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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-09-11 14:00:57 +02:00
parent 919aff828e
commit 3f7f2c4a13
1 changed files with 11 additions and 7 deletions
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18
train.py
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@ -16,6 +16,9 @@ try: # Mixed precision training https://github.com/NVIDIA/apex
except:
mixed_precision = False # not installed
# 0.329 0.963 0.918 0.455 0.331 0.481 0.14 0.353 61.9 0.062 26.9 0.386 0.0624 0.000219 -4 0.901 0.000414 1.86 0.57 0.317 1.11 0.068 0.106 0.577
# 0.256 0.948 0.921 0.358 1.27 3.24 1.11 1.3 19.2 1.02 87.6 1.76 0.116 0.00288 -4 0.956 0.000277 1.09 0.57 0.317 1.11 0.068 0.106 0.577
# Hyperparameters (j-series, 50.5 mAP yolov3-320) evolved by @ktian08 https://github.com/ultralytics/yolov3/issues/310
hyp = {'giou': 1.582, # giou loss gain
'cls': 27.76, # cls loss gain (CE=~1.0, uCE=~20)
@ -35,10 +38,11 @@ hyp = {'giou': 1.582, # giou loss gain
'scale': 0.1059, # image scale (+/- gain)
'shear': 0.5768} # image shear (+/- deg)
if os.path.exists('hyp.txt'): # overwrite hyp if hyp.txt is found
x = np.loadtxt('hyp.txt')
for i, k in enumerate(hyp.keys()):
hyp[k] = x[i]
# Overwrite hyp with hyp*.txt (optional)
f = glob.glob('hyp*.txt')
if f:
for k, v in zip(hyp.keys(), np.loadtxt(f[0])):
hyp[k] = v
def train():
@ -87,9 +91,9 @@ def train():
else:
pg0 += [v] # parameter group 0
# optimizer = optim.Adam(pg0, lr=hyp['lr0'])
optimizer = optim.Adam(pg0, lr=hyp['lr0'])
# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
# optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
del pg0, pg1
@ -427,7 +431,7 @@ if __name__ == '__main__':
# Mutate
init_seeds(seed=int(time.time()))
s = [.15, .15, .15, .15, .15, .15, .15, .00, .02, .20, .15, .20, .20, .20, .20, .20, .20] # sigmas
s = [.15, .15, .15, .15, .15, .15, .15, .00, .02, .20, .15, .0, .0, .0, .0, .0, .0] # sigmas
for i, k in enumerate(hyp.keys()):
x = (np.random.randn(1) * s[i] + 1) ** 2.0 # plt.hist(x.ravel(), 300)
hyp[k] *= float(x) # vary by sigmas