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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-07-17 14:16:21 +02:00
parent 33838b558d
commit 407a4c481d
1 changed files with 25 additions and 25 deletions
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train.py
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@ -21,34 +21,34 @@ from utils.utils import *
# 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
# # ha
# 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
# hd
hyp = {'giou': 1.153, # giou loss gain
# Training hyperparameters a
hyp = {'giou': 0.8541, # 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
'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': 0.916, # SGD momentum
'weight_decay': 0.000572} # optimizer weight decay
'momentum': 0.913, # SGD momentum
'weight_decay': 0.000467} # optimizer weight decay
# Training hyperparameters d
# 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
def train(cfg,