# YOLOv2 in PyTorch **NOTE: This project is no longer maintained and may not compatible with the newest pytorch (after 0.4.0).** This is a [PyTorch](https://github.com/pytorch/pytorch) implementation of YOLOv2. This project is mainly based on [darkflow](https://github.com/thtrieu/darkflow) and [darknet](https://github.com/pjreddie/darknet). I used a Cython extension for postprocessing and `multiprocessing.Pool` for image preprocessing. Testing an image in VOC2007 costs about 13~20ms. For details about YOLO and YOLOv2 please refer to their [project page](https://pjreddie.com/darknet/yolo/) and the [paper](https://arxiv.org/abs/1612.08242): *YOLO9000: Better, Faster, Stronger by Joseph Redmon and Ali Farhadi*. **NOTE 1:** This is still an experimental project. VOC07 test mAP is about 0.71 (trained on VOC07+12 trainval, reported by [@cory8249](https://github.com/longcw/yolo2-pytorch/issues/23)). See [issue1](https://github.com/longcw/yolo2-pytorch/issues/1) and [issue23](https://github.com/longcw/yolo2-pytorch/issues/23) for more details about training. **NOTE 2:** I recommend to write your own dataloader using [torch.utils.data.Dataset](http://pytorch.org/docs/data.html) since `multiprocessing.Pool.imap` won't stop even there is no enough memory space. An example of `dataloader` for VOCDataset: [issue71](https://github.com/longcw/yolo2-pytorch/issues/71). **NOTE 3:** Upgrade to PyTorch 0.4: https://github.com/longcw/yolo2-pytorch/issues/59 ## Installation and demo 1. Clone this repository ```bash git clone git@github.com:longcw/yolo2-pytorch.git ``` 2. Build the reorg layer ([`tf.extract_image_patches`](https://www.tensorflow.org/api_docs/python/tf/extract_image_patches)) ```bash cd yolo2-pytorch ./make.sh ``` 3. Download the trained model [yolo-voc.weights.h5](https://drive.google.com/open?id=0B4pXCfnYmG1WUUdtRHNnLWdaMEU) and set the model path in `demo.py` 4. Run demo `python demo.py`. ## Training YOLOv2 You can train YOLO2 on any dataset. Here we train it on VOC2007/2012. 1. Download the training, validation, test data and VOCdevkit ```bash wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar ``` 2. Extract all of these tars into one directory named `VOCdevkit` ```bash tar xvf VOCtrainval_06-Nov-2007.tar tar xvf VOCtest_06-Nov-2007.tar tar xvf VOCdevkit_08-Jun-2007.tar ``` 3. It should have this basic structure ```bash $VOCdevkit/ # development kit $VOCdevkit/VOCcode/ # VOC utility code $VOCdevkit/VOC2007 # image sets, annotations, etc. # ... and several other directories ... ``` 4. Since the program loading the data in `yolo2-pytorch/data` by default, you can set the data path as following. ```bash cd yolo2-pytorch mkdir data cd data ln -s $VOCdevkit VOCdevkit2007 ``` 5. Download the [pretrained darknet19 model](https://drive.google.com/file/d/0B4pXCfnYmG1WRG52enNpcV80aDg/view?usp=sharing) and set the path in `yolo2-pytorch/cfgs/exps/darknet19_exp1.py`. 7. (optional) Training with TensorBoard. To use the TensorBoard, set `use_tensorboard = True` in `yolo2-pytorch/cfgs/config.py` and install TensorboardX (https://github.com/lanpa/tensorboard-pytorch). Tensorboard log will be saved in `training/runs`. 6. Run the training program: `python train.py`. ## Evaluation Set the path of the `trained_model` in `yolo2-pytorch/cfgs/config.py`. ```bash cd faster_rcnn_pytorch mkdir output python test.py ``` ## Training on your own data The forward pass requires that you supply 4 arguments to the network: - `im_data` - image data. - This should be in the format `C x H x W`, where `C` corresponds to the color channels of the image and `H` and `W` are the height and width respectively. - Color channels should be in RGB format. - Use the `imcv2_recolor` function provided in `utils/im_transform.py` to preprocess your image. Also, make sure that images have been resized to `416 x 416` pixels - `gt_boxes` - A list of `numpy` arrays, where each one is of size `N x 4`, where `N` is the number of features in the image. The four values in each row should correspond to `x_bottom_left`, `y_bottom_left`, `x_top_right`, and `y_top_right`. - `gt_classes` - A list of `numpy` arrays, where each array contains an integer value corresponding to the class of each bounding box provided in `gt_boxes` - `dontcare` - a list of lists License: MIT license (MIT)