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car-detection-bayes
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GIoU to default

This commit is contained in:
glenn-jocher 2019-07-05 11:41:43 +02:00
parent b649a95c9a
commit 429bd3b8a9
1 changed files with 12 additions and 25 deletions
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37
train.py
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@ -11,38 +11,24 @@ from models import *
from utils.datasets import * from utils.datasets import *
from utils.utils import * from utils.utils import *
# 0.149 0.241 0.126 0.156 6.85 1.008 1.421 0.07989 16.94 6.215 10.61 4.272 0.251 0.001 -4 0.9 0.0005 320 64-1 giou # 0.0945 0.279 0.114 0.131 25 0.035 0.2 0.1 0.035 79 1.61 3.53 0.29 0.001 -4 0.9 0.0005 320
# 0.111 0.27 0.132 0.131 3.96 1.276 0.3156 0.1425 21.21 6.224 11.59 8.83 0.376 0.001 -4 0.9 0.0005 # 0.149 0.241 0.126 0.156 6.85 1.008 1.421 0.07989 16.94 6.215 10.61 4.272 0.251 0.001 -4 0.9 0.0005 320 giou
hyp = {'giou': 1.008, # giou loss gain # 0.111 0.27 0.132 0.131 3.96 1.276 0.3156 0.1425 21.21 6.224 11.59 8.83 0.376 0.001 -4 0.9 0.0005 320
# 0.114 0.287 0.144 0.132 7.1 1.666 4.046 0.1364 42.6 3.34 12.61 8.338 0.2705 0.001 -4 0.9 0.0005 320 giou + best_anchor False
hyp = {'giou': 1.666, # giou loss gain
'xy': 4.062, # xy loss gain 'xy': 4.062, # xy loss gain
'wh': 0.1845, # wh loss gain 'wh': 0.1845, # wh loss gain
'cls': 16.94, # cls loss gain 'cls': 42.6, # cls loss gain
'cls_pw': 6.215, # cls BCELoss positive_weight 'cls_pw': 3.34, # cls BCELoss positive_weight
'obj': 10.61, # obj loss gain 'obj': 12.61, # obj loss gain
'obj_pw': 4.272, # obj BCELoss positive_weight 'obj_pw': 8.338, # obj BCELoss positive_weight
'iou_t': 0.251, # iou target-anchor training threshold 'iou_t': 0.2705, # iou target-anchor training threshold
'lr0': 0.001, # initial learning rate 'lr0': 0.001, # initial learning rate
'lrf': -4., # final learning rate = lr0 * (10 ** lrf) 'lrf': -4., # final learning rate = lr0 * (10 ** lrf)
'momentum': 0.90, # SGD momentum 'momentum': 0.90, # SGD momentum
'weight_decay': 0.0005} # optimizer weight decay 'weight_decay': 0.0005} # optimizer weight decay
# 0.0945 0.279 0.114 0.131 25 0.035 0.2 0.1 0.035 79 1.61 3.53 0.29 0.001 -4 0.9 0.0005 320 64-1
# 0.112 0.265 0.111 0.144 12.6 0.035 0.2 0.1 0.035 79 1.61 3.53 0.29 0.001 -4 0.9 0.0005 320 32-2
# hyp = {'giou': .035, # giou loss gain
# 'xy': 0.20, # xy loss gain
# 'wh': 0.10, # wh loss gain
# 'cls': 0.035, # cls loss gain
# 'cls_pw': 79.0, # cls BCELoss positive_weight
# 'obj': 1.61, # obj loss gain
# 'obj_pw': 3.53, # obj BCELoss positive_weight
# 'iou_t': 0.29, # iou target-anchor training threshold
# 'lr0': 0.001, # initial learning rate
# 'lrf': -4., # final learning rate = lr0 * (10 ** lrf)
# 'momentum': 0.90, # SGD momentum
# 'weight_decay': 0.0005} # optimizer weight decay
def train( def train(
cfg, cfg,
data_cfg, data_cfg,
@ -348,7 +334,8 @@ if __name__ == '__main__':
for _ in range(gen): for _ in range(gen):
# Get best hyperparamters # Get best hyperparamters
x = np.loadtxt('evolve.txt', ndmin=2) x = np.loadtxt('evolve.txt', ndmin=2)
x = x[x[:, 2].argmax()] # select best mAP as genetic fitness (col 2) fitness = x[:, 2] * 0.9 + x[:, 3] * 0.1 # fitness as weighted combination of mAP and F1
x = x[fitness.argmax()] # select best fitness hyps
for i, k in enumerate(hyp.keys()): for i, k in enumerate(hyp.keys()):
hyp[k] = x[i + 5] hyp[k] = x[i + 5]