Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

updates

This commit is contained in:
Glenn Jocher 2020-03-04 13:06:31 -08:00
parent 9c661e2d53
commit 6ab753a9e7
1 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
train.py
View File

@ -28,7 +28,7 @@ hyp = {'giou': 3.54, # giou loss gain
'obj': 64.3, # obj loss gain (*=img_size/320 if img_size != 320) 'obj': 64.3, # obj loss gain (*=img_size/320 if img_size != 320)
'obj_pw': 1.0, # obj BCELoss positive_weight 'obj_pw': 1.0, # obj BCELoss positive_weight
'iou_t': 0.225, # iou training threshold 'iou_t': 0.225, # iou training threshold
'lr0': 0.00579, # initial learning rate (SGD=5E-3, Adam=5E-4) 'lr0': 0.01, # initial learning rate (SGD=5E-3, Adam=5E-4)
'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf) 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
'momentum': 0.937, # SGD momentum 'momentum': 0.937, # SGD momentum
'weight_decay': 0.000484, # optimizer weight decay 'weight_decay': 0.000484, # optimizer weight decay
@ -141,7 +141,7 @@ def train():
# lf = lambda x: 1 - x / epochs # linear ramp to zero # lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp # lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp # lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
lf = lambda x: 0.5 * (1 + math.cos(x * math.pi / epochs)) # cosine https://arxiv.org/pdf/1812.01187.pdf lf = lambda x: (1 + math.cos(x * math.pi / epochs)) / 2 * 0.99 + 0.01 # cosine https://arxiv.org/pdf/1812.01187.pdf
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(epochs * x) for x in [0.8, 0.9]], gamma=0.1) # scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(epochs * x) for x in [0.8, 0.9]], gamma=0.1)
scheduler.last_epoch = start_epoch scheduler.last_epoch = start_epoch
@ -311,7 +311,7 @@ def train():
batch_size=batch_size * 2, batch_size=batch_size * 2,
img_size=img_size_test, img_size=img_size_test,
model=model, model=model,
conf_thres=1E-3 if opt.evolve or (final_epoch and is_coco) else 0.1, # 0.1 faster conf_thres=0.001, # 0.001 if opt.evolve or (final_epoch and is_coco) else 0.01,
iou_thres=0.6, iou_thres=0.6,
save_json=final_epoch and is_coco, save_json=final_epoch and is_coco,
single_cls=opt.single_cls, single_cls=opt.single_cls,