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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-07-23 15:45:14 +02:00
parent b025b3123e
commit 8a2986e38f
1 changed files with 20 additions and 17 deletions
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train.py
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@ -18,6 +18,7 @@ from utils.adabound import *
# 0.231 0.215 0.135 0.191 9.51 1.432 3.007 0.06082 24.87 3.477 24.13 2.802 0.3436 0.001127 -5.036 0.9232 0.0005874 c
# 0.246 0.194 0.128 0.192 8.12 1.101 3.954 0.0817 22.83 3.967 19.83 1.779 0.3352 0.000895 -5.036 0.9238 0.0007973 d
# 0.187 0.237 0.144 0.186 14.6 1.607 4.202 0.09439 39.27 3.726 31.26 2.634 0.273 0.001542 -5.036 0.8364 0.0008393 e
# 0.25 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
# 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
@ -26,25 +27,26 @@ from utils.adabound 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 0.438 mAP @ epoch 100
# Training hyperparameters d
hyp = {'giou': 1.153, # giou loss gain
# Training hyperparameters f
hyp = {'giou': 1.2, # 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 training threshold
'lr0': 0.001357, # initial learning rate
'cls': 15.7, # cls loss gain
'cls_pw': 3.67, # cls BCELoss positive_weight
'obj': 20.0, # obj loss gain
'obj_pw': 1.36, # obj BCELoss positive_weight
'iou_t': 0.194, # iou training threshold
'lr0': 0.00128, # initial learning rate
'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
'momentum': 0.916, # SGD momentum
'weight_decay': 0.0000572, # optimizer weight decay
'hsv_s': 0.5, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.5, # image HSV-Value augmentation (fraction)
'degrees': 5, # image rotation (+/- deg)
'translate': 0.1, # image translation (+/- fraction)
'scale': 0.1, # image scale (+/- gain)
'shear': 2} # image shear (+/- deg)
'momentum': 0.95, # SGD momentum
'weight_decay': 0.000201, # optimizer weight decay
'hsv_s': 0.8, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.388, # image HSV-Value augmentation (fraction)
'degrees': 1.2, # image rotation (+/- deg)
'translate': 0.119, # image translation (+/- fraction)
'scale': 0.0589, # image scale (+/- gain)
'shear': 0.401} # image shear (+/- deg)
# # Training hyperparameters e
@ -90,7 +92,8 @@ def train(cfg,
model = Darknet(cfg).to(device)
# Optimizer
optimizer = optim.SGD(model.parameters(), lr=hyp['lr0'], momentum=hyp['momentum'], weight_decay=hyp['weight_decay'], nesterov=True)
optimizer = optim.SGD(model.parameters(), lr=hyp['lr0'], momentum=hyp['momentum'], weight_decay=hyp['weight_decay'],
nesterov=True)
# optimizer = AdaBound(model.parameters(), lr=hyp['lr0'], final_lr=0.1)
cutoff = -1 # backbone reaches to cutoff layer