updates

train.py

@@ -34,6 +34,7 @@ except:
 # 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
 
 # Hyperparameters (j-series, 50.5 mAP yolov3-320) evolved by @ktian08 https://github.com/ultralytics/yolov3/issues/310
+# Transfer learning edge layers: 0.1 lr0, 0.9 momentum
 hyp = {'giou': 1.582,  # giou loss gain
        'xy': 4.688,  # xy loss gain
        'wh': 0.1857,  # wh loss gain
@@ -54,27 +55,6 @@ hyp = {'giou': 1.582,  # giou loss gain
        'shear': 0.5768}  # image shear (+/- deg)
 
 
-# # Hyperparameters (i-series)
-# hyp = {'giou': 1.43,  # giou loss gain
-#        'xy': 4.688,  # xy loss gain
-#        'wh': 0.1857,  # wh loss gain
-#        'cls': 11.7,  # cls loss gain
-#        'cls_pw': 4.81,  # cls BCELoss positive_weight
-#        'obj': 11.5,  # obj loss gain
-#        'obj_pw': 1.56,  # obj BCELoss positive_weight
-#        'iou_t': 0.281,  # iou training threshold
-#        'lr0': 0.0013,  # initial learning rate
-#        'lrf': -4.,  # final LambdaLR learning rate = lr0 * (10 ** lrf)
-#        'momentum': 0.944,  # SGD momentum
-#        'weight_decay': 0.000427,  # optimizer weight decay
-#        'hsv_s': 0.0599,  # image HSV-Saturation augmentation (fraction)
-#        'hsv_v': 0.142,  # image HSV-Value augmentation (fraction)
-#        'degrees': 1.03,  # image rotation (+/- deg)
-#        'translate': 0.0552,  # image translation (+/- fraction)
-#        'scale': 0.0555,  # image scale (+/- gain)
-#        'shear': 0.434}  # image shear (+/- deg)
-
-
 def train():
     cfg = opt.cfg
     data = opt.data