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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-07-26 23:52:13 +02:00
parent def4a000aa
commit c413aaefea
1 changed files with 20 additions and 20 deletions
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train.py
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@ -26,6 +26,7 @@ except: # not installed: install help: https://github.com/NVIDIA/apex/issues/25
# 0.250 0.217 0.136 0.195 3.3 1.2 2 0.604 15.7 3.67 20 1.36 0.194 0.00128 -4 0.95 0.000201 0.8 0.388 1.2 0.119 0.0589 0.401 f
# 0.269 0.225 0.149 0.218 6.71 1.13 5.25 0.246 22.4 3.64 17.8 1.31 0.256 0.00146 -4 0.936 0.00042 0.123 0.18 1.81 0.0987 0.0788 0.441 g
# 0.179 0.274 0.165 0.187 7.95 1.22 7.62 0.224 17 5.71 17.7 3.28 0.295 0.00136 -4 0.875 0.000319 0.131 0.208 2.14 0.14 0.0773 0.228 h
# 0.296 0.228 0.152 0.220 5.18 1.43 4.27 0.265 11.7 4.81 11.5 1.56 0.281 0.0013 -4 0.944 0.000427 0.0599 0.142 1.03 0.0552 0.0555 0.434 i
# 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
@ -34,7 +35,7 @@ except: # not installed: install help: https://github.com/NVIDIA/apex/issues/25
# 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 g
# Training hyperparameters g
# hyp = {'giou': 1.13, # giou loss gain
# 'xy': 5.25, # xy loss gain
# 'wh': 0.246, # wh loss gain
@ -54,26 +55,25 @@ except: # not installed: install help: https://github.com/NVIDIA/apex/issues/25
# 'scale': 0.0788, # image scale (+/- gain)
# 'shear': 0.441} # image shear (+/- deg)
# Training hyperparameters h
hyp = {'giou': 1.22, # giou loss gain
'xy': 7.62, # xy loss gain
'wh': 0.224, # wh loss gain
'cls': 17.0, # cls loss gain
'cls_pw': 5.71, # cls BCELoss positive_weight
'obj': 17.7, # obj loss gain
'obj_pw': 3.28, # obj BCELoss positive_weight
'iou_t': 0.295, # iou training threshold
'lr0': 0.00136, # initial learning rate
# Training hyperparameters i
hyp = {'giou': 1.43, # giou loss gain
'xy': 4.27, # xy loss gain
'wh': 0.265, # 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': 0.875, # SGD momentum
'weight_decay': 0.00032, # optimizer weight decay
'hsv_s': 0.131, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.208, # image HSV-Value augmentation (fraction)
'degrees': 2.14, # image rotation (+/- deg)
'translate': 0.14, # image translation (+/- fraction)
'scale': 0.0773, # image scale (+/- gain)
'shear': 0.228} # image shear (+/- deg)
'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,