621623, 'train/epoch_acc': 0.968058968058968, 'train/epoch_loss': 0.09628425111664636}","{'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
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13,"{'epoch': 9, '_runtime': 336.5640392303467, '_timestamp': 1680688517.0028613, 'test/recall': 0.9, 'test/precision': 0.9574468085106383, 'train/epoch_acc': 1, 'train/batch_loss': 0.007201554253697395, 'train/epoch_loss': 0.007631345846546077, '_step': 149, '_wandb': {'runtime': 333}, 'test/f1-score': 0.9278350515463918, 'test/epoch_acc': 0.9222222222222224, 'test/epoch_loss': 0.16714997291564945}","{'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
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14,"{'test/f1-score': 0.8674698795180724, 'test/precision': 0.9230769230769232, 'train/batch_loss': 0.27152174711227417, '_step': 529, 'epoch': 9, '_wandb': {'runtime': 328}, 'test/epoch_acc': 0.8777777777777778, 'test/epoch_loss': 0.32556109494633145, 'train/epoch_acc': 0.9496314496314496, 'train/epoch_loss': 0.17368088453934877, '_runtime': 331.98337984085083, '_timestamp': 1680688162.2054858, 'test/recall': 0.8181818181818182}","{'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
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15,"{'test/recall': 0.8292682926829268, 'test/epoch_acc': 0.7222222222222222, 'test/epoch_loss': 0.5193446947468652, 'train/batch_loss': 0.3307788372039795, '_wandb': {'runtime': 332}, '_timestamp': 1680687816.5057352, '_runtime': 335.6552822589874, 'test/f1-score': 0.7311827956989247, 'test/precision': 0.6538461538461539, 'train/epoch_acc': 0.7469287469287469, 'train/epoch_loss': 0.5277571982775039, '_step': 1039, 'epoch': 9}","{'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
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16,"{'test/precision': 0.8085106382978723, 'epoch': 9, '_wandb': {'runtime': 334}, '_runtime': 336.80703043937683, 'test/recall': 0.9047619047619048, 'test/f1-score': 0.853932584269663, 'test/epoch_acc': 0.8555555555555556, '_step': 149, '_timestamp': 1680687470.9289024, 'test/epoch_loss': 0.4616309046745301, 'train/epoch_acc': 1, 'train/batch_loss': 0.0030224076472222805, 'train/epoch_loss': 0.003708146820279612}","{'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
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17,"{'_step': 422, 'epoch': 7, '_runtime': 265.48077392578125, '_timestamp': 1680687113.1220188, 'test/recall': 0.08888888888888889, 'test/f1-score': 0.14035087719298245, 'test/precision': 0.3333333333333333, 'test/epoch_loss': 11610.708938450283, 'train/batch_loss': 9.74098777770996, '_wandb': {'runtime': 265}, 'test/epoch_acc': 0.45555555555555555, 'train/epoch_acc': 0.5331695331695332, 'train/epoch_loss': 9.16968992828444}","{'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
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18,"{'test/recall': 0.803921568627451, 'test/f1-score': 0.845360824742268, 'test/epoch_acc': 0.8333333333333334, 'test/precision': 0.8913043478260869, 'test/epoch_loss': 0.3831123087141249, '_step': 529, '_runtime': 330.36346793174744, '_timestamp': 1680686834.80723, 'train/batch_loss': 0.34334877133369446, 'train/epoch_loss': 0.3055295220024756, 'epoch': 9, '_wandb': {'runtime': 327}, 'train/epoch_acc': 0.8955773955773956}","{'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
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19,"{'train/epoch_acc': 0.484029484029484, 'train/epoch_loss': 'NaN', 'epoch': 2, '_wandb': {'runtime': 99}, '_runtime': 99.40804982185364, '_timestamp': 1680686491.634724, 'test/recall': 1, 'test/f1-score': 0.6259541984732825, '_step': 157, 'test/epoch_acc': 0.45555555555555555, 'test/precision': 0.45555555555555555, 'test/epoch_loss': 6.554853016439314e+29, 'train/batch_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
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20,"{'_step': 279, '_timestamp': 1680686383.3591404, 'test/f1-score': 0.8695652173913044, 'test/epoch_acc': 0.8666666666666667, 'test/precision': 0.851063829787234, 'train/batch_loss': 0.3707323968410492, 'epoch': 9, '_wandb': {'runtime': 334}, '_runtime': 337.17863941192627, 'test/recall': 0.8888888888888888, 'test/epoch_loss': 0.35141510632303025, 'train/epoch_acc': 0.9103194103194104, 'train/epoch_loss': 0.3219767680771521}","{'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
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21,"{'test/f1-score': 0.6601941747572815, 'test/epoch_acc': 0.6111111111111112, 'test/precision': 0.6296296296296297, 'train/batch_loss': 0.7027227878570557, 'train/epoch_acc': 0.5196560196560196, '_step': 149, 'epoch': 9, '_wandb': {'runtime': 342}, '_runtime': 344.80718994140625, '_timestamp': 1680686028.304971, 'test/recall': 0.6938775510204082, '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
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22,"{'_step': 529, '_wandb': {'runtime': 331}, '_runtime': 333.9663326740265, '_timestamp': 1680685671.7387648, 'test/f1-score': 0.9066666666666668, 'test/epoch_acc': 0.9222222222222224, 'test/precision': 0.9444444444444444, 'train/epoch_acc': 0.9864864864864864, 'train/batch_loss': 0.15035715699195862, 'train/epoch_loss': 0.10497688309859292, 'epoch': 9, 'test/recall': 0.8717948717948718, '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
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23,"{'_step': 149, 'test/recall': 0.925, 'test/f1-score': 0.6379310344827587, 'train/epoch_loss': 0.6564877619028677, 'test/epoch_loss': 0.6597137530644734, 'train/epoch_acc': 0.5909090909090909, 'epoch': 9, '_wandb': {'runtime': 333}, '_runtime': 335.79468297958374, '_timestamp': 1680685319.453976, 'test/epoch_acc': 0.5333333333333333, 'test/precision': 0.4868421052631579, 'train/batch_loss': 0.652446985244751}","{'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
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24,"{'test/epoch_acc': 0.7444444444444445, 'test/precision': 0.6271186440677966, 'test/epoch_loss': 0.5467572536733415, '_step': 529, 'epoch': 9, '_wandb': {'runtime': 329}, '_runtime': 331.50625491142273, 'test/f1-score': 0.7628865979381443, '_timestamp': 1680684975.004809, 'test/recall': 0.9736842105263158, 'train/epoch_acc': 0.7899262899262899, 'train/batch_loss': 0.5583129525184631, 'train/epoch_loss': 0.4703364581675143}","{'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
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25,"{'test/epoch_acc': 0.9, 'train/epoch_acc': 0.9987714987714988, '_step': 529, 'epoch': 9, '_wandb': {'runtime': 447}, '_runtime': 450.5545320510864, 'test/recall': 0.8863636363636364, 'train/epoch_loss': 0.007131033717467008, '_timestamp': 1680684633.811369, 'test/f1-score': 0.896551724137931, 'test/precision': 0.9069767441860463, 'test/epoch_loss': 0.30911533037821454, 'train/batch_loss': 0.005764181260019541}","{'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
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26,"{'_wandb': {'runtime': 83}, '_timestamp': 1680629962.8990817, 'train/epoch_acc': 0.8931203931203932, 'train/epoch_loss': 0.2428556958016658, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.8444444444444444, 'test/epoch_loss': 0.29840316110187104, '_step': 239, 'epoch': 1, '_runtime': 83.58446168899536, 'test/recall': 0.9047619047619048, 'test/f1-score': 0.8735632183908046, 'train/batch_loss': 0.08615076541900635}","{'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
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27,"{'epoch': 9, '_wandb': {'runtime': 347}, '_runtime': 348.9410927295685, '_timestamp': 1680629872.8401277, 'test/recall': 0.975, 'test/f1-score': 0.951219512195122, 'test/epoch_acc': 0.9555555555555556, '_step': 149, 'train/batch_loss': 0.10338585078716278, 'train/epoch_loss': 0.1163152276517718, 'train/epoch_acc': 0.9803439803439804, 'test/epoch_loss': 0.20102048052681817, 'test/precision': 0.9285714285714286}","{'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
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28,"{'train/batch_loss': 82027960, '_step': 210, 'epoch': 3, '_wandb': {'runtime': 135}, '_runtime': 132.22715950012207, '_timestamp': 1680629513.1781075, 'test/f1-score': 0.6721311475409836, 'test/epoch_acc': 0.5555555555555556, 'test/recall': 0.9111111111111112, 'test/precision': 0.5324675324675324, 'test/epoch_loss': 3.395405118153546e+20, 'train/epoch_acc': 0.5282555282555282, 'train/epoch_loss': 60563307.6520902}","{'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
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29,"{'_wandb': {'runtime': 326}, 'test/recall': 0.8888888888888888, 'test/epoch_acc': 0.6333333333333333, 'test/precision': 0.5245901639344263, 'train/batch_loss': 0.5836847424507141, 'train/epoch_loss': 0.6072891213970044, '_step': 279, 'epoch': 9, 'test/f1-score': 0.6597938144329897, 'test/epoch_loss': 0.6240786300765143, 'train/epoch_acc': 0.7469287469287469, '_runtime': 327.2181556224823, '_timestamp': 1680629374.0562296}","{'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
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121,"{'train/batch_loss': 0.6510805487632751, '_step': 1039, 'epoch': 9, '_wandb': {'runtime': 469}, 'test/f1-score': 0.7608695652173914, 'test/epoch_loss': 0.6144020875295003, 'train/epoch_acc': 0.7542997542997543, '_runtime': 469.65283608436584, '_timestamp': 1678739200.083605, 'test/recall': 0.875, 'test/epoch_acc': 0.7555555555555555, 'test/precision': 0.6730769230769231, 'train/epoch_loss': 0.6267796501480684}","{'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
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126,"{'train/batch_loss': 0.6653294563293457, '_wandb': {'runtime': 560}, 'test/recall': 0.9090909090909092, 'test/f1-score': 0.8602150537634408, 'test/precision': 0.8163265306122449, 'train/epoch_acc': 0.7567567567567567, 'test/epoch_loss': 0.6165981186760796, 'train/epoch_loss': 0.6107166709712448, '_step': 2059, 'epoch': 9, '_runtime': 560.7235152721405, '_timestamp': 1678738182.1088202, 'test/epoch_acc': 0.8555555555555556}","{'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
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129,"{'train/epoch_acc': 0.687960687960688, 'train/epoch_loss': 0.5984233345387902, '_runtime': 564.230875492096, '_timestamp': 1678733784.6976814, 'test/epoch_acc': 0.7111111111111111, 'test/epoch_loss': 0.5302444166607327, 'epoch': 9, '_wandb': {'runtime': 563}, 'test/recall': 0.7674418604651163, '_step': 2289, 'train/batch_loss': 0.3260266184806824, 'test/f1-score': 0.7173913043478259, 'test/precision': 0.673469387755102, 'test/batch_loss': 0.9658783674240112}","{'gamma': 0.1, 'epochs': 10, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01}",distinctive-sweep-9
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130,"{'train/batch_loss': 0.007875862531363964, 'train/epoch_loss': 0.1743801347293527, 'test/epoch_acc': 0.9333333333333332, 'test/precision': 1, 'test/batch_loss': 0.1419784128665924, '_step': 2289, '_runtime': 527.6160025596619, 'test/recall': 0.8636363636363636, 'test/f1-score': 0.9268292682926828, 'test/epoch_loss': 0.17092165086004468, 'train/epoch_acc': 0.9496314496314496, 'epoch': 9, '_wandb': {'runtime': 527}, '_timestamp': 1678733210.1129615}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.0003}",winter-sweep-8
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131,"{'epoch': 9, 'test/precision': 1, 'train/batch_loss': 0.04383014515042305, 'test/batch_loss': 0.27116066217422485, 'train/epoch_loss': 0.07730489082323246, 'test/epoch_acc': 0.9222222222222224, 'test/epoch_loss': 0.21558621691332924, 'train/epoch_acc': 0.9791154791154792, '_step': 1159, '_wandb': {'runtime': 452}, '_runtime': 453.52900218963623, '_timestamp': 1678732673.1225052, 'test/recall': 0.8292682926829268, 'test/f1-score': 0.9066666666666668}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.001}",stilted-sweep-7
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133,"{'_step': 1159, '_wandb': {'runtime': 453}, '_timestamp': 1678731639.156168, 'test/precision': 0.945945945945946, 'test/f1-score': 0.813953488372093, 'epoch': 9, '_runtime': 454.3645238876343, 'test/recall': 0.7142857142857143, 'test/epoch_acc': 0.8222222222222223, 'test/batch_loss': 0.5068956017494202, 'test/epoch_loss': 0.4936415394147237, 'train/epoch_loss': 0.5186349417126442, 'train/epoch_acc': 0.8218673218673218, 'train/batch_loss': 0.4434223175048828}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0001}",different-sweep-5
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134,"{'_step': 1159, '_wandb': {'runtime': 453}, '_runtime': 454.26038885116577, 'test/epoch_loss': 0.5482642173767089, 'test/precision': 0.825, 'test/batch_loss': 0.5159374475479126, 'train/epoch_acc': 0.812039312039312, 'train/batch_loss': 0.5655931830406189, 'test/f1-score': 0.8354430379746836, 'test/epoch_acc': 0.8555555555555556, 'train/epoch_loss': 0.5429200196149016, 'epoch': 9, '_timestamp': 1678731176.111379, 'test/recall': 0.8461538461538461}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0001}",wise-sweep-4
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135,"{'epoch': 9, '_wandb': {'runtime': 528}, 'test/recall': 0.775, 'test/epoch_acc': 0.8777777777777778, 'test/precision': 0.9393939393939394, 'test/batch_loss': 1.7588363885879517, 'train/epoch_loss': 0.02060394324720534, '_step': 2289, '_runtime': 528.9760706424713, 'test/f1-score': 0.8493150684931509, '_timestamp': 1678730714.7711067, 'train/epoch_acc': 0.9963144963144964, 'train/batch_loss': 0.00470334617421031, 'test/epoch_loss': 0.24194780117250048}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.003}",misty-sweep-3
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136,"{'test/f1-score': 0.7536231884057972, 'test/epoch_acc': 0.8111111111111111, '_step': 1159, '_wandb': {'runtime': 454}, 'test/batch_loss': 0.455120325088501, 'test/epoch_loss': 0.4792341656155056, 'train/batch_loss': 0.5347514748573303, 'epoch': 9, 'train/epoch_acc': 0.8329238329238329, 'test/recall': 0.6842105263157895, '_timestamp': 1678730177.1362092, 'test/precision': 0.8387096774193549, 'train/epoch_loss': 0.42904984072326735, '_runtime': 455.41485929489136}","{'gamma': 0.1, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0003}",unique-sweep-2
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137,"{'test/precision': 0.9047619047619048, 'train/epoch_acc': 0.9901719901719902, 'test/recall': 0.8636363636363636, 'test/epoch_acc': 0.888888888888889, 'test/batch_loss': 2.5320074558258057, 'test/epoch_loss': 0.5442472649919283, 'train/epoch_loss': 0.024021292951151657, '_wandb': {'runtime': 527}, 'test/f1-score': 0.8837209302325582, 'epoch': 9, '_runtime': 528.4356484413147, '_timestamp': 1678729705.2001765, 'train/batch_loss': 0.005740344058722258, '_step': 2289}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.003}",polar-sweep-1
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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}","{'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
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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}","{'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
|
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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}","{'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
|
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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}","{'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
|
||||
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}","{'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
|
||||
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}","{'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
|
||||
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}","{'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
|
||||
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}","{'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
|
||||
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}}","{'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
|
||||
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}","{'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
|
||||
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}","{'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
|
||||
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}","{'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
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|
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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}","{'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
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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}}","{'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
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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}","{'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
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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}","{'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
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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}","{'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
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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}","{'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
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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}","{'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
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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}","{'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
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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}","{'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
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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}","{'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
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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}","{'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, '_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}","{'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,"{'_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}","{'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,"{'_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}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.003}",super-sweep-10
|
||||
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}","{'gamma': 0.1, 'epochs': 10, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01}",distinctive-sweep-9
|
||||
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}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.0003}",winter-sweep-8
|
||||
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}","{'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/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}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'adam', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.01}",summer-sweep-6
|
||||
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}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0001}",different-sweep-5
|
||||
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}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 2, 'batch_size': 8, 'learning_rate': 0.0001}",wise-sweep-4
|
||||
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}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 4, 'learning_rate': 0.003}",misty-sweep-3
|
||||
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}","{'gamma': 0.1, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 3, 'batch_size': 8, 'learning_rate': 0.0003}",unique-sweep-2
|
||||
137,"{'epoch': 9, '_wandb': {'runtime': 527}, 'test/recall': 0.8636363636363636, 'test/batch_loss': 2.5320074558258057, 'train/epoch_acc': 0.9901719901719902, 'train/batch_loss': 0.005740344058722258, 'train/epoch_loss': 0.024021292951151657, '_step': 2289, 'test/epoch_acc': 0.888888888888889, 'test/precision': 0.9047619047619048, 'test/epoch_loss': 0.5442472649919283, '_runtime': 528.4356484413147, '_timestamp': 1678729705.2001765, 'test/f1-score': 0.8837209302325582}","{'gamma': 0.5, 'epochs': 10, 'optimizer': 'sgd', 'step_size': 7, 'batch_size': 4, 'learning_rate': 0.003}",polar-sweep-1
|
||||
|
||||
|
File diff suppressed because one or more lines are too long
@ -29,7 +29,16 @@
|
||||
"execution_count": 1,
|
||||
"id": "b88ce481",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/zenon/.local/share/miniconda3/lib/python3.7/site-packages/requests/__init__.py:104: RequestsDependencyWarning: urllib3 (1.26.13) or chardet (5.1.0)/charset_normalizer (2.0.4) doesn't match a supported version!\n",
|
||||
" RequestsDependencyWarning)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import torch\n",
|
||||
"import torch.nn as nn\n",
|
||||
@ -132,7 +141,7 @@
|
||||
"class_names = dataset.classes\n",
|
||||
"\n",
|
||||
"num_epochs = 50\n",
|
||||
"batch_size = 4"
|
||||
"batch_size = 64"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@ -95,9 +95,10 @@ def classify(resnet_path, img):
|
||||
batch = img.unsqueeze(0)
|
||||
|
||||
# Do inference
|
||||
providers = [('CUDAExecutionProvider', {
|
||||
"cudnn_conv_algo_search": "DEFAULT"
|
||||
}), 'CPUExecutionProvider']
|
||||
#providers = [('CUDAExecutionProvider',{
|
||||
# "cudnn_conv_algo_search": "DEFAULT"
|
||||
#}), 'CPUExecutionProvider']
|
||||
providers = ['CPUExecutionProvider']
|
||||
session = onnxruntime.InferenceSession(resnet_path, providers=providers)
|
||||
|
||||
outname = [i.name for i in session.get_outputs()]
|
||||
@ -184,9 +185,10 @@ def get_boxes(yolo_path, image):
|
||||
img['image'] = img['image'].unsqueeze(0)
|
||||
|
||||
# Do inference
|
||||
providers = [('CUDAExecutionProvider', {
|
||||
"cudnn_conv_algo_search": "DEFAULT"
|
||||
}), 'CPUExecutionProvider']
|
||||
#providers = [('CUDAExecutionProvider',{
|
||||
# "cudnn_conv_algo_search": "DEFAULT"
|
||||
#}), 'CPUExecutionProvider']
|
||||
providers = ['CPUExecutionProvider']
|
||||
session = onnxruntime.InferenceSession(yolo_path, providers=providers)
|
||||
|
||||
outname = [i.name for i in session.get_outputs()]
|
||||
@ -204,6 +206,7 @@ def get_boxes(yolo_path, image):
|
||||
|
||||
# Apply NMS to results
|
||||
preds_nms = apply_nms([outs])[0]
|
||||
#preds_nms = outs
|
||||
|
||||
# Convert boxes from resized img to original img
|
||||
xyxy_boxes = preds_nms[:, [1, 2, 3, 4]] # xmin, ymin, xmax, ymax
|
||||
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
Binary file not shown.
Binary file not shown.
@ -79,6 +79,8 @@
|
||||
\newacronym{resnet}{ResNet}{Residual Neural Network}
|
||||
\newacronym{cnn}{CNN}{Convolutional Neural Network}
|
||||
\newacronym{sgd}{SGD}{Stochastic Gradient Descent}
|
||||
\newacronym{roc}{ROC}{Receiver Operating Characteristic}
|
||||
\newacronym{auc}{AUC}{Area Under the Curve}
|
||||
|
||||
\begin{document}
|
||||
|
||||
@ -294,6 +296,135 @@ for the \emph{Plant} class.
|
||||
\label{fig:yolo-ap}
|
||||
\end{figure}
|
||||
|
||||
\subsection{Hyper-parameter Optimization}
|
||||
\label{ssec:yolo-hyp-opt}
|
||||
|
||||
To further improve the object detection performance, we perform
|
||||
hyper-parameter optimization using a genetic algorithm. Evolution of
|
||||
the hyper-parameters starts from the initial 30 default values
|
||||
provided by the authors of YOLO. Of those 30 values, 26 are allowed to
|
||||
mutate. During each generation, there is an 80\% chance that a
|
||||
mutation occurs with a variance of 0.04. To determine which generation
|
||||
should be the parent of the new mutation, all previous generations are
|
||||
ordered by fitness in decreasing order. At most five top generations
|
||||
are selected and one of them is chosen at random. Better generations
|
||||
have a higher chance of being selected as the selection is weighted by
|
||||
fitness. The parameters of that chosen generation are then mutated
|
||||
with the aforementioned probability and variance. Each generation is
|
||||
trained for three epochs and the fitness of the best epoch is
|
||||
recorded.
|
||||
|
||||
In total, we ran 87 iterations of which the 34\textsuperscript{th}
|
||||
generation provides the best fitness of 0.6076. Due to time
|
||||
constraints, it was not possible to train each generation for more
|
||||
epochs or to run more iterations in total. We assume that the
|
||||
performance of the first few epochs is a reasonable proxy for model
|
||||
performance overall. The optimized version of the object detection
|
||||
model is then trained for 70 epochs using the parameters of the
|
||||
34\textsuperscript{th} generation.
|
||||
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics{graphics/model_fitness_final.pdf}
|
||||
\caption[Optimized object detection fitness per epoch.]{Object
|
||||
detection model fitness for each epoch calculated as in
|
||||
equation~\ref{eq:fitness}. The vertical gray line at 27 marks the
|
||||
epoch with the highest fitness of 0.6172.}
|
||||
\label{fig:hyp-opt-fitness}
|
||||
\end{figure}
|
||||
|
||||
Figure~\ref{fig:hyp-opt-fitness} shows the model's fitness during
|
||||
training for each epoch. After the highest fitness of 0.6172 at epoch
|
||||
27, the performance quickly declines and shows that further training
|
||||
would likely not yield improved results. The model converges to its
|
||||
highest fitness much earlier than the non-optimized version discussed
|
||||
in section~\ref{ssec:yolo-training-phase}, which indicates that the
|
||||
adjusted parameters provide a better starting point in general.
|
||||
Furthermore, the maximum fitness is 0.74\% higher than in the
|
||||
non-optimized version.
|
||||
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics{graphics/precision_recall_final.pdf}
|
||||
\caption[Hyper-parameter optimized object detection precision and
|
||||
recall during training.]{Overall precision and recall during
|
||||
training for each epoch of the optimized model. The vertical gray
|
||||
line at 27 marks the epoch with the highest fitness.}
|
||||
\label{fig:hyp-opt-prec-rec}
|
||||
\end{figure}
|
||||
|
||||
Figure~\ref{fig:hyp-opt-prec-rec} shows precision and recall for the
|
||||
optimized model during training. Similarly to the non-optimized model
|
||||
from figure~\ref{fig:prec-rec}, both metrics do not change materially
|
||||
during training. Precision is slightly higher than in the
|
||||
non-optimized version and recall hovers at the same levels.
|
||||
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics{graphics/val_box_obj_loss_final.pdf}
|
||||
\caption[Hyper-parameter optimized object detection box and object
|
||||
loss.]{Box and object loss measured against the validation set of
|
||||
3091 images and 4092 ground truth labels. The class loss is
|
||||
omitted because there is only one class in the dataset and the
|
||||
loss is therefore always zero.}
|
||||
\label{fig:hyp-opt-box-obj-loss}
|
||||
\end{figure}
|
||||
|
||||
The box and object loss during training is pictured in
|
||||
figure~\ref{fig:hyp-opt-box-obj-loss}. Both losses start from a lower
|
||||
level which suggests that the initial optimized parameters allow the
|
||||
model to converge quicker. The object loss exhibits a similar slope to
|
||||
the non-optimized model in figure~\ref{fig:box-obj-loss}. The vertical
|
||||
gray line again marks epoch 27 with the highest fitness. The box loss
|
||||
reaches its lower limit at that point and the object loss starts to
|
||||
increase again after epoch 27.
|
||||
|
||||
\begin{table}[h]
|
||||
\centering
|
||||
\begin{tabular}{lrrrr}
|
||||
\toprule
|
||||
{} & Precision & Recall & F1-score & Support \\
|
||||
\midrule
|
||||
Plant & 0.633358 & 0.702811 & 0.666279 & 12238.0 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
\caption{Precision, recall and F1-score for the optimized object
|
||||
detection model.}
|
||||
\label{tab:yolo-metrics-hyp}
|
||||
\end{table}
|
||||
|
||||
Turning to the evaluation of the optimized model on the test dataset,
|
||||
table~\ref{tab:yolo-metrics-hyp} shows precision, recall and the
|
||||
F1-score for the optimized model. Comparing these metrics with the
|
||||
non-optimized version from table~\ref{tab:yolo-metrics}, precision is
|
||||
significantly higher by more than 8.5\%. Recall, however, is 3.5\%
|
||||
lower. The F1-score is higher by more than 3.7\% which indicates that
|
||||
the optimized model is better overall despite the lower recall. We
|
||||
feel that the lower recall value is a suitable trade off for the
|
||||
substantially higher precision considering that the non-optimized
|
||||
model's precision is quite low at 0.55.
|
||||
|
||||
The precision-recall curves in figure~\ref{fig:yolo-ap-hyp} for the
|
||||
optimized model show that the model draws looser bounding boxes than
|
||||
the optimized model. The \gls{ap} for both \gls{iou} thresholds of 0.5
|
||||
and 0.95 is lower indicating worse performance. It is likely that more
|
||||
iterations during evolution would help increase the \gls{ap} values as
|
||||
well. Even though the precision and recall values from
|
||||
table~\ref{tab:yolo-metrics-hyp} are better, the \textsf{mAP}@0.5:0.95
|
||||
is lower by 1.8\%.
|
||||
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics{graphics/APpt5-pt95-final.pdf}
|
||||
\caption[Hyper-parameter optimized object detection AP@0.5 and
|
||||
AP@0.95.]{Precision-recall curves for \gls{iou} thresholds of 0.5
|
||||
and 0.95. The \gls{ap} of a specific threshold is defined as the
|
||||
area under the precision-recall curve of that threshold. The
|
||||
\gls{map} across \gls{iou} thresholds from 0.5 to 0.95 in 0.05
|
||||
steps \textsf{mAP}@0.5:0.95 is 0.5546.}
|
||||
\label{fig:yolo-ap-hyp}
|
||||
\end{figure}
|
||||
|
||||
\section{Classification}
|
||||
\label{sec:resnet-eval}
|
||||
|
||||
@ -421,6 +552,89 @@ figure~\ref{fig:classifier-training-metrics}.
|
||||
\label{fig:resnet-hyp-results}
|
||||
\end{figure}
|
||||
|
||||
Table~\ref{tab:resnet-final-hyps} lists the final hyper-parameters
|
||||
which were chosen to train the improved model. In order to confirm
|
||||
that the model does not suffer from overfitting or is a product of
|
||||
chance due to a coincidentally advantageous train/test split, we
|
||||
perform stratified $10$-fold cross validation on the dataset. Each
|
||||
fold contains 90\% training and 10\% test data and was trained for 25
|
||||
epochs. Figure~\ref{fig:classifier-hyp-roc} shows the performance of
|
||||
the epoch with the highest F1-score of each fold as measured against
|
||||
the test split. The mean \gls{roc} curve provides a robust metric for
|
||||
a classifier's performance because it averages out the variability of
|
||||
the evaluation. Each fold manages to achieve at least an \gls{auc} of
|
||||
0.94, while the best fold reaches 0.98. The mean \gls{roc} has an
|
||||
\gls{auc} of 0.96 with a standard deviation of 0.02. These results
|
||||
indicate that the model is accurately predicting the correct class and
|
||||
is robust against variations in the training set.
|
||||
|
||||
\begin{table}
|
||||
\centering
|
||||
\begin{tabular}{cccc}
|
||||
\toprule
|
||||
Optimizer & Batch Size & Learning Rate & Step Size \\
|
||||
\midrule
|
||||
\gls{sgd} & 64 & 0.01 & 5\\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
\caption[Hyper-parameters for the optimized classifier.]{Chosen
|
||||
hyper-parameters for the final, improved model. The difference to
|
||||
the parameters listed in Table~\ref{tab:resnet-hyps} comes as a
|
||||
result of choosing \gls{sgd} over Adam. The missing four
|
||||
parameters are only required for Adam and not \gls{sgd}.}
|
||||
\label{tab:resnet-final-hyps}
|
||||
\end{table}
|
||||
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics{graphics/classifier-hyp-folds-roc.pdf}
|
||||
\caption[Mean \gls{roc} and variability of hyper-parameter-optimized
|
||||
model.]{This plot shows the \gls{roc} curve for the epoch with the
|
||||
highest F1-score of each fold as well as the \gls{auc}. To get a
|
||||
less variable performance metric of the classifier, the mean
|
||||
\gls{roc} curve is shown as a thick line and the variability is
|
||||
shown in gray. The overall mean \gls{auc} is 0.96 with a standard
|
||||
deviation of 0.02. The best-performing fold reaches an \gls{auc}
|
||||
of 0.99 and the worst an \gls{auc} of 0.94. The black dashed line
|
||||
indicates the performance of a classifier which picks classes at
|
||||
random ($\mathrm{\gls{auc}} = 0.5$). The shapes of the \gls{roc}
|
||||
curves show that the classifier performs well and is robust
|
||||
against variations in the training set.}
|
||||
\label{fig:classifier-hyp-roc}
|
||||
\end{figure}
|
||||
|
||||
The classifier shows good performance so far, but care has to be taken
|
||||
to not overfit the model to the training set. Comparing the F1-score
|
||||
during training with the F1-score during testing gives insight into
|
||||
when the model tries to increase its performance during training at
|
||||
the expense of generalizability. Figure~\ref{fig:classifier-hyp-folds}
|
||||
shows the F1-scores of each epoch and fold. The classifier converges
|
||||
quickly to 1 for the training set at which point it experiences a
|
||||
slight drop in generalizability. Training the model for at most five
|
||||
epochs is sufficient because there are generally no improvements
|
||||
afterwards. The best-performing epoch for each fold is between the
|
||||
second and fourth epoch which is just before the model achieves an
|
||||
F1-score of 1 on the training set.
|
||||
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[width=.9\textwidth]{graphics/classifier-hyp-folds-f1.pdf}
|
||||
\caption[F1-score of stratified $10$-fold cross validation.]{These
|
||||
plots show the F1-score during training as well as testing for
|
||||
each of the folds. The classifier converges to 1 by the third
|
||||
epoch during the training phase, which might indicate
|
||||
overfitting. However, the performance during testing increases
|
||||
until epoch three in most cases and then stabilizes at
|
||||
approximately 2-3\% lower than the best epoch. We believe that the
|
||||
third, or in some cases fourth, epoch is detrimental to
|
||||
performance and results in overfitting, because the model achieves
|
||||
an F1-score of 1 for the training set, but that gain does not
|
||||
transfer to the test set. Early stopping during training
|
||||
alleviates this problem.}
|
||||
\label{fig:classifier-hyp-folds}
|
||||
\end{figure}
|
||||
|
||||
|
||||
\subsection{Class Activation Maps}
|
||||
\label{ssec:resnet-cam}
|
||||
|
||||
@ -438,7 +652,7 @@ One such method, \gls{cam}~\cite{zhou2015}, is a popular tool to
|
||||
produce visual explanations for decisions made by
|
||||
\glspl{cnn}. Convolutional layers essentially function as object
|
||||
detectors as long as no fully-connected layers perform the
|
||||
classification. This ability to localize regions of interest which
|
||||
classification. This ability to localize regions of interest, which
|
||||
play a significant role in the type of class the model predicts, can
|
||||
be retained until the last layer and used to generate activation maps
|
||||
for the predictions.
|
||||
@ -567,10 +781,95 @@ the cutoff for either class.
|
||||
\label{fig:aggregate-ap}
|
||||
\end{figure}
|
||||
|
||||
Overall, we believe that the aggregate model shows sufficient
|
||||
predictive performance to be deployed in the field. The detections are
|
||||
accurate, especially for potted plants, and the classification into
|
||||
healthy and stressed is robust.
|
||||
\subsection{Hyper-parameter Optimization}
|
||||
\label{ssec:model-hyp-opt}
|
||||
|
||||
So far the metrics shown in table~\ref{tab:model-metrics} are obtained
|
||||
with the non-optimized versions of both the object detection and
|
||||
classification model. Hyper-parameter optimization of the classifier
|
||||
led to significant model improvements, while the object detector has
|
||||
improved precision but lower recall and slightly lower \gls{map}
|
||||
values. To evaluate the final aggregate model which consists of the
|
||||
individual optimized models, we run the same test as in
|
||||
section~\ref{sec:aggregate-model}.
|
||||
|
||||
\begin{table}
|
||||
\centering
|
||||
\begin{tabular}{lrrrr}
|
||||
\toprule
|
||||
{} & precision & recall & f1-score & support \\
|
||||
\midrule
|
||||
Healthy & 0.664 & 0.640 & 0.652 & 662.0 \\
|
||||
Stressed & 0.680 & 0.539 & 0.601 & 488.0 \\
|
||||
micro avg & 0.670 & 0.597 & 0.631 & 1150.0 \\
|
||||
macro avg & 0.672 & 0.590 & 0.626 & 1150.0 \\
|
||||
weighted avg & 0.670 & 0.597 & 0.630 & 1150.0 \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
\caption{Precision, recall and F1-score for the optimized aggregate
|
||||
model.}
|
||||
\label{tab:model-metrics-hyp}
|
||||
\end{table}
|
||||
|
||||
Table~\ref{tab:model-metrics-hyp} shows precision, recall and F1-score
|
||||
for the optimized model on the same test dataset of 640 images. All of
|
||||
the metrics are significantly worse than for the non-optimized
|
||||
model. Considering that the optimized classifier performs better than
|
||||
the non-optimized version this is a surprising result. There are
|
||||
multiple possible explanations for this behavior:
|
||||
|
||||
\begin{enumerate}
|
||||
\item The optimized classifier has worse generalizability than the
|
||||
non-optimized version.
|
||||
\item The small difference in the \gls{map} values for the object
|
||||
detection model result in significantly higher error rates
|
||||
overall. This might be the case because a large number of plants is
|
||||
not detected in the first place and/or those which are detected are
|
||||
more often not classified correctly by the classifier. As mentioned
|
||||
in section~\ref{ssec:yolo-hyp-opt}, running the evolution of the
|
||||
hyper-parameters for more generations could better the performance
|
||||
overall.
|
||||
\item The test dataset is tailored to the non-optimized version and
|
||||
does not provide an accurate measure of real-world performance. The
|
||||
test dataset was labeled by running the individual models on the
|
||||
images and taking the predicted bounding boxes and labels as a
|
||||
starting point for the labeling process. If the labels were not
|
||||
rigorously corrected, the dataset will allow the non-optimized model
|
||||
to achieve high scores because the labels are already in line with
|
||||
what it predicts. Conversely, the optimized model might get closer
|
||||
to the actual ground truth, but that truth is not what is specified
|
||||
by the labels to begin with. If that is the case, the evaluation of
|
||||
the non-optimized model is too favorably and should be corrected
|
||||
down.
|
||||
\end{enumerate}
|
||||
|
||||
Of these three possibilities, the second and third points are the most
|
||||
likely culprits. The first scenario is unlikely because the optimized
|
||||
classifier has been evaluated in a cross validation setting and the
|
||||
results do not lend themselves easily to such an
|
||||
interpretation. Dealing with the second scenario could allow the
|
||||
object detection model to perform better on its own, but would
|
||||
probably not explain the big difference in performance. Scenario three
|
||||
is the most likely one because the process of creating the test
|
||||
dataset can lead to favorable labels for the non-optimized model.
|
||||
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics{graphics/APmodel-final.pdf}
|
||||
\caption[Optimized aggregate model AP@0.5 and
|
||||
AP@0.95.]{Precision-recall curves for \gls{iou} thresholds of 0.5
|
||||
and 0.95. The \gls{ap} of a specific threshold is defined as the
|
||||
area under the precision-recall curve of that threshold. The
|
||||
\gls{map} across \gls{iou} thresholds from 0.5 to 0.95 in 0.05
|
||||
steps \textsf{mAP}@0.5:0.95 is 0.4426.}
|
||||
\label{fig:aggregate-ap-hyp}
|
||||
\end{figure}
|
||||
|
||||
Figure~\ref{fig:aggregate-ap-hyp} confirms the suspicions raised by
|
||||
the lower metrics from table~\ref{tab:model-metrics-hyp}. More
|
||||
iterations for the evolution of the object detection model would
|
||||
likely have a significant effect on \gls{iou} and the confidence
|
||||
values associated with the bounding boxes.
|
||||
|
||||
\backmatter
|
||||
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user