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Glenn Jocher 2018-09-01 13:34:05 +02:00
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# Training # Training
Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`. 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 (coming soon) Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`. 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 (coming soon)
![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_training_loss.png "training loss") ![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_training_loss.png "coco training loss")
## Augmentation
`datasets.py` applies random augmentation to the input images in accordance with the following specifications. Augmentation is applied *only* during training, not during inference. Bounding boxes are automatically tracked and updated with the images. Examples pictured below.
- Translation: +/- 20% X and Y
- Rotation: +/- 5 degrees
- Skew: +/- 3 degrees
- Scale: +/- 20%
- Reflection: 50% probability left-right
- Saturation: +/- 50%
- Intensity: +/- 50%
![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_augmentation_examples.png "coco image augmentation")
# Inference # Inference
Checkpoints will be saved in `/checkpoints` directory. Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here. Checkpoints will be saved in `/checkpoints` directory. Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here.
![Alt](https://github.com/ultralytics/yolov3/blob/master/data/zidane_result.jpg "example") ![Alt](https://github.com/ultralytics/yolov3/blob/master/data/zidane_result.jpg "inference example")
# Testing # Testing