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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-06-12 11:40:17 +02:00
parent b0b6554eee
commit dd7ca339f5
1 changed files with 10 additions and 2 deletions
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12
train.py
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@ -76,7 +76,7 @@ def train(
if multi_scale:
img_size = round((img_size / 32) * 1.5) * 32 # initiate with maximum multi_scale size
opt.num_workers = 0 # bug https://github.com/ultralytics/yolov3/issues/174
# opt.num_workers = 0 # bug https://github.com/ultralytics/yolov3/issues/174
else:
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
@ -247,6 +247,14 @@ def train(
min_size = round(img_size / 32 / 1.5)
max_size = round(img_size / 32 * 1.5)
dataset.img_size = random.choice(range(min_size, max_size + 1)) * 32
dataloader = DataLoader(dataset,
batch_size=batch_size,
num_workers=opt.num_workers,
shuffle=True, # disable rectangular training if True
pin_memory=True,
collate_fn=dataset.collate_fn)
print('multi_scale img_size = %g' % dataset.img_size)
# Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
@ -310,7 +318,7 @@ if __name__ == '__main__':
parser.add_argument('--accumulate', type=int, default=4, help='accumulate gradient x batches before optimizing')
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
parser.add_argument('--data-cfg', type=str, default='data/coco_64img.data', help='coco.data file path')
parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
parser.add_argument('--nomultiscale', action='store_false', help='random image sizes per batch 320 - 608')
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--resume', action='store_true', help='resume training flag')
parser.add_argument('--transfer', action='store_true', help='transfer learning flag')