updates

models.py

@@ -34,6 +34,7 @@ def create_modules(module_defs):
             if bn:
                 modules.add_module('batch_norm_%d' % i, nn.BatchNorm2d(filters))
             if module_def['activation'] == 'leaky':
+                # modules.add_module('leaky_%d' % i, nn.PReLU(num_parameters=filters, init=0.10))
                 modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1, inplace=True))
 
         elif module_def['type'] == 'maxpool':

train.py

@@ -11,44 +11,48 @@ from models import *
 from utils.datasets import *
 from utils.utils import *
 
+# 320 --epochs 1
 #      0.109      0.297       0.15      0.126       7.04      1.666      4.062     0.1845       42.6       3.34      12.61      8.338     0.2705      0.001         -4        0.9     0.0005   320 giou + best_anchor False
 #      0.223      0.218      0.138      0.189       9.28      1.153      4.376    0.08263      24.28       3.05      20.93      2.842     0.2759   0.001357     -5.036     0.9158  0.0005722   mAP/F1 - 50/50 weighting
 #      0.231      0.215      0.135      0.191       9.51      1.432      3.007    0.06082      24.87      3.477      24.13      2.802     0.3436   0.001127     -5.036     0.9232  0.0005874
 #      0.246      0.194      0.128      0.192       8.12      1.101      3.954     0.0817      22.83      3.967      19.83      1.779     0.3352   0.000895     -5.036     0.9238  0.0007973
-# 0.242	0.296	0.196	0.231	5.67	0.8541	4.286	0.1539	21.61	1.957	22.9	2.894	0.3689	0.001844	-4	0.913	0.000467  # ha
+#      0.187      0.237      0.144      0.186       14.6      1.607      4.202    0.09439      39.27      3.726      31.26      2.634      0.273   0.001542     -5.036     0.8364  0.0008393
+
+# 320 --epochs 2
+# 0.242	0.296	0.196	0.231	5.67	0.8541	4.286	0.1539	21.61	1.957	22.9	2.894	0.3689	0.001844	-4	0.913	0.000467  # ha 0.417 mAP @ epoch 100
 # 0.298	0.244	0.167	0.247	4.99	0.8896	4.067	0.1694	21.41	2.033	25.61	1.783	0.4115	0.00128	    -4	0.950	0.000377  # hb
 # 0.268	0.268	0.178	0.240	4.36	1.104	5.596	0.2087	14.47	2.599	16.27	2.406	0.4114	0.001585	-4	0.950	0.000524  # hc
-# 0.161	0.327	0.190	0.193	7.82	1.153	4.062	0.1845	24.28	3.05	20.93	2.842	0.2759	0.001357	-4	0.916	0.000572  # hd 320 --epochs 2
+# 0.161	0.327	0.190	0.193	7.82	1.153	4.062	0.1845	24.28	3.05	20.93	2.842	0.2759	0.001357	-4	0.916	0.000572  # hd 0.438 mAP @ epoch 100
-
-
-# Training hyperparameters a
-hyp = {'giou': 0.8541,  # giou loss gain
-       'xy': 4.062,  # xy loss gain
-       'wh': 0.1845,  # wh loss gain
-       'cls': 21.61,  # cls loss gain
-       'cls_pw': 1.957,  # cls BCELoss positive_weight
-       'obj': 22.9,  # obj loss gain
-       'obj_pw': 2.894,  # obj BCELoss positive_weight
-       'iou_t': 0.3689,  # iou target-anchor training threshold
-       'lr0': 0.001844,  # initial learning rate
-       'lrf': -4.,  # final learning rate = lr0 * (10 ** lrf)
-       'momentum': 0.913,  # SGD momentum
-       'weight_decay': 0.000467}  # optimizer weight decay
 
 
 # Training hyperparameters d
-# hyp = {'giou': 1.153,  # giou loss gain
+hyp = {'giou': 1.153,  # giou loss gain
+       'xy': 4.062,  # xy loss gain
+       'wh': 0.1845,  # wh loss gain
+       'cls': 24.28,  # cls loss gain
+       'cls_pw': 3.05,  # cls BCELoss positive_weight
+       'obj': 20.93,  # obj loss gain
+       'obj_pw': 2.842,  # obj BCELoss positive_weight
+       'iou_t': 0.2759,  # iou target-anchor training threshold
+       'lr0': 0.001357,  # initial learning rate
+       'lrf': -4.,  # final learning rate = lr0 * (10 ** lrf)
+       'momentum': 0.916,  # SGD momentum
+       'weight_decay': 0.000572}  # optimizer weight decay
+
+
+# # Training hyperparameters e
+# hyp = {'giou': 1.607,  # giou loss gain
 #        'xy': 4.062,  # xy loss gain
 #        'wh': 0.1845,  # wh loss gain
-#        'cls': 24.28,  # cls loss gain
+#        'cls': 39.27,  # cls loss gain
-#        'cls_pw': 3.05,  # cls BCELoss positive_weight
+#        'cls_pw': 3.726,  # cls BCELoss positive_weight
-#        'obj': 20.93,  # obj loss gain
+#        'obj': 31.26,  # obj loss gain
-#        'obj_pw': 2.842,  # obj BCELoss positive_weight
+#        'obj_pw': 2.634,  # obj BCELoss positive_weight
-#        'iou_t': 0.2759,  # iou target-anchor training threshold
+#        'iou_t': 0.273,  # iou target-anchor training threshold
-#        'lr0': 0.001357,  # initial learning rate
+#        'lr0': 0.001542,  # initial learning rate
 #        'lrf': -4.,  # final learning rate = lr0 * (10 ** lrf)
-#        'momentum': 0.916,  # SGD momentum
+#        'momentum': 0.8364,  # SGD momentum
-#        'weight_decay': 0.000572}  # optimizer weight decay
+#        'weight_decay': 0.0008393}  # optimizer weight decay
 
 
 def train(cfg,

utils/utils.py

@@ -534,11 +534,12 @@ def coco_only_people(path='../coco/labels/val2014/'):
             print(labels.shape[0], file)
 
 
-def select_best_evolve(path='../../Downloads/evolve*.txt'):  # from utils.utils import *; select_best_evolve()
+def select_best_evolve(path='evolve*.txt'):  # from utils.utils import *; select_best_evolve()
     # Find best evolved mutation
     for file in sorted(glob.glob(path)):
         x = np.loadtxt(file, dtype=np.float32)
-        print(file, x[x[:, 2].argmax()])
+        fitness = x[:, 2] * 0.5 + x[:, 3] * 0.5  # weighted mAP and F1 combination
+        print(file, x[fitness.argmax()])
 
 
 def kmeans_targets(path='./data/coco_64img.txt'):  # from utils.utils import *; kmeans_targets()