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car-detection-bayes
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Glenn Jocher 2018-09-01 18:47:08 +02:00
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README.md
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@ -19,11 +19,12 @@ Python 3.6 or later with the following `pip3 install -U -r requirements.txt` pac
# Training # Training
Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh` and specifying COCO path on line 37 (local) or line 39 (cloud). ***Start Training:*** Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh` and specifying COCO path on line 37 (local) or line 39 (cloud).
Run `train.py -resume 1` to resume training from the most recently saved checkpoint `checkpoints/latest.pt`. ***Resume Training:*** Run `train.py -resume 1` to resume training from the most recently saved checkpoint `checkpoints/latest.pt`.
Each epoch trains on 120,000 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. An Nvidia GTX 1080 Ti will process ~10 epochs/day with full augmentation, or ~15 epochs/day without input image augmentation. Loss plots for the bounding boxes, objectness and class confidence should appear similar to results shown here (results in progress to 160 epochs, will update). Each epoch trains on 120,000 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. An Nvidia GTX 1080 Ti will process ~10 epochs/day with full augmentation, or ~15 epochs/day without input image augmentation. Loss plots for the bounding boxes, objectness and class confidence should appear similar to results shown here (results in progress to 160 epochs, will update).
![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_training_loss.png "coco training loss") ![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_training_loss.png "coco training loss")
## Image Augmentation ## Image Augmentation

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train.py
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@ -93,6 +93,7 @@ def main(opt):
for epoch in range(opt.epochs): for epoch in range(opt.epochs):
epoch += start_epoch epoch += start_epoch
# Random input
# img_size = random.choice(range(10, 20)) * 32 # img_size = random.choice(range(10, 20)) * 32
# dataloader = ListDataset(train_path, batch_size=opt.batch_size, img_size=img_size) # dataloader = ListDataset(train_path, batch_size=opt.batch_size, img_size=img_size)
# print('Running image size %g' % img_size) # print('Running image size %g' % img_size)