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car-detection-bayes
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Glenn Jocher 2018-09-01 18:37:40 +02:00
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@ -23,7 +23,7 @@ Run `train.py` to begin training after downloading COCO data with `data/get_coco
Run `train.py -resume 1` to resume training from the most recently saved checkpoint `checkpoints/latest.pt`. 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 (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