YOLO v3 的 PyTorch 实现，包括训练和测试，并可适用于用户定义的数据集.zip

# yolov3.pytorch This repository is used for object detection. The algorithm is based on [YOLOv3: An Incremental Improvement](https://pjreddie.com/media/files/papers/YOLOv3.pdf), implemented in PyTorch v0.4. **Thanks to [ayooshkathuria/pytorch-yolo-v3](https://github.com/ayooshkathuria/pytorch-yolo-v3) and [ultralytics/yolov3](https://github.com/ultralytics/yolov3)**, based on their work, I re-implemented YOLO v3 in PyTorch for better readability and re-useablity. ## News Full version of update logs could be seen in issue [#2](https://github.com/ECer23/yolov3.pytorch/issues/2) * (2018/10/10) Support training on VOC dataset. ## Environments * Python 3.6 * PyTorch 0.4.1 * CUDA (CPU is not supported) * pycocoapi ## Train ### How to train on COCO 1. Download [COCO detection](http://cocodataset.org/#download) dataset and annotions and provide full path to your downloaded dataset in `config.py` like below ```python 'coco': { 'train_imgs': '/home/data/coco/2017/train2017', 'train_anno': '/home/data/coco/2017/annotations/instances_train2017.json' } ```` 2. Download official pre-trained Darknet53 weights on ImageNet [here](https://pjreddie.com/media/files/darknet53.conv.74), and move it to `checkpoint/darknet/darknet53.conv.74.weights` 3. Transform the weights to PyTorch readable file `0.ckpt` by running ```bash $ python transfer.py --dataset=coco --weights=darknet53.conv.74.weights ``` 4. Run ```bash $ python train.py ``` ### How to train on custom dataset 1. Implement your own dataset loading function in `dataset.py`. You should keep the interfaces similar to that in `dataset.py`. 2. Add your dataset in `prepare_dataset` function in `dataset.py` 3. Details can be viewed in `dataset.py`. This part requires some coding, and need to be imporved later. ### Training visualization Logging directory will be displayed when you run training scripts. You can visualize the training process by running ```shell $ tensorboard --logdir path-to-your-logs ```  ## Evaluation ### How to evaluate on COCO 1. Download [COCO detection](http://cocodataset.org/#download) dataset and annotions and provide full path to your downloaded dataset in `config.py` like below ```python 'coco': { 'val_imgs': '/home/data/coco/2017/val2017', 'val_anno': '/home/data/coco/2017/annotations/instances_val2017.json', } ```` 2. Download official pretrained YOLO v3 weights [here](https://pjreddie.com/media/files/yolov3.weights) and move it to `checkpoint/darknet/yolov3-coco.weights` 3. Transform the weights to PyTorch readable file `checkpoint/coco/-1.-1.ckpt` by running ```bash $ python transfer.py --dataset=coco --weights=yolov3-coco.weights ``` 4. Evaluate on validation sets you specify in `config.py` and compute the mAP by running. Some validation detection examples will be save to `assets/results` ```bash $ python evaluate.py ``` ### How to detect COCO objects 1. Download official pretrained YOLO v3 weights [here](https://pjreddie.com/media/files/yolov3.weights) and move it to `checkpoint/darknet/yolov3-coco.weights` 2. Transform the weights to PyTorch readable file `checkpoint/coco/-1.-1.ckpt` by running ```bash $ python transfer.py --dataset=coco --weights=yolov3-coco.weights ``` 3. Specify the images folder in `config.py` ```python demo = { 'images_dir': opj(ROOT, 'assets/imgs'), 'result_dir': opj(ROOT, 'assets/dets') } ``` 4. Detect your own images by running ```bash $ python demo.py ``` ### Evaluation results mAP computation seems not very accruate | Test datasets | Training datasets | Resolution | Notes | mAP | FPS | |---|---|---|---|---|---| | COCO 2017 | | 416 | official pretrained YOLO v3 weights | 63.4 | | | COCO 2017 | | 608 | paper results | 57.9 | | ### Evaluation demo  ## TODO - [ ] Evaluation - [x] ~~Draw right bounding box~~ - [ ] mAP re-implementated - [x] ~~VOC mAP implemented~~ - [ ] COCO mAP implemented - [ ] Training - [x] ~~Loss function implementation~~ - [x] ~~Visualize training process~~ - [x] ~~Use pre trained Darknet model to train on custom datasets~~ - [x] ~~Validation~~ - [ ] Train COCO from scratch - [ ] Train custom datasets from scratch - [x] ~~Learning rate scheduler~~ - [x] ~~Data augumentation~~ - [ ] General - [ ] Generalize annotation format to VOC for every dataset - [x] ~~Multi-GPU support~~ - [x] ~~Memory use imporvements~~ ## Reference * [Series: YOLO object detector in PyTorch](https://blog.paperspace.com/tag/series-yolo/) A very nice tutorial of YOLO v3 * [ayooshkathuria/pytorch-yolo-v3](https://github.com/ayooshkathuria/pytorch-yolo-v3) PyTorch implmentation of YOLO v3, with only evaluation part * [ultralytics/yolov3](https://github.com/ultralytics/yolov3) PyTorch implmentation of YOLO v3, with both training and evaluation parts * [utkuozbulak/pytorch-custom-dataset-examples](https://github.com/utkuozbulak/pytorch-custom-dataset-examples) Example of PyTorch custom dataset