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car-detection-bayes
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add *.jpeg support

This commit is contained in:
Glenn Jocher 2019-05-11 14:55:10 +02:00
parent bc19e89247
commit 584e0a3be8
1 changed files with 37 additions and 18 deletions
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train.py
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@ -10,21 +10,19 @@ from models import *
from utils.datasets import *
from utils.utils import *
# Hyperparameters
# Evolved with python3 train.py --evolve --data data/coco_1k5k.data --epochs 50 --img-size 320
hyp = {'xy': 0.5, # xy loss gain
'wh': 0.0625, # wh loss gain
'cls': 0.0625, # cls loss gain
'conf': 4, # conf loss gain
'iou_t': 0.1, # iou target-anchor training threshold
'lr0': 0.001, # initial learning rate
# Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.204 0.302 0.175 0.234 (square smart)
hyp = {'xy': 0.167, # xy loss gain
'wh': 0.09339, # wh loss gain
'cls': 0.03868, # cls loss gain
'conf': 4.546, # conf loss gain
'iou_t': 0.2454, # iou target-anchor training threshold
'lr0': 0.000198, # initial learning rate
'lrf': -5., # final learning rate = lr0 * (10 ** lrf)
'momentum': 0.9, # SGD momentum
'weight_decay': 0.0005, # optimizer weight decay
}
'momentum': 0.95, # SGD momentum
'weight_decay': 0.0007838} # optimizer weight decay
# Original
# Hyperparameters: Original, Metrics: 0.172 0.304 0.156 0.205 (square)
# hyp = {'xy': 0.5, # xy loss gain
# 'wh': 0.0625, # wh loss gain
# 'cls': 0.0625, # cls loss gain
@ -33,8 +31,29 @@ hyp = {'xy': 0.5, # xy loss gain
# 'lr0': 0.001, # initial learning rate
# 'lrf': -5., # final learning rate = lr0 * (10 ** lrf)
# 'momentum': 0.9, # SGD momentum
# 'weight_decay': 0.0005, # optimizer weight decay
# }
# 'weight_decay': 0.0005} # optimizer weight decay
# Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.225 0.251 0.145 0.218 (rect)
# hyp = {'xy': 0.4499, # xy loss gain
# 'wh': 0.05121, # wh loss gain
# 'cls': 0.04207, # cls loss gain
# 'conf': 2.853, # conf loss gain
# 'iou_t': 0.2487, # iou target-anchor training threshold
# 'lr0': 0.0005301, # initial learning rate
# 'lrf': -5., # final learning rate = lr0 * (10 ** lrf)
# 'momentum': 0.8823, # SGD momentum
# 'weight_decay': 0.0004149} # optimizer weight decay
# Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.178 0.313 0.167 0.212 (square)
# hyp = {'xy': 0.4664, # xy loss gain
# 'wh': 0.08437, # wh loss gain
# 'cls': 0.05145, # cls loss gain
# 'conf': 4.244, # conf loss gain
# 'iou_t': 0.09121, # iou target-anchor training threshold
# 'lr0': 0.0004938, # initial learning rate
# 'lrf': -5., # final learning rate = lr0 * (10 ** lrf)
# 'momentum': 0.9025, # SGD momentum
# 'weight_decay': 0.0005417} # optimizer weight decay
def train(
@ -119,7 +138,7 @@ def train(
# plt.savefig('LR.png', dpi=300)
# Dataset
dataset = LoadImagesAndLabels(train_path, img_size, batch_size, augment=True, image_weights=False)
dataset = LoadImagesAndLabels(train_path, img_size, batch_size, augment=True, rect=True, image_weights=True)
# Initialize distributed training
if torch.cuda.device_count() > 1:
@ -330,14 +349,14 @@ if __name__ == '__main__':
# Mutate hyperparameters
old_hyp = hyp.copy()
init_seeds(seed=int(time.time()))
s = [.2, .2, .2, .2, .3, .2, .2, .03, .3]
s = [.3, .3, .3, .3, .3, .3, .3, .03, .3]
for i, k in enumerate(hyp.keys()):
x = (np.random.randn(1) * s[i] + 1) ** 1.1 # plt.hist(x.ravel(), 100)
hyp[k] = hyp[k] * float(x) # vary by about 30% 1sigma
# Clip to limits
keys = ['iou_t', 'momentum', 'weight_decay']
limits = [(0, 0.90), (0.75, 0.95), (0, 0.01)]
keys = ['lr0', 'iou_t', 'momentum', 'weight_decay']
limits = [(1e-4, 1e-2), (0, 0.90), (0.70, 0.99), (0, 0.01)]
for k, v in zip(keys, limits):
hyp[k] = np.clip(hyp[k], v[0], v[1])