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https://github.com/longcw/yolo2-pytorch

YOLOv2 in PyTorch
https://github.com/longcw/yolo2-pytorch

darknet detection pytorch yolo yolo2

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YOLOv2 in PyTorch

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# 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**NOTE: This project is no longer maintained and may not compatible with the newest pytorch (after 0.4.0).**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 [email protected]: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 (link updated)](https://drive.google.com/file/d/0B4pXCfnYmG1WUUdtRHNnLWdaMEU/view?usp=sharing&resourcekey=0-P9etgQJ4Mc9zOJ77qopoDw) 

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 (link updated)](https://drive.google.com/file/d/0B4pXCfnYmG1WRG52enNpcV80aDg/view?usp=sharing&resourcekey=0-LUq4HFw9tSLKqMcPZeWsaQ)

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)