diff --git a/classification/classifier/hyp-metrics.csv b/classification/classifier/hyp-metrics.csv
index 60c41c8..a3c99e3 100644
--- a/classification/classifier/hyp-metrics.csv
+++ b/classification/classifier/hyp-metrics.csv
@@ -1,139 +1,139 @@
 ,summary,config,name
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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
diff --git a/classification/classifier/hyp-metrics.ipynb b/classification/classifier/hyp-metrics.ipynb
index 45f1c0a..6579501 100644
--- a/classification/classifier/hyp-metrics.ipynb
+++ b/classification/classifier/hyp-metrics.ipynb
@@ -99,14 +99,14 @@
        "      <th></th>\n",
        "      <th>Unnamed: 0</th>\n",
        "      <th>name</th>\n",
+       "      <th>test/epoch_acc</th>\n",
+       "      <th>test/precision</th>\n",
        "      <th>test/epoch_loss</th>\n",
        "      <th>train/epoch_acc</th>\n",
-       "      <th>train/batch_loss</th>\n",
+       "      <th>_step</th>\n",
        "      <th>epoch</th>\n",
        "      <th>_timestamp</th>\n",
-       "      <th>test/recall</th>\n",
-       "      <th>test/precision</th>\n",
-       "      <th>_step</th>\n",
+       "      <th>test/f1-score</th>\n",
        "      <th>...</th>\n",
        "      <th>test/batch_loss</th>\n",
        "      <th>eps</th>\n",
@@ -125,14 +125,14 @@
        "      <th>0</th>\n",
        "      <td>0</td>\n",
        "      <td>fiery-sweep-26</td>\n",
+       "      <td>0.733333</td>\n",
+       "      <td>0.828571</td>\n",
        "      <td>0.566462</td>\n",
        "      <td>0.823096</td>\n",
-       "      <td>0.335779</td>\n",
+       "      <td>2059</td>\n",
        "      <td>9</td>\n",
        "      <td>1.680693e+09</td>\n",
-       "      <td>0.617021</td>\n",
-       "      <td>0.828571</td>\n",
-       "      <td>2059</td>\n",
+       "      <td>0.707317</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1.000000e-01</td>\n",
@@ -149,14 +149,14 @@
        "      <th>1</th>\n",
        "      <td>1</td>\n",
        "      <td>radiant-sweep-25</td>\n",
+       "      <td>0.722222</td>\n",
+       "      <td>0.685185</td>\n",
        "      <td>0.645458</td>\n",
        "      <td>0.712531</td>\n",
-       "      <td>0.70145</td>\n",
+       "      <td>1039</td>\n",
        "      <td>9</td>\n",
        "      <td>1.680693e+09</td>\n",
-       "      <td>0.822222</td>\n",
-       "      <td>0.685185</td>\n",
-       "      <td>1039</td>\n",
+       "      <td>0.747475</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1.000000e+00</td>\n",
@@ -173,14 +173,14 @@
        "      <th>2</th>\n",
        "      <td>2</td>\n",
        "      <td>blooming-sweep-24</td>\n",
+       "      <td>0.888889</td>\n",
+       "      <td>0.935484</td>\n",
        "      <td>0.348129</td>\n",
        "      <td>0.998771</td>\n",
-       "      <td>0.019566</td>\n",
+       "      <td>1039</td>\n",
        "      <td>9</td>\n",
        "      <td>1.680692e+09</td>\n",
-       "      <td>0.783784</td>\n",
-       "      <td>0.935484</td>\n",
-       "      <td>1039</td>\n",
+       "      <td>0.852941</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1.000000e-08</td>\n",
@@ -197,14 +197,14 @@
        "      <th>3</th>\n",
        "      <td>3</td>\n",
        "      <td>visionary-sweep-23</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.760870</td>\n",
        "      <td>0.555318</td>\n",
        "      <td>0.835381</td>\n",
-       "      <td>0.522233</td>\n",
+       "      <td>529</td>\n",
        "      <td>9</td>\n",
        "      <td>1.680692e+09</td>\n",
-       "      <td>0.833333</td>\n",
-       "      <td>0.760870</td>\n",
-       "      <td>529</td>\n",
+       "      <td>0.795455</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1.000000e+00</td>\n",
@@ -221,14 +221,14 @@
        "      <th>4</th>\n",
        "      <td>4</td>\n",
        "      <td>ancient-sweep-22</td>\n",
+       "      <td>0.577778</td>\n",
+       "      <td>0.589744</td>\n",
        "      <td>1.560271</td>\n",
        "      <td>0.557740</td>\n",
-       "      <td>0.508366</td>\n",
+       "      <td>410</td>\n",
        "      <td>1</td>\n",
        "      <td>1.680692e+09</td>\n",
-       "      <td>0.884615</td>\n",
-       "      <td>0.589744</td>\n",
-       "      <td>410</td>\n",
+       "      <td>0.707692</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1.000000e-08</td>\n",
@@ -269,14 +269,14 @@
        "      <th>133</th>\n",
        "      <td>133</td>\n",
        "      <td>different-sweep-5</td>\n",
+       "      <td>0.822222</td>\n",
+       "      <td>0.945946</td>\n",
        "      <td>0.493642</td>\n",
        "      <td>0.821867</td>\n",
-       "      <td>0.443422</td>\n",
+       "      <td>1159</td>\n",
        "      <td>9</td>\n",
        "      <td>1.678732e+09</td>\n",
-       "      <td>0.714286</td>\n",
-       "      <td>0.945946</td>\n",
-       "      <td>1159</td>\n",
+       "      <td>0.813953</td>\n",
        "      <td>...</td>\n",
        "      <td>0.506896</td>\n",
        "      <td>NaN</td>\n",
@@ -293,14 +293,14 @@
        "      <th>134</th>\n",
        "      <td>134</td>\n",
        "      <td>wise-sweep-4</td>\n",
+       "      <td>0.855556</td>\n",
+       "      <td>0.825000</td>\n",
        "      <td>0.548264</td>\n",
        "      <td>0.812039</td>\n",
-       "      <td>0.565593</td>\n",
+       "      <td>1159</td>\n",
        "      <td>9</td>\n",
        "      <td>1.678731e+09</td>\n",
-       "      <td>0.846154</td>\n",
-       "      <td>0.825000</td>\n",
-       "      <td>1159</td>\n",
+       "      <td>0.835443</td>\n",
        "      <td>...</td>\n",
        "      <td>0.515937</td>\n",
        "      <td>NaN</td>\n",
@@ -317,14 +317,14 @@
        "      <th>135</th>\n",
        "      <td>135</td>\n",
        "      <td>misty-sweep-3</td>\n",
+       "      <td>0.877778</td>\n",
+       "      <td>0.939394</td>\n",
        "      <td>0.241948</td>\n",
        "      <td>0.996314</td>\n",
-       "      <td>0.004703</td>\n",
+       "      <td>2289</td>\n",
        "      <td>9</td>\n",
        "      <td>1.678731e+09</td>\n",
-       "      <td>0.775000</td>\n",
-       "      <td>0.939394</td>\n",
-       "      <td>2289</td>\n",
+       "      <td>0.849315</td>\n",
        "      <td>...</td>\n",
        "      <td>1.758836</td>\n",
        "      <td>NaN</td>\n",
@@ -341,14 +341,14 @@
        "      <th>136</th>\n",
        "      <td>136</td>\n",
        "      <td>unique-sweep-2</td>\n",
+       "      <td>0.811111</td>\n",
+       "      <td>0.838710</td>\n",
        "      <td>0.479234</td>\n",
        "      <td>0.832924</td>\n",
-       "      <td>0.534751</td>\n",
+       "      <td>1159</td>\n",
        "      <td>9</td>\n",
        "      <td>1.678730e+09</td>\n",
-       "      <td>0.684211</td>\n",
-       "      <td>0.838710</td>\n",
-       "      <td>1159</td>\n",
+       "      <td>0.753623</td>\n",
        "      <td>...</td>\n",
        "      <td>0.455120</td>\n",
        "      <td>NaN</td>\n",
@@ -365,14 +365,14 @@
        "      <th>137</th>\n",
        "      <td>137</td>\n",
        "      <td>polar-sweep-1</td>\n",
+       "      <td>0.888889</td>\n",
+       "      <td>0.904762</td>\n",
        "      <td>0.544247</td>\n",
        "      <td>0.990172</td>\n",
-       "      <td>0.00574</td>\n",
+       "      <td>2289</td>\n",
        "      <td>9</td>\n",
        "      <td>1.678730e+09</td>\n",
-       "      <td>0.863636</td>\n",
-       "      <td>0.904762</td>\n",
-       "      <td>2289</td>\n",
+       "      <td>0.883721</td>\n",
        "      <td>...</td>\n",
        "      <td>2.532007</td>\n",
        "      <td>NaN</td>\n",
@@ -391,57 +391,57 @@
        "</div>"
       ],
       "text/plain": [
-       "     Unnamed: 0                name  test/epoch_loss  train/epoch_acc  \\\n",
-       "0             0      fiery-sweep-26         0.566462         0.823096   \n",
-       "1             1    radiant-sweep-25         0.645458         0.712531   \n",
-       "2             2   blooming-sweep-24         0.348129         0.998771   \n",
-       "3             3  visionary-sweep-23         0.555318         0.835381   \n",
-       "4             4    ancient-sweep-22         1.560271         0.557740   \n",
-       "..          ...                 ...              ...              ...   \n",
-       "133         133   different-sweep-5         0.493642         0.821867   \n",
-       "134         134        wise-sweep-4         0.548264         0.812039   \n",
-       "135         135       misty-sweep-3         0.241948         0.996314   \n",
-       "136         136      unique-sweep-2         0.479234         0.832924   \n",
-       "137         137       polar-sweep-1         0.544247         0.990172   \n",
+       "     Unnamed: 0                name  test/epoch_acc  test/precision  \\\n",
+       "0             0      fiery-sweep-26        0.733333        0.828571   \n",
+       "1             1    radiant-sweep-25        0.722222        0.685185   \n",
+       "2             2   blooming-sweep-24        0.888889        0.935484   \n",
+       "3             3  visionary-sweep-23        0.800000        0.760870   \n",
+       "4             4    ancient-sweep-22        0.577778        0.589744   \n",
+       "..          ...                 ...             ...             ...   \n",
+       "133         133   different-sweep-5        0.822222        0.945946   \n",
+       "134         134        wise-sweep-4        0.855556        0.825000   \n",
+       "135         135       misty-sweep-3        0.877778        0.939394   \n",
+       "136         136      unique-sweep-2        0.811111        0.838710   \n",
+       "137         137       polar-sweep-1        0.888889        0.904762   \n",
        "\n",
-       "    train/batch_loss  epoch    _timestamp  test/recall  test/precision  _step  \\\n",
-       "0           0.335779      9  1.680693e+09     0.617021        0.828571   2059   \n",
-       "1            0.70145      9  1.680693e+09     0.822222        0.685185   1039   \n",
-       "2           0.019566      9  1.680692e+09     0.783784        0.935484   1039   \n",
-       "3           0.522233      9  1.680692e+09     0.833333        0.760870    529   \n",
-       "4           0.508366      1  1.680692e+09     0.884615        0.589744    410   \n",
-       "..               ...    ...           ...          ...             ...    ...   \n",
-       "133         0.443422      9  1.678732e+09     0.714286        0.945946   1159   \n",
-       "134         0.565593      9  1.678731e+09     0.846154        0.825000   1159   \n",
-       "135         0.004703      9  1.678731e+09     0.775000        0.939394   2289   \n",
-       "136         0.534751      9  1.678730e+09     0.684211        0.838710   1159   \n",
-       "137          0.00574      9  1.678730e+09     0.863636        0.904762   2289   \n",
+       "     test/epoch_loss  train/epoch_acc  _step  epoch    _timestamp  \\\n",
+       "0           0.566462         0.823096   2059      9  1.680693e+09   \n",
+       "1           0.645458         0.712531   1039      9  1.680693e+09   \n",
+       "2           0.348129         0.998771   1039      9  1.680692e+09   \n",
+       "3           0.555318         0.835381    529      9  1.680692e+09   \n",
+       "4           1.560271         0.557740    410      1  1.680692e+09   \n",
+       "..               ...              ...    ...    ...           ...   \n",
+       "133         0.493642         0.821867   1159      9  1.678732e+09   \n",
+       "134         0.548264         0.812039   1159      9  1.678731e+09   \n",
+       "135         0.241948         0.996314   2289      9  1.678731e+09   \n",
+       "136         0.479234         0.832924   1159      9  1.678730e+09   \n",
+       "137         0.544247         0.990172   2289      9  1.678730e+09   \n",
        "\n",
-       "     ...  test/batch_loss           eps  gamma epochs  beta_one  beta_two  \\\n",
-       "0    ...              NaN  1.000000e-01    0.1     10      0.99     0.900   \n",
-       "1    ...              NaN  1.000000e+00    0.5     10      0.99     0.900   \n",
-       "2    ...              NaN  1.000000e-08    0.5     10      0.90     0.999   \n",
-       "3    ...              NaN  1.000000e+00    0.1     10      0.90     0.900   \n",
-       "4    ...              NaN  1.000000e-08    0.5     10      0.90     0.990   \n",
-       "..   ...              ...           ...    ...    ...       ...       ...   \n",
-       "133  ...         0.506896           NaN    0.5     10       NaN       NaN   \n",
-       "134  ...         0.515937           NaN    0.5     10       NaN       NaN   \n",
-       "135  ...         1.758836           NaN    0.5     10       NaN       NaN   \n",
-       "136  ...         0.455120           NaN    0.1     10       NaN       NaN   \n",
-       "137  ...         2.532007           NaN    0.5     10       NaN       NaN   \n",
+       "     test/f1-score  ... test/batch_loss           eps  gamma  epochs  \\\n",
+       "0         0.707317  ...             NaN  1.000000e-01    0.1      10   \n",
+       "1         0.747475  ...             NaN  1.000000e+00    0.5      10   \n",
+       "2         0.852941  ...             NaN  1.000000e-08    0.5      10   \n",
+       "3         0.795455  ...             NaN  1.000000e+00    0.1      10   \n",
+       "4         0.707692  ...             NaN  1.000000e-08    0.5      10   \n",
+       "..             ...  ...             ...           ...    ...     ...   \n",
+       "133       0.813953  ...        0.506896           NaN    0.5      10   \n",
+       "134       0.835443  ...        0.515937           NaN    0.5      10   \n",
+       "135       0.849315  ...        1.758836           NaN    0.5      10   \n",
+       "136       0.753623  ...        0.455120           NaN    0.1      10   \n",
+       "137       0.883721  ...        2.532007           NaN    0.5      10   \n",
        "\n",
-       "     optimizer  step_size  batch_size  learning_rate  \n",
-       "0         adam          3           4         0.0003  \n",
-       "1         adam          2           8         0.0003  \n",
-       "2          sgd          5           8         0.0030  \n",
-       "3          sgd          2          16         0.0003  \n",
-       "4         adam          7           4         0.0100  \n",
-       "..         ...        ...         ...            ...  \n",
-       "133        sgd          3           8         0.0001  \n",
-       "134        sgd          2           8         0.0001  \n",
-       "135        sgd          3           4         0.0030  \n",
-       "136        sgd          3           8         0.0003  \n",
-       "137        sgd          7           4         0.0030  \n",
+       "     beta_one  beta_two  optimizer  step_size  batch_size  learning_rate  \n",
+       "0        0.99     0.900       adam          3           4         0.0003  \n",
+       "1        0.99     0.900       adam          2           8         0.0003  \n",
+       "2        0.90     0.999        sgd          5           8         0.0030  \n",
+       "3        0.90     0.900        sgd          2          16         0.0003  \n",
+       "4        0.90     0.990       adam          7           4         0.0100  \n",
+       "..        ...       ...        ...        ...         ...            ...  \n",
+       "133       NaN       NaN        sgd          3           8         0.0001  \n",
+       "134       NaN       NaN        sgd          2           8         0.0001  \n",
+       "135       NaN       NaN        sgd          3           4         0.0030  \n",
+       "136       NaN       NaN        sgd          3           8         0.0003  \n",
+       "137       NaN       NaN        sgd          7           4         0.0030  \n",
        "\n",
        "[138 rows x 25 columns]"
       ]
@@ -517,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "id": "00efa25b",
    "metadata": {},
    "outputs": [
@@ -883,10 +883,72 @@
     "pd.DataFrame.from_dict(parameters_dict).explode('optimizer').explode('batch_size').explode('learning_rate').explode('step_size').explode('gamma').explode('beta_one').explode('beta_two').explode('eps')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0d01bf18",
+   "metadata": {},
+   "source": [
+    "# F1-score stratified 10-fold cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "bb567230",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_scores_test = pd.read_csv('f1-scores-folds.csv', delimiter=',')\n",
+    "f_scores_test['epoch'] = np.resize(np.arange(25), 10*25)\n",
+    "f_scores_test['fold'] = np.repeat(np.arange(10), 25)\n",
+    "f_scores_test = pd.melt(f_scores_test[['epoch', 'fold', 'StratifiedKFold-ROC - test/f1-score']], ['epoch', 'fold'])\n",
+    "\n",
+    "f_scores_train = pd.read_csv('f1-scores-folds-train.csv', delimiter=',')\n",
+    "f_scores_train['epoch'] = np.resize(np.arange(25), 10*25)\n",
+    "f_scores_train['fold'] = np.repeat(np.arange(10), 25)\n",
+    "f_scores_train = pd.melt(f_scores_train[['epoch', 'fold', 'StratifiedKFold-ROC - train/f1-score']], ['epoch', 'fold'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "493e415e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 578.387x714.925 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 1, figsize=set_size(width, subplots=(2,1)), sharex=True)\n",
+    "sns.lineplot(x=\"epoch\", y=\"value\",\n",
+    "                hue='fold',\n",
+    "                palette=sns.cubehelix_palette(10, light=0.8, gamma=1.2),\n",
+    "                linewidth=1,\n",
+    "                data=f_scores_train, ax=ax[0])\n",
+    "\n",
+    "sns.lineplot(x=\"epoch\", y=\"value\",\n",
+    "                hue='fold',\n",
+    "                palette=sns.cubehelix_palette(10, light=0.8, gamma=1.2),\n",
+    "                linewidth=1,\n",
+    "                data=f_scores_test, ax=ax[1])\n",
+    "ax[0].set_ylabel('train/f1-score')\n",
+    "ax[1].set_ylabel('test/f1-score')\n",
+    "fig.tight_layout()\n",
+    "fig.savefig(fig_save_dir + 'classifier-hyp-folds-f1.pdf', format='pdf', bbox_inches='tight')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9f163a6c",
+   "id": "2c642d40",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/classification/classifier/train.ipynb b/classification/classifier/train.ipynb
index eeb309c..0609af6 100644
--- a/classification/classifier/train.ipynb
+++ b/classification/classifier/train.ipynb
@@ -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"
    ]
   },
   {
diff --git a/classification/evaluation/detection.py b/classification/evaluation/detection.py
index e042ac9..251228d 100644
--- a/classification/evaluation/detection.py
+++ b/classification/evaluation/detection.py
@@ -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
diff --git a/classification/evaluation/eval-test-model.ipynb b/classification/evaluation/eval-test-model.ipynb
index dc55425..f3682f2 100644
--- a/classification/evaluation/eval-test-model.ipynb
+++ b/classification/evaluation/eval-test-model.ipynb
@@ -30,7 +30,16 @@
    "execution_count": 1,
    "id": "ff25695e",
    "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 fiftyone as fo\n",
     "from PIL import Image\n",
@@ -57,7 +66,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "name = \"dataset\"\n",
+    "name = \"dataset-new\"\n",
     "dataset_dir = \"dataset\"\n",
     "\n",
     "# The splits to load\n",
@@ -110,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 9,
    "id": "63f675ab",
    "metadata": {},
    "outputs": [
@@ -118,7 +127,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " 100% |█████████████████| 640/640 [5.8m elapsed, 0s remaining, 2.2 samples/s]      \n"
+      " 100% |█████████████████| 640/640 [8.7m elapsed, 0s remaining, 1.4 samples/s]      \n"
      ]
     }
    ],
@@ -128,7 +137,7 @@
     "    for sample in pb(dataset.view()):\n",
     "        image = Image.open(sample.filepath)\n",
     "        w, h = image.size\n",
-    "        pred = detect(sample.filepath, '../weights/yolo.onnx', '../weights/resnet.onnx')\n",
+    "        pred = detect(sample.filepath, '../weights/yolo-final.onnx', '../weights/resnet-fold-7.onnx')\n",
     "\n",
     "        detections = []\n",
     "        for _, row in pred.iterrows():\n",
@@ -142,7 +151,7 @@
     "                             bounding_box=rel_box,\n",
     "                             confidence=int(row['cls_conf'])))\n",
     "\n",
-    "        sample[\"predictions\"] = fo.Detections(detections=detections)\n",
+    "        sample[\"predictions_yolo_resnet_final\"] = fo.Detections(detections=detections)\n",
     "        sample.save()"
    ]
   },
@@ -158,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 3,
    "id": "68cfdad2",
    "metadata": {},
    "outputs": [
@@ -167,17 +176,17 @@
      "output_type": "stream",
      "text": [
       "Evaluating detections...\n",
-      " 100% |█████████████████| 640/640 [2.0s elapsed, 0s remaining, 314.2 samples/s]         \n",
+      " 100% |█████████████████| 640/640 [2.2s elapsed, 0s remaining, 278.4 samples/s]      \n",
       "Performing IoU sweep...\n",
-      " 100% |█████████████████| 640/640 [2.2s elapsed, 0s remaining, 285.3 samples/s]      \n"
+      " 100% |█████████████████| 640/640 [2.4s elapsed, 0s remaining, 270.2 samples/s]      \n"
      ]
     }
    ],
    "source": [
     "results = dataset.view().evaluate_detections(\n",
-    "    \"predictions\",\n",
+    "    \"predictions_yolo_resnet_final\",\n",
     "    gt_field=\"ground_truth\",\n",
-    "    eval_key=\"eval\",\n",
+    "    eval_key=\"eval_yolo_resnet_final\",\n",
     "    compute_mAP=True,\n",
     ")"
    ]
@@ -194,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 4,
    "id": "86b90e80",
    "metadata": {},
    "outputs": [],
@@ -207,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 5,
    "id": "e34a18f4",
    "metadata": {},
    "outputs": [],
@@ -230,7 +239,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 10,
+   "id": "b14d2b25",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{lrrrr}\n",
+      "\\toprule\n",
+      "{} &  precision &  recall &  f1-score &  support \\\\\n",
+      "\\midrule\n",
+      "Healthy      &      0.841 &   0.759 &     0.798 &    663.0 \\\\\n",
+      "Stressed     &      0.726 &   0.810 &     0.766 &    484.0 \\\\\n",
+      "micro avg    &      0.786 &   0.780 &     0.783 &   1147.0 \\\\\n",
+      "macro avg    &      0.784 &   0.784 &     0.782 &   1147.0 \\\\\n",
+      "weighted avg &      0.793 &   0.780 &     0.784 &   1147.0 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_df = pd.DataFrame(results.report()).transpose().round(3)\n",
+    "\n",
+    "# Export DataFrame to LaTeX tabular environment\n",
+    "print(results_df.to_latex())\n",
+    "# YOLO original with Resnet original and new dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "900e9014",
    "metadata": {},
    "outputs": [
@@ -242,14 +284,15 @@
       "\\toprule\n",
       "{} &  precision &  recall &  f1-score &  support \\\\\n",
       "\\midrule\n",
-      "Healthy      &      0.824 &   0.745 &     0.783 &    662.0 \\\\\n",
-      "Stressed     &      0.707 &   0.783 &     0.743 &    488.0 \\\\\n",
-      "micro avg    &      0.769 &   0.761 &     0.765 &   1150.0 \\\\\n",
-      "macro avg    &      0.766 &   0.764 &     0.763 &   1150.0 \\\\\n",
-      "weighted avg &      0.775 &   0.761 &     0.766 &   1150.0 \\\\\n",
+      "Healthy      &      0.674 &   0.721 &     0.696 &    662.0 \\\\\n",
+      "Stressed     &      0.616 &   0.543 &     0.577 &    488.0 \\\\\n",
+      "micro avg    &      0.652 &   0.645 &     0.649 &   1150.0 \\\\\n",
+      "macro avg    &      0.645 &   0.632 &     0.637 &   1150.0 \\\\\n",
+      "weighted avg &      0.649 &   0.645 &     0.646 &   1150.0 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
-      "\n"
+      "\n",
+      "0.49320073714096757\n"
      ]
     }
    ],
@@ -257,12 +300,14 @@
     "results_df = pd.DataFrame(results.report()).transpose().round(3)\n",
     "\n",
     "# Export DataFrame to LaTeX tabular environment\n",
-    "print(results_df.to_latex())"
+    "print(results_df.to_latex())\n",
+    "print(results.mAP())\n",
+    "# YOLO original and Resnet final with old dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 51,
    "id": "24df35b4",
    "metadata": {},
    "outputs": [
@@ -287,12 +332,58 @@
     "# Print a classification report for all classes\n",
     "results.print_report()\n",
     "\n",
-    "print(results.mAP())"
+    "print(results.mAP())\n",
+    "# YOLO original and Resnet original with old dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 8,
+   "id": "a6bb272a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     Healthy       0.66      0.64      0.65       662\n",
+      "    Stressed       0.68      0.54      0.60       488\n",
+      "\n",
+      "   micro avg       0.67      0.60      0.63      1150\n",
+      "   macro avg       0.67      0.59      0.63      1150\n",
+      "weighted avg       0.67      0.60      0.63      1150\n",
+      "\n",
+      "0.44258882390400406\n",
+      "\\begin{tabular}{lrrrr}\n",
+      "\\toprule\n",
+      "{} &  precision &  recall &  f1-score &  support \\\\\n",
+      "\\midrule\n",
+      "Healthy      &      0.664 &   0.640 &     0.652 &    662.0 \\\\\n",
+      "Stressed     &      0.680 &   0.539 &     0.601 &    488.0 \\\\\n",
+      "micro avg    &      0.670 &   0.597 &     0.631 &   1150.0 \\\\\n",
+      "macro avg    &      0.672 &   0.590 &     0.626 &   1150.0 \\\\\n",
+      "weighted avg &      0.670 &   0.597 &     0.630 &   1150.0 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print a classification report for all classes\n",
+    "results.print_report()\n",
+    "results_df = pd.DataFrame(results.report()).transpose().round(3)\n",
+    "\n",
+    "print(results.mAP())\n",
+    "print(results_df.to_latex())\n",
+    "# YOLO final and Resnet final with old dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "id": "da05e2ba",
    "metadata": {},
    "outputs": [
@@ -306,7 +397,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 578.387x178.731 with 2 Axes>"
       ]
@@ -324,10 +415,10 @@
     "# Set the labels for the legends manually\n",
     "ax[0].get_lines()[0].set_linestyle('dashed')\n",
     "ax[1].get_lines()[0].set_linestyle('dashed')\n",
-    "ax[0].legend(['AP: 0.66, Healthy', 'AP: 0.65, Stressed'], frameon=False)\n",
-    "ax[1].legend(['AP: 0.56, Healthy', 'AP: 0.53, Stressed'], frameon=False)\n",
+    "ax[0].legend(['AP: 0.52, Healthy', 'AP: 0.46, Stressed'], frameon=False)\n",
+    "ax[1].legend(['AP: 0.31, Healthy', 'AP: 0.29, Stressed'], frameon=False)\n",
     "fig.tight_layout()\n",
-    "fig.savefig(fig_save_dir + 'APmodel.pdf', format='pdf', bbox_inches='tight')"
+    "fig.savefig(fig_save_dir + 'APmodel-final.pdf', format='pdf', bbox_inches='tight')"
    ]
   },
   {
@@ -363,7 +454,7 @@
     "labels = ['Healthy', 'Stressed', '(none)']\n",
     "sns.heatmap(matrix, annot=True, xticklabels=labels, yticklabels=labels, fmt=\".0f\", cmap=sns.cubehelix_palette(as_cmap=True, start=.3, hue=1, light=.9))\n",
     "fig.tight_layout()\n",
-    "fig.savefig(fig_save_dir + 'CMmodel.pdf', format='pdf', bbox_inches='tight')"
+    "fig.savefig(fig_save_dir + 'CMmodel-final.pdf', format='pdf', bbox_inches='tight')"
    ]
   },
   {
@@ -378,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 5,
    "id": "bfb39b5d",
    "metadata": {},
    "outputs": [
diff --git a/classification/evaluation/eval-test-yolo.ipynb b/classification/evaluation/eval-test-yolo.ipynb
index 5b6bd87..47cc0ef 100644
--- a/classification/evaluation/eval-test-yolo.ipynb
+++ b/classification/evaluation/eval-test-yolo.ipynb
@@ -32,7 +32,16 @@
    "execution_count": 1,
    "id": "3fe8177c",
    "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 fiftyone as fo\n",
     "from PIL import Image\n",
@@ -62,10 +71,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "19c5b271",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading split 'test' to '/home/zenon/fiftyone/open-images-v6/test' if necessary\n",
+      "Necessary images already downloaded\n",
+      "Existing download of split 'test' is sufficient\n",
+      "Loading 'open-images-v6' split 'test'\n",
+      " 100% |█████████████| 12106/12106 [1.0m elapsed, 0s remaining, 209.3 samples/s]      \n",
+      "Dataset 'open-images-v6-test' created\n"
+     ]
+    }
+   ],
    "source": [
     "import fiftyone as fo\n",
     "import fiftyone.zoo as foz\n",
@@ -75,7 +97,7 @@
     "    classes=[\"Plant\", \"Houseplant\"],\n",
     "    label_types=[\"detections\"],\n",
     "    shuffle=True,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -90,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "ebdde519",
    "metadata": {},
    "outputs": [],
@@ -153,7 +175,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "yolo_dataset_dir = '/mnt/yolo-second-run/data'\n",
+    "yolo_dataset_dir = '/home/zenon/testdir'\n",
     "\n",
     "# The type of the dataset being imported\n",
     "dataset_type = fo.types.YOLOv5Dataset\n",
@@ -162,9 +184,9 @@
     "yolo_test = fo.Dataset.from_dir(\n",
     "    dataset_dir=yolo_dataset_dir,\n",
     "    dataset_type=dataset_type,\n",
-    "    split='test'\n",
+    "    split='val'\n",
     ")\n",
-    "yolo_test.name = 'yolo_test'\n",
+    "yolo_test.name = 'yolo_test4'\n",
     "yolo_test.persistent = True"
    ]
   },
@@ -178,17 +200,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 3,
    "id": "0b86639e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['dataset', 'dataset-small', 'yolo', 'yolo_test']"
+       "['dataset',\n",
+       " 'dataset-new',\n",
+       " 'open-images-v6-test',\n",
+       " 'plantsdata',\n",
+       " 'yolo',\n",
+       " 'yolo_test',\n",
+       " 'yolo_test2',\n",
+       " 'yolo_test3',\n",
+       " 'yolo_test4']"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -210,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 28,
    "id": "030e9c7c",
    "metadata": {},
    "outputs": [
@@ -218,7 +248,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " 100% |███████████████| 9184/9184 [56.3m elapsed, 0s remaining, 2.7 samples/s]      \n"
+      " 100% |███████████████| 9184/9184 [1.5h elapsed, 0s remaining, 1.6 samples/s]      \n"
      ]
     }
    ],
@@ -229,7 +259,7 @@
     "    for sample in pb(yolo_view):\n",
     "        image = Image.open(sample.filepath)\n",
     "        w, h = image.size\n",
-    "        pred = detect_yolo_only(sample.filepath, '../weights/yolo.onnx')\n",
+    "        pred = detect_yolo_only(sample.filepath, '../weights/yolo-final.onnx')\n",
     "\n",
     "        detections = []\n",
     "        for _, row in pred.iterrows():\n",
@@ -259,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "4aaa4577",
    "metadata": {},
    "outputs": [
@@ -268,9 +298,9 @@
      "output_type": "stream",
      "text": [
       "Evaluating detections...\n",
-      " 100% |███████████████| 9184/9184 [24.5s elapsed, 0s remaining, 341.6 samples/s]      \n",
+      " 100% |███████████████| 9184/9184 [23.3s elapsed, 0s remaining, 363.8 samples/s]      \n",
       "Performing IoU sweep...\n",
-      " 100% |███████████████| 9184/9184 [26.9s elapsed, 0s remaining, 301.2 samples/s]      \n"
+      " 100% |███████████████| 9184/9184 [25.3s elapsed, 0s remaining, 333.0 samples/s]      \n"
      ]
     }
    ],
@@ -290,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "59355da5",
    "metadata": {},
    "outputs": [],
@@ -303,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "0c8a3151",
    "metadata": {},
    "outputs": [],
@@ -324,6 +354,50 @@
     "The code for the LaTeX table of the classification report can be printed by first converting the results to a pandas DataFrame and then calling the `to_latex()` method of the DataFrame. This code can then be inserted into the LaTeX document."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f4ede94a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{lrrrr}\n",
+      "\\toprule\n",
+      "{} &  precision &    recall &  f1-score &  support \\\\\n",
+      "\\midrule\n",
+      "Plant        &   0.633358 &  0.702811 &  0.666279 &  12238.0 \\\\\n",
+      "micro avg    &   0.633358 &  0.702811 &  0.666279 &  12238.0 \\\\\n",
+      "macro avg    &   0.633358 &  0.702811 &  0.666279 &  12238.0 \\\\\n",
+      "weighted avg &   0.633358 &  0.702811 &  0.666279 &  12238.0 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       Plant       0.63      0.70      0.67     12238\n",
+      "\n",
+      "   micro avg       0.63      0.70      0.67     12238\n",
+      "   macro avg       0.63      0.70      0.67     12238\n",
+      "weighted avg       0.63      0.70      0.67     12238\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_df = pd.DataFrame(results.report()).transpose()\n",
+    "\n",
+    "# Results for hyper-optimized final YOLO model\n",
+    "\n",
+    "# Export DataFrame to LaTeX tabular environment\n",
+    "print(results_df.to_latex())\n",
+    "\n",
+    "# Print classification report\n",
+    "results.print_report()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 13,
@@ -348,6 +422,8 @@
    "source": [
     "results_df = pd.DataFrame(results.report()).transpose()\n",
     "\n",
+    "# Results for original YOLO model\n",
+    "\n",
     "# Export DataFrame to LaTeX tabular environment\n",
     "# print(results_df.to_latex())\n",
     "\n",
@@ -357,7 +433,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
+   "id": "ea4985d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       Plant       0.52      0.54      0.53     22535\n",
+      "\n",
+      "   micro avg       0.52      0.54      0.53     22535\n",
+      "   macro avg       0.52      0.54      0.53     22535\n",
+      "weighted avg       0.52      0.54      0.53     22535\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_df = pd.DataFrame(results.report()).transpose()\n",
+    "\n",
+    "# Export DataFrame to LaTeX tabular environment\n",
+    "# print(results_df.to_latex())\n",
+    "\n",
+    "# Print classification report\n",
+    "results.print_report()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "id": "a6e0e146",
    "metadata": {},
    "outputs": [
@@ -365,17 +472,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.5726538843333254\n"
+      "0.5545944356667605\n"
      ]
     }
    ],
    "source": [
+    "# Result of final optimized YOLO model\n",
     "print(results.mAP())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "98122829",
    "metadata": {},
    "outputs": [
@@ -389,7 +497,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 578.387x178.731 with 2 Axes>"
       ]
@@ -406,10 +514,10 @@
     "results.plot_pr_curves(iou_thresh=0.95, backend='matplotlib', ax=ax[1], color='black', linewidth=1)\n",
     "# Set the labels for the legends manually because\n",
     "# the default ones contain a line for the classes (irrelevant).\n",
-    "ax[0].legend(['AP = 0.66'], frameon=False)\n",
-    "ax[1].legend(['AP = 0.41'], frameon=False)\n",
+    "ax[0].legend(['AP = 0.64'], frameon=False)\n",
+    "ax[1].legend(['AP = 0.40'], frameon=False)\n",
     "fig.tight_layout()\n",
-    "fig.savefig(fig_save_dir + 'APpt5-pt95.pdf', format='pdf', bbox_inches='tight')"
+    "fig.savefig(fig_save_dir + 'APpt5-pt95-final.pdf', format='pdf', bbox_inches='tight')"
    ]
   },
   {
diff --git a/classification/evaluation/eval-train-yolo.ipynb b/classification/evaluation/eval-train-yolo.ipynb
index 7092e25..95f39c5 100644
--- a/classification/evaluation/eval-train-yolo.ipynb
+++ b/classification/evaluation/eval-train-yolo.ipynb
@@ -26,7 +26,16 @@
    "execution_count": 1,
    "id": "c0727442",
    "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 matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
@@ -94,103 +103,103 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0/299</td>\n",
-       "      <td>7.49G</td>\n",
-       "      <td>0.04468</td>\n",
-       "      <td>0.01796</td>\n",
+       "      <td>0/69</td>\n",
+       "      <td>7.28G</td>\n",
+       "      <td>0.02551</td>\n",
+       "      <td>0.011000</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.06264</td>\n",
-       "      <td>87</td>\n",
+       "      <td>0.03651</td>\n",
+       "      <td>12</td>\n",
        "      <td>640</td>\n",
-       "      <td>0.7777</td>\n",
-       "      <td>0.6004</td>\n",
-       "      <td>0.7016</td>\n",
-       "      <td>0.5741</td>\n",
-       "      <td>0.04719</td>\n",
-       "      <td>0.007429</td>\n",
+       "      <td>0.7350</td>\n",
+       "      <td>0.5716</td>\n",
+       "      <td>0.6676</td>\n",
+       "      <td>0.5290</td>\n",
+       "      <td>0.02950</td>\n",
+       "      <td>0.005770</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.677645</td>\n",
-       "      <td>0.58685</td>\n",
+       "      <td>0.643083</td>\n",
+       "      <td>0.54286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1/299</td>\n",
-       "      <td>5.66G</td>\n",
-       "      <td>0.03713</td>\n",
-       "      <td>0.01763</td>\n",
+       "      <td>1/69</td>\n",
+       "      <td>7.27G</td>\n",
+       "      <td>0.02155</td>\n",
+       "      <td>0.010970</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.05476</td>\n",
-       "      <td>87</td>\n",
+       "      <td>0.03252</td>\n",
+       "      <td>6</td>\n",
        "      <td>640</td>\n",
-       "      <td>0.7596</td>\n",
-       "      <td>0.5931</td>\n",
-       "      <td>0.6851</td>\n",
-       "      <td>0.5284</td>\n",
-       "      <td>0.04633</td>\n",
-       "      <td>0.007840</td>\n",
+       "      <td>0.7681</td>\n",
+       "      <td>0.6184</td>\n",
+       "      <td>0.7172</td>\n",
+       "      <td>0.5787</td>\n",
+       "      <td>0.02820</td>\n",
+       "      <td>0.005597</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.666103</td>\n",
-       "      <td>0.54407</td>\n",
+       "      <td>0.685168</td>\n",
+       "      <td>0.59255</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2/299</td>\n",
-       "      <td>5.9G</td>\n",
-       "      <td>0.03728</td>\n",
-       "      <td>0.01787</td>\n",
+       "      <td>2/69</td>\n",
+       "      <td>7.27G</td>\n",
+       "      <td>0.02127</td>\n",
+       "      <td>0.010850</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.05515</td>\n",
-       "      <td>64</td>\n",
+       "      <td>0.03212</td>\n",
+       "      <td>22</td>\n",
        "      <td>640</td>\n",
-       "      <td>0.7899</td>\n",
-       "      <td>0.5904</td>\n",
-       "      <td>0.6901</td>\n",
-       "      <td>0.5618</td>\n",
-       "      <td>0.04848</td>\n",
-       "      <td>0.007925</td>\n",
+       "      <td>0.7820</td>\n",
+       "      <td>0.5965</td>\n",
+       "      <td>0.7014</td>\n",
+       "      <td>0.5684</td>\n",
+       "      <td>0.02819</td>\n",
+       "      <td>0.005582</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.675733</td>\n",
-       "      <td>0.57463</td>\n",
+       "      <td>0.676769</td>\n",
+       "      <td>0.58170</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>3/299</td>\n",
-       "      <td>5.87G</td>\n",
-       "      <td>0.03721</td>\n",
-       "      <td>0.01785</td>\n",
+       "      <td>3/69</td>\n",
+       "      <td>7.27G</td>\n",
+       "      <td>0.02089</td>\n",
+       "      <td>0.010820</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.05507</td>\n",
-       "      <td>128</td>\n",
+       "      <td>0.03170</td>\n",
+       "      <td>9</td>\n",
        "      <td>640</td>\n",
-       "      <td>0.7593</td>\n",
-       "      <td>0.5991</td>\n",
-       "      <td>0.6911</td>\n",
-       "      <td>0.5547</td>\n",
-       "      <td>0.04522</td>\n",
-       "      <td>0.007872</td>\n",
+       "      <td>0.7795</td>\n",
+       "      <td>0.6028</td>\n",
+       "      <td>0.7099</td>\n",
+       "      <td>0.5858</td>\n",
+       "      <td>0.02629</td>\n",
+       "      <td>0.005540</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.669754</td>\n",
-       "      <td>0.56834</td>\n",
+       "      <td>0.679856</td>\n",
+       "      <td>0.59821</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>4/299</td>\n",
-       "      <td>5.9G</td>\n",
-       "      <td>0.03695</td>\n",
-       "      <td>0.01766</td>\n",
+       "      <td>4/69</td>\n",
+       "      <td>7.28G</td>\n",
+       "      <td>0.02061</td>\n",
+       "      <td>0.010730</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.05461</td>\n",
-       "      <td>39</td>\n",
+       "      <td>0.03135</td>\n",
+       "      <td>33</td>\n",
        "      <td>640</td>\n",
-       "      <td>0.7454</td>\n",
-       "      <td>0.6202</td>\n",
-       "      <td>0.7018</td>\n",
-       "      <td>0.5798</td>\n",
-       "      <td>0.04608</td>\n",
-       "      <td>0.007888</td>\n",
+       "      <td>0.7653</td>\n",
+       "      <td>0.6153</td>\n",
+       "      <td>0.7170</td>\n",
+       "      <td>0.5929</td>\n",
+       "      <td>0.02638</td>\n",
+       "      <td>0.005602</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.677061</td>\n",
-       "      <td>0.59200</td>\n",
+       "      <td>0.682151</td>\n",
+       "      <td>0.60531</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -213,151 +222,151 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>295</th>\n",
-       "      <td>295/299</td>\n",
-       "      <td>5.91G</td>\n",
-       "      <td>0.02877</td>\n",
-       "      <td>0.01319</td>\n",
+       "      <th>65</th>\n",
+       "      <td>65/69</td>\n",
+       "      <td>7.25G</td>\n",
+       "      <td>0.01648</td>\n",
+       "      <td>0.008796</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.04196</td>\n",
-       "      <td>46</td>\n",
+       "      <td>0.02527</td>\n",
+       "      <td>14</td>\n",
        "      <td>640</td>\n",
-       "      <td>0.6611</td>\n",
-       "      <td>0.6464</td>\n",
-       "      <td>0.6605</td>\n",
-       "      <td>0.5391</td>\n",
-       "      <td>0.04283</td>\n",
-       "      <td>0.009531</td>\n",
+       "      <td>0.7416</td>\n",
+       "      <td>0.6157</td>\n",
+       "      <td>0.6932</td>\n",
+       "      <td>0.5738</td>\n",
+       "      <td>0.02396</td>\n",
+       "      <td>0.006050</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.653667</td>\n",
-       "      <td>0.55124</td>\n",
+       "      <td>0.672811</td>\n",
+       "      <td>0.58574</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>296</th>\n",
-       "      <td>296/299</td>\n",
-       "      <td>5.86G</td>\n",
-       "      <td>0.02869</td>\n",
-       "      <td>0.01313</td>\n",
+       "      <th>66</th>\n",
+       "      <td>66/69</td>\n",
+       "      <td>7.25G</td>\n",
+       "      <td>0.01645</td>\n",
+       "      <td>0.008787</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.04182</td>\n",
-       "      <td>35</td>\n",
+       "      <td>0.02524</td>\n",
+       "      <td>8</td>\n",
        "      <td>640</td>\n",
-       "      <td>0.6792</td>\n",
-       "      <td>0.6322</td>\n",
-       "      <td>0.6616</td>\n",
-       "      <td>0.5396</td>\n",
-       "      <td>0.04283</td>\n",
-       "      <td>0.009532</td>\n",
+       "      <td>0.7360</td>\n",
+       "      <td>0.6175</td>\n",
+       "      <td>0.6915</td>\n",
+       "      <td>0.5715</td>\n",
+       "      <td>0.02398</td>\n",
+       "      <td>0.006076</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.654858</td>\n",
-       "      <td>0.55180</td>\n",
+       "      <td>0.671563</td>\n",
+       "      <td>0.58350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>297</th>\n",
-       "      <td>297/299</td>\n",
-       "      <td>5.91G</td>\n",
-       "      <td>0.02872</td>\n",
-       "      <td>0.01319</td>\n",
+       "      <th>67</th>\n",
+       "      <td>67/69</td>\n",
+       "      <td>7.25G</td>\n",
+       "      <td>0.01629</td>\n",
+       "      <td>0.008693</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.04191</td>\n",
-       "      <td>98</td>\n",
+       "      <td>0.02499</td>\n",
+       "      <td>3</td>\n",
        "      <td>640</td>\n",
-       "      <td>0.7010</td>\n",
-       "      <td>0.6163</td>\n",
-       "      <td>0.6619</td>\n",
-       "      <td>0.5394</td>\n",
-       "      <td>0.04282</td>\n",
-       "      <td>0.009539</td>\n",
+       "      <td>0.7511</td>\n",
+       "      <td>0.6058</td>\n",
+       "      <td>0.6895</td>\n",
+       "      <td>0.5694</td>\n",
+       "      <td>0.02401</td>\n",
+       "      <td>0.006101</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.655927</td>\n",
-       "      <td>0.55165</td>\n",
+       "      <td>0.670670</td>\n",
+       "      <td>0.58141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>298</th>\n",
-       "      <td>298/299</td>\n",
-       "      <td>5.92G</td>\n",
-       "      <td>0.02870</td>\n",
-       "      <td>0.01315</td>\n",
+       "      <th>68</th>\n",
+       "      <td>68/69</td>\n",
+       "      <td>7.25G</td>\n",
+       "      <td>0.01627</td>\n",
+       "      <td>0.008705</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.04185</td>\n",
-       "      <td>47</td>\n",
+       "      <td>0.02498</td>\n",
+       "      <td>27</td>\n",
        "      <td>640</td>\n",
-       "      <td>0.6962</td>\n",
-       "      <td>0.6193</td>\n",
-       "      <td>0.6637</td>\n",
-       "      <td>0.5406</td>\n",
-       "      <td>0.04284</td>\n",
-       "      <td>0.009546</td>\n",
+       "      <td>0.7536</td>\n",
+       "      <td>0.6024</td>\n",
+       "      <td>0.6883</td>\n",
+       "      <td>0.5680</td>\n",
+       "      <td>0.02404</td>\n",
+       "      <td>0.006127</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.655502</td>\n",
-       "      <td>0.55291</td>\n",
+       "      <td>0.669570</td>\n",
+       "      <td>0.58003</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>299</th>\n",
-       "      <td>299/299</td>\n",
-       "      <td>5.9G</td>\n",
-       "      <td>0.02875</td>\n",
-       "      <td>0.01319</td>\n",
+       "      <th>69</th>\n",
+       "      <td>69/69</td>\n",
+       "      <td>7.25G</td>\n",
+       "      <td>0.01622</td>\n",
+       "      <td>0.008689</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.04195</td>\n",
-       "      <td>44</td>\n",
+       "      <td>0.02491</td>\n",
+       "      <td>28</td>\n",
        "      <td>640</td>\n",
-       "      <td>0.6892</td>\n",
-       "      <td>0.6242</td>\n",
-       "      <td>0.6642</td>\n",
-       "      <td>0.5413</td>\n",
-       "      <td>0.04285</td>\n",
-       "      <td>0.009554</td>\n",
+       "      <td>0.6964</td>\n",
+       "      <td>0.6407</td>\n",
+       "      <td>0.6871</td>\n",
+       "      <td>0.5661</td>\n",
+       "      <td>0.02406</td>\n",
+       "      <td>0.006154</td>\n",
        "      <td>0</td>\n",
-       "      <td>0.655092</td>\n",
-       "      <td>0.55359</td>\n",
+       "      <td>0.667390</td>\n",
+       "      <td>0.57820</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>300 rows × 17 columns</p>\n",
+       "<p>70 rows × 17 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "       epoch    mem  train/box_loss  train/obj_loss  train/cls_loss    total  \\\n",
-       "0      0/299  7.49G         0.04468         0.01796               0  0.06264   \n",
-       "1      1/299  5.66G         0.03713         0.01763               0  0.05476   \n",
-       "2      2/299   5.9G         0.03728         0.01787               0  0.05515   \n",
-       "3      3/299  5.87G         0.03721         0.01785               0  0.05507   \n",
-       "4      4/299   5.9G         0.03695         0.01766               0  0.05461   \n",
-       "..       ...    ...             ...             ...             ...      ...   \n",
-       "295  295/299  5.91G         0.02877         0.01319               0  0.04196   \n",
-       "296  296/299  5.86G         0.02869         0.01313               0  0.04182   \n",
-       "297  297/299  5.91G         0.02872         0.01319               0  0.04191   \n",
-       "298  298/299  5.92G         0.02870         0.01315               0  0.04185   \n",
-       "299  299/299   5.9G         0.02875         0.01319               0  0.04195   \n",
+       "    epoch    mem  train/box_loss  train/obj_loss  train/cls_loss    total  \\\n",
+       "0    0/69  7.28G         0.02551        0.011000               0  0.03651   \n",
+       "1    1/69  7.27G         0.02155        0.010970               0  0.03252   \n",
+       "2    2/69  7.27G         0.02127        0.010850               0  0.03212   \n",
+       "3    3/69  7.27G         0.02089        0.010820               0  0.03170   \n",
+       "4    4/69  7.28G         0.02061        0.010730               0  0.03135   \n",
+       "..    ...    ...             ...             ...             ...      ...   \n",
+       "65  65/69  7.25G         0.01648        0.008796               0  0.02527   \n",
+       "66  66/69  7.25G         0.01645        0.008787               0  0.02524   \n",
+       "67  67/69  7.25G         0.01629        0.008693               0  0.02499   \n",
+       "68  68/69  7.25G         0.01627        0.008705               0  0.02498   \n",
+       "69  69/69  7.25G         0.01622        0.008689               0  0.02491   \n",
        "\n",
-       "     target  img_size  precision  recall  mAP_0.5  mAP_0.5:0.95  val/box_loss  \\\n",
-       "0        87       640     0.7777  0.6004   0.7016        0.5741       0.04719   \n",
-       "1        87       640     0.7596  0.5931   0.6851        0.5284       0.04633   \n",
-       "2        64       640     0.7899  0.5904   0.6901        0.5618       0.04848   \n",
-       "3       128       640     0.7593  0.5991   0.6911        0.5547       0.04522   \n",
-       "4        39       640     0.7454  0.6202   0.7018        0.5798       0.04608   \n",
-       "..      ...       ...        ...     ...      ...           ...           ...   \n",
-       "295      46       640     0.6611  0.6464   0.6605        0.5391       0.04283   \n",
-       "296      35       640     0.6792  0.6322   0.6616        0.5396       0.04283   \n",
-       "297      98       640     0.7010  0.6163   0.6619        0.5394       0.04282   \n",
-       "298      47       640     0.6962  0.6193   0.6637        0.5406       0.04284   \n",
-       "299      44       640     0.6892  0.6242   0.6642        0.5413       0.04285   \n",
+       "    target  img_size  precision  recall  mAP_0.5  mAP_0.5:0.95  val/box_loss  \\\n",
+       "0       12       640     0.7350  0.5716   0.6676        0.5290       0.02950   \n",
+       "1        6       640     0.7681  0.6184   0.7172        0.5787       0.02820   \n",
+       "2       22       640     0.7820  0.5965   0.7014        0.5684       0.02819   \n",
+       "3        9       640     0.7795  0.6028   0.7099        0.5858       0.02629   \n",
+       "4       33       640     0.7653  0.6153   0.7170        0.5929       0.02638   \n",
+       "..     ...       ...        ...     ...      ...           ...           ...   \n",
+       "65      14       640     0.7416  0.6157   0.6932        0.5738       0.02396   \n",
+       "66       8       640     0.7360  0.6175   0.6915        0.5715       0.02398   \n",
+       "67       3       640     0.7511  0.6058   0.6895        0.5694       0.02401   \n",
+       "68      27       640     0.7536  0.6024   0.6883        0.5680       0.02404   \n",
+       "69      28       640     0.6964  0.6407   0.6871        0.5661       0.02406   \n",
        "\n",
-       "     val/obj_loss  val/cls_loss        f1  fitness  \n",
-       "0        0.007429             0  0.677645  0.58685  \n",
-       "1        0.007840             0  0.666103  0.54407  \n",
-       "2        0.007925             0  0.675733  0.57463  \n",
-       "3        0.007872             0  0.669754  0.56834  \n",
-       "4        0.007888             0  0.677061  0.59200  \n",
-       "..            ...           ...       ...      ...  \n",
-       "295      0.009531             0  0.653667  0.55124  \n",
-       "296      0.009532             0  0.654858  0.55180  \n",
-       "297      0.009539             0  0.655927  0.55165  \n",
-       "298      0.009546             0  0.655502  0.55291  \n",
-       "299      0.009554             0  0.655092  0.55359  \n",
+       "    val/obj_loss  val/cls_loss        f1  fitness  \n",
+       "0       0.005770             0  0.643083  0.54286  \n",
+       "1       0.005597             0  0.685168  0.59255  \n",
+       "2       0.005582             0  0.676769  0.58170  \n",
+       "3       0.005540             0  0.679856  0.59821  \n",
+       "4       0.005602             0  0.682151  0.60531  \n",
+       "..           ...           ...       ...      ...  \n",
+       "65      0.006050             0  0.672811  0.58574  \n",
+       "66      0.006076             0  0.671563  0.58350  \n",
+       "67      0.006101             0  0.670670  0.58141  \n",
+       "68      0.006127             0  0.669570  0.58003  \n",
+       "69      0.006154             0  0.667390  0.57820  \n",
        "\n",
-       "[300 rows x 17 columns]"
+       "[70 rows x 17 columns]"
       ]
      },
      "execution_count": 2,
@@ -366,9 +375,10 @@
     }
    ],
    "source": [
-    "df = pd.read_csv('../../classification/yolo-second-run/runs/train/yolov7-custom7/results.txt',\n",
-    "                       delimiter=',',\n",
-    "                      names=['epoch', 'mem', 'train/box_loss', 'train/obj_loss', 'train/cls_loss', 'total', 'target', 'img_size', 'precision', 'recall', 'mAP_0.5', 'mAP_0.5:0.95', 'val/box_loss', 'val/obj_loss', 'val/cls_loss'])\n",
+    "#df = pd.read_csv('../../classification/yolo-second-run/runs/train/yolov7-custom7/results.txt',\n",
+    "df = pd.read_csv('results-final.txt',\n",
+    "                 delimiter=',',\n",
+    "                 names=['epoch', 'mem', 'train/box_loss', 'train/obj_loss', 'train/cls_loss', 'total', 'target', 'img_size', 'precision', 'recall', 'mAP_0.5', 'mAP_0.5:0.95', 'val/box_loss', 'val/obj_loss', 'val/cls_loss'])\n",
     "df['f1'] = 2 * np.divide(df['precision'] * df['recall'],\n",
     "                         df['precision'] + df['recall'])\n",
     "df['fitness'] = 0.1 * df['mAP_0.5'] + 0.9 * df['mAP_0.5:0.95']\n",
@@ -385,10 +395,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "id": "f2a956f0",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/zenon/.local/share/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  \n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -420,31 +442,31 @@
        "      <th>0</th>\n",
        "      <td>0</td>\n",
        "      <td>precision</td>\n",
-       "      <td>0.7777</td>\n",
+       "      <td>0.7350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
        "      <td>precision</td>\n",
-       "      <td>0.7596</td>\n",
+       "      <td>0.7681</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
        "      <td>precision</td>\n",
-       "      <td>0.7899</td>\n",
+       "      <td>0.7820</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3</td>\n",
        "      <td>precision</td>\n",
-       "      <td>0.7593</td>\n",
+       "      <td>0.7795</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>4</td>\n",
        "      <td>precision</td>\n",
-       "      <td>0.7454</td>\n",
+       "      <td>0.7653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -453,58 +475,58 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>595</th>\n",
-       "      <td>295</td>\n",
+       "      <th>135</th>\n",
+       "      <td>65</td>\n",
        "      <td>recall</td>\n",
-       "      <td>0.6464</td>\n",
+       "      <td>0.6157</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>596</th>\n",
-       "      <td>296</td>\n",
+       "      <th>136</th>\n",
+       "      <td>66</td>\n",
        "      <td>recall</td>\n",
-       "      <td>0.6322</td>\n",
+       "      <td>0.6175</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>597</th>\n",
-       "      <td>297</td>\n",
+       "      <th>137</th>\n",
+       "      <td>67</td>\n",
        "      <td>recall</td>\n",
-       "      <td>0.6163</td>\n",
+       "      <td>0.6058</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>598</th>\n",
-       "      <td>298</td>\n",
+       "      <th>138</th>\n",
+       "      <td>68</td>\n",
        "      <td>recall</td>\n",
-       "      <td>0.6193</td>\n",
+       "      <td>0.6024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>599</th>\n",
-       "      <td>299</td>\n",
+       "      <th>139</th>\n",
+       "      <td>69</td>\n",
        "      <td>recall</td>\n",
-       "      <td>0.6242</td>\n",
+       "      <td>0.6407</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>600 rows × 3 columns</p>\n",
+       "<p>140 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "     epoch     metric   value\n",
-       "0        0  precision  0.7777\n",
-       "1        1  precision  0.7596\n",
-       "2        2  precision  0.7899\n",
-       "3        3  precision  0.7593\n",
-       "4        4  precision  0.7454\n",
+       "0        0  precision  0.7350\n",
+       "1        1  precision  0.7681\n",
+       "2        2  precision  0.7820\n",
+       "3        3  precision  0.7795\n",
+       "4        4  precision  0.7653\n",
        "..     ...        ...     ...\n",
-       "595    295     recall  0.6464\n",
-       "596    296     recall  0.6322\n",
-       "597    297     recall  0.6163\n",
-       "598    298     recall  0.6193\n",
-       "599    299     recall  0.6242\n",
+       "135     65     recall  0.6157\n",
+       "136     66     recall  0.6175\n",
+       "137     67     recall  0.6058\n",
+       "138     68     recall  0.6024\n",
+       "139     69     recall  0.6407\n",
        "\n",
-       "[600 rows x 3 columns]"
+       "[140 rows x 3 columns]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -529,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "e04f6713",
    "metadata": {},
    "outputs": [],
@@ -551,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "65aca46f",
    "metadata": {},
    "outputs": [],
@@ -577,7 +599,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 578.387x357.463 with 1 Axes>"
       ]
@@ -591,11 +613,11 @@
     "sns.lineplot(x='epoch', y='value', style='metric', dashes=[\"\", (2,1)], data=df_aranged,\n",
     "             color='black', linewidth=1)\n",
     "ax.set_ylim([0, 1])\n",
-    "ax.set_xticks(np.arange(0, 350, 50))\n",
+    "#ax.set_xticks(np.arange(0, 70, 50))\n",
     "ax.set_ylabel('')\n",
-    "ax.axvline(133, 0, 1, lw=1, color='grey')\n",
+    "ax.axvline(27, 0, 1, lw=1, color='grey')\n",
     "fig.tight_layout()\n",
-    "fig.savefig(fig_save_dir + 'precision_recall.pdf', format='pdf', bbox_inches='tight')"
+    "fig.savefig(fig_save_dir + 'precision_recall_final.pdf', format='pdf', bbox_inches='tight')"
    ]
   },
   {
@@ -608,13 +630,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "id": "bc5a84dd",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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nP2HSpEkBqhGRerW1tcFqtUKj0aCysvK2z2tqapjgkOrY7XY89dRTuHjxIl566SU8/vjjeOihhwK6mrkajGqQcSh48cUXsXDhQpw6dQo9PT0oLCzEihUr8Morr2DevHmYMGFCoKtIpBparRbZ2dkoKytDfX39bY+jnn/++QDVjGhs1dXV4cSJE9i1axcqKiowffp0VFZWYvr06aitrQ109VSBCc4IoqKi8Mgjj+CRRx4BANxzzz348Y9/jO9+97vQ6XRIS0vDAw88gPDwcMiyjOTkZEyZMgVRUVGYPn06pk+fjvj4eMTFxSEyMjLAV0MU/AwGAzZu3IiqqirVTT1BoaO9vR01NTXQarU4d+4crl69iu7ubvT19WHPnj3K3Gr33HMP9uzZgx/+8IeIjo5W1WregcYEx0NPPPEELl26hJMnT+Ljjz/G6dOncebMGWU+IKvVim+++QYDAwODBl1HRUVh1qxZyMrKQlZWFmJjY9Hb24v+/n7IsoyJEyciPj4eAKDX69Hb26uqQdtEnsrIyMBbb70Fs9mMuLg4VFVVITMzE3FxcYGuGpFLVqsVP/nJT1BXV3fbZ+Hh4ejr64PJZMLu3buxaNEi3HnnnSE927AvMcHxQnR0NLKzs11+u5RlGT09Paivr8eXX34JSZJw6dIlfPbZZ/jzn/+MN954w604EyZMwNy5c3Hvvfdi0qRJmDx5MqKiotDS0oLOzk50d3ejq6sL3d3d0Gq1mDZtGlJSUqDT6dDT0wNZlhEXF4e4uDhMnDgRcXFxiIqKQlRUFBITEzlgjQAAPT09kCQJ/f396O/vD5rn/larFS0tLcp2dnY2Dh06hEWLFgWwVkRDO3nyJLZv345du3Zh8eLF2LRpE+6//35cv35daZvDwsLQ3t7OyWP9xOsE56233sJzzz034r5QpNFoEBUVhfvvvx/333//bZ9fv34dAwMDCA8PR3h4uLKvtbUVsixDkiS0tbWhoqICZ86cwcWLF/H555/jypUr6OzsxKRJkxAbG6skK1FRUZAkCfv27Rv0H8JwdDqd8jgtOTkZ06dPR0xMjNKz9PXXX+PChQvo7+9He3s77rzzTqSmpqKzsxPt7e2Ij4/H1KlTodfrER0djZiYmEH1iY6ORkJCAiZPnqxcI/mWLMtoa2tDT08P4uLi0NfXh9raWvztb3/DxYsXcf36daVsW1sbLl68iLq6Oly8eBEDAwPKZzk5OSguLg7EJQyi1+vx5JNPBroaRApZlnH58mVMmjQJEyZMQG1tLc6cOYNDhw5hx44dSEpKwo4dO7BmzRqXXyCZ3PiP1//z7N27V+k6BoCDBw+itLSUCY4bhpoZWa/XQ6/XK9sdHR2IjY1FamqqR9+o29vb0dbWhsjISGg0GrS3t+P69eu4fv062tvb0d3djY6ODpw9exZff/01mpubceHCBfz5z39WPgsPD8ekSZMwceJEJCcn4+6774Yoivj444+VHqGWlhY0Njaiv79/2PpoNBpMmjQJCQkJCA8Ph9lsxvLly9HY2IiOjg5ERkYq54yOjsbkyZMxZcoUREdHo6WlBYmJiYiMjMTAwICSFCYlJaGlpQUnTpxAU1MTtFotdDod7rrrLjz44IOYPHkyJk+ejMjISLS2tt72q6WlBQ6HAzqdDgkJCYiLi0NCQgL6+voAAPHx8bh+/Tp6e3txzz33YNKkSYiJifGqx6upqQlXrlzB+fPncfbsWZw9exbnzp3D9evXERYWBo1Gg5aWFoSHh6O/vx+TJ0/GXXfdhfDwcOj1ekRERGDatGmIi4tDV1cXIiMjIcuyci1tbW1obW3FuXPncObMmSGf34eHhyM5OXlQwxobG4upU6ciKysLM2bMQGJiopJwz5gxAw6Hw+NrHWunTp2C0Wgc9EiqurqaPTjkV/39/fjoo49w7Ngx/PGPf8SxY8dw9913K4OCgRu97SUlJSgoKOAXuiDi9U/CbDbDarUiIyMDFRUVqK+vx+rVq8eybuQFZ7LgdMcdd3h1no6ODmXpDVcJVn9/Pzo7O5Vf3d3dyq+uri5cu3YNzc3N+Oabb3Dt2jXo9Xr09/dj9uzZ+M53voPY2Fj09PQoSVl3dzcuX76Ms2fPor29HT09PcpbamFhYQgLC0N3dzcuXboErVaLhx56CBkZGWhvb4fD4cCRI0dQWlo6ZNIVHh6OhIQEJZHU6XT48ssv0draivb2dly7dg0REREYGBhAZ2fnbccnJCQgKysLaWlpGBgYQEdHB7q7u9Ha2gqdTgeNRqM8LnRe/+XLl/HFF18o59DpdJg5cyZmzpwJrVYLWZbR39+P+Ph4JXm7fPky6urq0NTUhJiYGMiyjN27d6OrqwvR0dHo7u6GRqNRrsWZ3GVlZeHpp59GcnIyIiMjldm3Z86ciQcffBDR0dEe/eyDIcExm81YunQpUlJSoNVqcfr0aRQVFQW6WhQCZFnG8ePHUV5ejn379qG5uRl6vR4ZGRl4++238fnnn0MURezevRuPPvooACAlJSWwlabbeJ3gPP/882hra0NeXh7S09Oxbds2t4+12WwAbkxsZDAYhpwgcKQyNpsNOp1OdZMLjicTJky4LaEaTlNTE0pLS/Gzn/0MSUlJw5YdLsGSZdllb8rAwAAkScKVK1fQ09OjJDSxsbFu9cAMDAzg6tWr+PTTT6HT6dDR0YGrV6/iyy+/xIkTJ3Do0CGEh4cjNjYWERER0Ov1aGhoUB5LRkdHK4/tMjMz8Ytf/AIGgwH33Xcf7rzzTrfqcOu133q9w12/mhgMBpSXl8NqtcLhcGDt2rUwGAyBrhaNI01NTTh27JjSs1lXV4euri6kpKQMmuJDlmXU19fjH//4B44fP44//OEPOHXqFO6++2489dRTWL58OR5++GHl392zzz4bkOshz3id4CxatAharRbbtm2DVqvFvn370NbWNuIjKlEUIQgCNm7cCODGX5Rbk5SRyjgcDpSWliI/P9/b6tM4Ntx/7mFhYUhISEBCQoJX5w4LC8PEiRORlJTk8eNBX7n1ekMhuXGyWCyoqanBr371K1RVVSmPFIluJcsympqa0NjYiL///e9466238OmnnwIAkpKSsGbNGvzgBz9AU1MT7r33XsyaNQstLS1oa2tDe3s7zpw5AwCYNGkSUlNTsW/fPuTl5XGus3HM6wQnLi4Ou3btUp7rx8fH4wc/+MGICY4gCIPGAmi1WgiCMCiBGamM1WpFTk6Ot1UnonGgpKQEKSkpyr97vkVFt+rt7cUHH3yAr776Ch999BEqKioA3PgSsHjxYvz+97/H/PnzcfHiRRw+fBhvvPEGIiMj8e677+LKlSuYMGECUlJSEBkZiddffx3f/va3kZCQgC+++AKpqalMbsY5rxMc5yhx5yrjOTk5bo3Bqa+vHzSYVq/X3/a8f7gydrsdRqNReYTlLVmWh51QyTkOY6jxGP6gxvhdXV3K7yNNZhXI61fjvR/r+P54TJaZmYnFixejqqrKp3FofDl79iyOHj2KU6dO4X/+539w7do15dHwf//3f2P27NmYOXMmEhMTlWOcicrcuXORlJQ07MLRnGhPPbxOcNLS0rBixQrlmXhZWZlH43BuJkmS22VEUYTJZPIqzs16e3vdmg57qMma/ElN8Z0/w6+++grXrl3ze3xPqene+yK+r2fmbmhouG0f36JSv97eXhw/fhyVlZX47LPPcOHCBTQ2NuLOO++EXq/HX//6V3R3d0Ov12PNmjV46qmnkJGRAQCcMI8G8TrBOXToEMrLywft27p1K1JTU4c9LiUlZVCPTWtr620DB12VKSsrg8FggM1mQ3V1NURRhMFgQHp6usf1j4iIwIwZM1x+3tnZibq6OkybNg0xMTEen3+01Bi/ubkZR48exfTp00dckT2Q16/Gez/W8c+fP+/zeqSlpSEvLw8JCQkQBAGCIKCwsNDnccn/WltbYbPZ8MEHH8BqtSpfhtLT0zFr1izMnz9feSvztddew9q1axEdHR1S49HIc14nOLcuggdAyaKHYzQasWXLFmW7oaFBecbunJvEVZmbx+lUV1cjMzPTq+QGuPGM1p0BpM7J7wJFTfGdrypHR0e7fc5AXr+a7v1Yx/fHfyzZ2dnYtm0bLBYLZFlGcXEx0tLSfB6XfOcvf/kLjhw5gm+++QYXLlzAxYsXIcsyLl68iL6+PsyZMwf/+q//irS0NDz++OPQ6XSBrjKNY14nOKIo3rZvqC7lWxkMBuTm5sJms0GSpEFvQuXl5aG8vHzYMsCNQchVVVUQRRHp6el8dZRIpQwGg9e9NqOZjmKk/U5j8bhcrZwDgC9cuICpU6eioqICe/bsQWJiInQ6HTIzM7FkyRKEhYUhJSUF//Iv/4KUlBRlmgROmEej5fXfIKPRiFWrVik9KJ50H7tqFA4fPjxiGWfsWx+PEdH49txzz+H5559X1nj7/ve/73LCwYyMDBQVFQ3ZkwyMbjoKV/sdDgdEUVReptiwYYNfE5w9e/Zg//79uO+++1BSUhKUb/j09/fj5MmT+NOf/oSysjI0NjYiNjYWHR0dSElJwW9+8xvk5+fz0RL5xagGGRcVFcFisQAAu4+JaFQWL148qDc2OztbSTJuJYoiDh486HJaitFMRyGKostjLRYLjEYj0tPTR7WmkDdvcW7cuFEZ+/Too4/i+9//vtfxR+Lpm3y1tbXYvn07Dhw4gNbWVkycOBHLli3DmjVrkJGRgatXrypvNY10zlB+gzPU44/1G5yj6gM0GAx44YUXAICTbxHRqNy6sOa6detclrVYLMNO5jia6SiGO7awsFCZvX3Xrl0jX5QLnrzF+c0330AQBJw/fx4lJSV444038MMf/hAzZ87EggULcOXKFTz22GOYM2eO1/UZLj5w4z+VkydPor6+Hh0dHejr68MjjzyCS5cu4YMPPsDRo0eRmJiIZcuWYe7cuUhPT0dERAQAKBPoXblyxavYY2G8vcEZ6vHH6g1OrxOctrY2FBQUQBAEaDQaGI1GbNu2jYkOEY0JZw+Jc12tm9uWmycAdJcn01G42l9dXY3y8nKUlJRg5cqVXj8q9+QtzmeeeUZJhp566ik8+uij2LNnDz799FPs2LEDCQkJ2L9/PzZu3IiCgoIxefzjjO9wOHDq1CkcOHAAR44cAQBERUUBALZv3w4ASE1Nxc6dO/Hkk0+OydQBofwGZ6jHH+s3OL1OcHbu3InVq1fj7bffBgCcPn0aVqsVy5Yt8/aURESK4b5E3drbc6vRTEcBYMj9NpsN8+bNQ3p6Ot555x1s2LDhtsde7nLnLc76+nocOHBgUE9PUlISkpKS8NBDDwG4sXbawMAA1q9fj5///Of43e9+h9LS0lGt0dfX14eTJ0+iqKgIH374IcLDw5Geno73338fixcvhizL6O7uRm1tLZKTk5GcnOyTMTWh/AZnqMcfqzc4vZ4VKTMzUxkMCNwYk+PtK9tERLdyfon64osvUFtbi7Vr18Jqtbp1rNFoRHV1tbJ963QUw5VxtV+SJMTHxw+KcfP2WPvlL3/pcgySU1hYGMLDw/Ef//EfOHr0KKKiojBv3jwsWLAAH374IXp6etyOd+nSJRQVFeHee+/F/PnzIQgC3n77bbS3t+Pvf/87nnjiCURHRyMmJgZ6vR7Z2dkwGAwcMExBy+senKHmJ+CcBUQ0Vob6EuWu0UxH4Wq/2WxGWVkZampqANxYf8+XX+qcj+aAG4+Fbp4bbCjz589XVsJ+6aWXsGjRIkyePBk//elPsXDhQnzrW98CcGMtv6ioKNx1113Q6XS4dOkSSktLUVFRgYiICJjNZixfvhyxsbGYPXu28kiKaLxxO8E5dOjQoO3KykqcPn1aSWocDgcMBoPL1zaJiDwx2i9Ro5mOwtV+d9bbGwsDAwM4e/as8ibZwoUL3TpuwoQJWLJkCXJycnD69Gls374dRUVFKCoqGva49PR0bN68Gc8++ywSEhKUuWiIxjO3E5wtW7bAaDQqA/+0Wi1aWlrQ0tKilGltbeU6MUTkFX6J+idJktDR0aH0vngqKioKc+bMwTvvvIOtW7fi3LlzaGpqQmdnJ7773e8iIiICLS0tkCQJEydORGpqKh81keq4neBs3LhxUHcxEdFY4peof3LO1TJx4sRRn+uOO+7A3Llzb9t/82rbRGrkdoLD5IaIfIlfov5pqFfjicgzXOyDiILCUMlNe3u78uZUTk5OyPyH7+zBCeRrwkTjndeviRMR+ZIoilixYgU++eQTfPLJJ8jLywuZga/swSEaPfbgEFFQOnTo0G0zBW/duhWpqakBqpH/XL9+HcDYjMEhClXswSGioDTU21IZGRkBqIn/ORMcPqIi8h4THCIKSqIo3ravoaEhADXxP/bgEI0eH1ERUVAyGo1YtWqVMluwIAgoLCwMcK38o6OjA5GRkQgPZxNN5C324BBRUEpLS0NRURFkWYYsyyguLg6Z18jb29sDspI0kZrw6wERBS2DwRAyvTY36+joYIJDNErswSEiCjLt7e0cYEw0SkxwiIiCTEdHB6KjowNdDaJxjQkOEVGQ4RgcotHjGBwioiDz4IMPQq/XB7oaROMaExwioiDzb//2byGzLAWRr/ARFREREakOExwiIiJSHSY4REREpDpMcIiIiEh1mOAQERGR6jDBISIiItVhgkNERESqwwSHiIiIVIcJDhEREakOExwiIiJSnYAs1WCz2QAAkiTBYDDAaDS6XcZms0GSJNjtdphMpiGPJSIabTsz1P6XX34ZmzZtgk6n88clENEo+L0HRxRFCIIAk8kEs9mMsrIyt8vY7XYAgNlsRmFhIQoKCvxadyIaH0bTzgy3/+DBg3jsscfw8MMP44EHHhjyvEQUHPzegyMIArRarbKt1WohCMKgb1euyjg/M5lM0Ol0iI+Ph91uR3p6ukd16O3thSzLOHXqlMsysiwDAM6dOweNRuPR+ceCGuP39/fje9/7Hi5duoTLly/7Pb671Hjvxzp+b29vQOrmrtG0M6Ioumx/Tpw4ofTeWCwWmM1mj+vG9sf/scdL2xPq8ce67fF7glNfXw+9Xq9s6/V6OBwOt8rc+khKkiSPkxsAys0Z7iZpNBpERkZ6fO6xosb44eHhSEhICFh8d6nx3o91fI1GE9QJzmjameHaHyeLxYKcnByv6sb2x/+xx0vbE+rxx7rtCcgYnFtJkuRxmQ0bNqC4uNireHPmzPHqOCIav7xpZ4baL4oiHA6H1+Nw2P4Q+Yffx+CkpKQM2m5tbYXBYPCojM1mg9FoHPSNiojIaTTtzEjH7t2716ueYyLyL78nOEajEdXV1cp2Q0OD8tjJ2YU8XBlBEKDT6WAymWC32yGKoh9rT0TjwWjameGOBYCDBw/eliwRUfDRyM5RPX508yuY8fHxSk/MwoULUV5eDp1ON2QZURSRl5ennMfhcODMmTP+rj4RjQPetjPDHQsAeXl52LVrF18VJwpyAUlwiIiIiHyJMxkTERGR6jDBISIiItVhgkNERESqwwSHiIiIVIcJDhEREakOExwiIiJSHSY4REREpDpMcIiIiEh1gmKxzWB080ymBoNh0FTtvvDyyy9jzZo1AICKigqsW7fOp/VwOBywWCwAgNWrVyv7XcUb63q4iu/P+2Cz2SBJEux2+6CV6v1xD1zF9tf122w2GAwG1NTUAADMZvOwcfz97yGUBeJeh1L7E+ptz3DxVdf+yHSb+vp6ef369cr2ypUrfR5z6dKlclZWlrxy5UpZkiSf18Nqtcqvv/66XFpaquxzFc8X9Rgqviz77z7U1NTIVqtVlmVZliRJzsrKGjbWWNbBVWxZ9s/1S5IkL126VPnzzJkzh40TiH8PoSpQ9zqU2p9QbnuGiy/L6mt/+IhqCIIgQKvVKttarRaCIPg0Zn5+Pk6cOIF33nlHWePGl/UwmUy3rZrsKp4v6jFUfMB/90GSJOUcOp0O8fHxsNvtfrkHrmID/rl+nU6H8vJyAIAoioMWsvXXz5+GFqh7HUrtTyi3PcPFB9TX/vAR1RDq6+uh1+uVbb1er6xA7CvO1YslSQJwo8vO3/VwFc+f9fDXfXCuGu0kSRLS09NRUVHh83vgKjbg378HFosFlZWV2LZtG4Dg+PmHukDda7Y/odH2DBcfUF/7wwTHTc4fuK84n3UCN1Y7zsnJCUg93I3nq3oE4j5s2LABxcXFLj/35T24NbY/r99sNsNgMKCkpAQbN270KI6//x6GMn/ca7Y/odf2DBVfbe0PH1EN4dbuy9bWVhgMBp/Fs9ls2LJli7Kt0+kgiqLf6+Eqnr/qEYj7YLPZYDQaYTKZAPj3Htwa25/X7/wGZDQaYbVaIQhCwH/+5P+2B2D7A4Re2zNUfDW2P0xwhmA0GpWuOgBoaGjw6ZsMBoMB8+bNU7YdDgfS09P9Xg9X8fxVD3/fB0EQoNPpYDKZYLfblefB/rgHQ8X21/VbLBbs3LlT2Y6Pj0d8fHzAf/7k/7YHYPsDhFbb4yq+GtsfjSzLstc1VbGbX0uLj49Xslxfx6uursby5cuVDNVX9RAEAXv37kVbWxvMZvOgLH6oeGNdj5Hi+/o+iKKIvLw8ZdvhcODMmTPDxhqrOrgT25fX73A4lAausrISer1eeV3WXz9/ci0Q9zqU2p9Qbnvcja+W9ocJDhEREakOH1ERERGR6jDBISIiItVhgkNERESqwwSHiIiIVIcJDhEREakOExwiIiJSHSY4NK4JgoC8vDxYLJZAV4WIQgjbnuDHBIfGNaPRiOzs7EBXg4hCDNue4McEh8a9m1eaJSLyF7Y9wY0JDhEREalOeKArQOokCALsdjsMBgOqq6uxbt06CIKADRs2KAuoSZIEu92OwsJC6HQ6AIDdbocgCDAYDBBFESaTSVkPRRRF7N27F5mZmZAkCTk5OcpxzvVNRFFEZWUltm/fHrBrJ6LAYdtDCplojNXX18tLly5Vtvfu3SuXlpbKsizLr7/+uvJnWZZlq9Uqr1y5UjnO+WenpUuXypIkyZIkyY899pgsSdJt5yktLZVfeukl5ZiVK1fKNTU1vrk4IgpabHvoZuzBoTG3d+9exMfHQxAEZd/Ny907v/kAgMlkQkFBARwOB/bu3Yu0tLRB50pOTobVagUAGAwG5dg1a9YMKpeZman8WavVQpKksbsgIhoX2PbQzZjgkE+kpaXBaDQq22azeVTnczgc0Gq1yvbNDRURkRPbHnLiIGMac7m5uaiqqhq07+ZvVA6HQ/mzzWaD0WiETqcb8rjTp08jJycHJpMJp0+fdnlOIiK2PXQzjSzLcqArQeojCAIqKyuV7ltnQ7Jlyxa0tbXBZDLB4XCguroaa9asUb4V3TpAMDc3F+np6S7PKYoi1q9fDwAoLi6GKIooKSlBWloa1q1bpwwSJKLQwLaHnJjgkF9t2bIFKSkpo+42JiLyBNue0MNHVERERKQ6THDIbwRBQFVVFWw2G+x2e6CrQ0Qhgm1PaOIjKiIiIlId9uAQERGR6jDBISIiItVhgkNERESqwwSHiIiIVIcJDhEREakOExwiIiJSHSY4REREpDpMcIiIiEh1/h9GaxAUuXZgzgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 578.387x178.731 with 2 Axes>"
       ]
@@ -627,20 +649,20 @@
     "fig, ax = plt.subplots(1, 2, figsize=set_size(width, subplots=(1,2)))\n",
     "sns.lineplot(x=df.index, y='val/box_loss', data=df, ax=ax[0], color='black', linewidth=1)\n",
     "sns.lineplot(x=df.index, y='val/obj_loss', data=df, ax=ax[1], color='black', linewidth=1)\n",
-    "ax[0].set_ylim([0.02, 0.07])\n",
-    "ax[0].set_xticks(np.arange(0, 350, 50))\n",
+    "ax[0].set_ylim([0.02, 0.03])\n",
+    "#ax[0].set_xticks(np.arange(0, 350, 50))\n",
     "ax[0].set_xlabel('epoch')\n",
     "ax[0].set_ylabel('box loss')\n",
-    "ax[0].axvline(133, 0, 1, lw=1, color='grey')\n",
+    "ax[0].axvline(27, 0, 1, lw=1, color='grey')\n",
     "\n",
-    "ax[1].set_ylim([0.007, 0.01])\n",
-    "ax[1].set_xticks(np.arange(0, 350, 50))\n",
+    "ax[1].set_ylim([0.005, 0.007])\n",
+    "#ax[1].set_xticks(np.arange(0, 350, 50))\n",
     "ax[1].set_xlabel('epoch')\n",
     "ax[1].set_ylabel('object loss')\n",
-    "ax[1].axvline(133, 0, 1, lw=1, color='grey')\n",
+    "ax[1].axvline(27, 0, 1, lw=1, color='grey')\n",
     "\n",
     "fig.tight_layout()\n",
-    "fig.savefig(fig_save_dir + 'val_box_obj_loss.pdf', format='pdf', bbox_inches='tight')"
+    "fig.savefig(fig_save_dir + 'val_box_obj_loss_final.pdf', format='pdf', bbox_inches='tight')"
    ]
   },
   {
@@ -653,13 +675,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "id": "fe9b6f1c",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.61718\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 578.387x357.463 with 1 Axes>"
       ]
@@ -671,11 +700,19 @@
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize=set_size(width, subplots=(1,1)))\n",
     "ax = sns.lineplot(x=df.index, y='fitness', data=df, color='black', linewidth=1)\n",
-    "ax.axvline(133, 0, 1, lw=1, color='grey')\n",
+    "ax.axvline(27, 0, 1, lw=1, color='grey')\n",
     "ax.set_xlabel('epoch')\n",
-    "df['fitness'].max()\n",
-    "fig.savefig(fig_save_dir + 'model_fitness.pdf', format='pdf', bbox_inches='tight')"
+    "print(df['fitness'].max())\n",
+    "fig.savefig(fig_save_dir + 'model_fitness_final.pdf', format='pdf', bbox_inches='tight')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4580a3cb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/thesis/graphics/classifier-hyp-metrics.pdf b/thesis/graphics/classifier-hyp-metrics.pdf
index f93f28c..9e1968b 100644
Binary files a/thesis/graphics/classifier-hyp-metrics.pdf and b/thesis/graphics/classifier-hyp-metrics.pdf differ
diff --git a/thesis/thesis.pdf b/thesis/thesis.pdf
index 34d8e4c..1a16369 100644
Binary files a/thesis/thesis.pdf and b/thesis/thesis.pdf differ
diff --git a/thesis/thesis.tex b/thesis/thesis.tex
index 0bdb94b..999971c 100644
--- a/thesis/thesis.tex
+++ b/thesis/thesis.tex
@@ -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