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https://github.com/ruiminshen/yolo-tf

TensorFlow implementation of the YOLO (You Only Look Once)
https://github.com/ruiminshen/yolo-tf

deep-learning object-detection python tensorflow yolo yolo2

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TensorFlow implementation of the YOLO (You Only Look Once)

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README 

        
This project is deprecated. Please see [yolo2-pytorch](https://github.com/ruiminshen/yolo2-pytorch)



# TensorFlow implementation of the [YOLO (You Only Look Once)](https://arxiv.org/pdf/1506.02640.pdf) and [YOLOv2](https://arxiv.org/pdf/1612.08242.pdf)



## Dependencies



* [Python 3](https://www.python.org/)

* [TensorFlow 1.0](https://www.tensorflow.org/)

* [NumPy](www.numpy.org/)

* [SciPy](https://www.scipy.org/)

* [Pandas](pandas.pydata.org/)

* [Matplotlib](https://matplotlib.org/)

* [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup/)

* [OpenCV](https://github.com/opencv/opencv)

* [PIL](http://www.pythonware.com/products/pil/)

* [tqdm](https://github.com/tqdm/tqdm)

* [COCO](https://github.com/pdollar/coco) (optional)



## Configuration



Configurations are mainly defined in the "config.ini" file. Such as the detection model (config/model), base directory (config/basedir, which identifies the cache files (.tfrecord), the model data files (.ckpt), and summary data for TensorBoard), and the inference function ([model]/inference). *Notability the configurations can be extended using the "-c" command-line argument*.



## Basic Usage



- Download the [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) 2007 ([training, validation](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar) and [test](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar)) and 2012 ([training and validation](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar)) dataset. Extract these tars into one directory (such as "~/Documents/Database/").



- Download the [COCO](http://mscoco.org/) 2014 ([training](http://msvocds.blob.core.windows.net/coco2014/train2014.zip), [validation](http://msvocds.blob.core.windows.net/coco2014/val2014.zip), and [test](http://msvocds.blob.core.windows.net/coco2014/test2014.zip)) dataset. Extract these zip files into one directory (such as "~/Documents/Database/coco/").



- Run "cache.py" to create the cache file for the training program. **A verify command-line argument "-v" is strongly recommended to check the training data and drop the corrupted examples**, such as the image "COCO_val2014_000000320612.jpg" in the COCO dataset.



- Run "train.py" to start the training process (the model data saved previously will be loaded if it exists). Multiple command-line arguments can be defined to control the training process. Such as the batch size, the learning rate, the optimization algorithm and the maximum number of steps.



- Run "detect.py" to detect objects in an image. Run "export CUDA_VISIBLE_DEVICES=" to avoid out of GPU memory error while the training process is running.



## Examples



### Training a 20 classes Darknet YOLOv2 model from a pretrained 80 classes model



- Cache the 20 classes data using the customized config file argument. Cache files (.tfrecord) in "~/Documents/Database/yolo-tf/cache/20" will be created.



```

python3 cache.py -c config.ini config/yolo2/darknet-20.ini -v

```



- Download a 80 classes Darknet YOLOv2 model (the original file name is "yolo.weights", a [version](https://drive.google.com/drive/folders/0B1tW_VtY7onidEwyQ2FtQVplWEU) from Darkflow is recommanded). In this tutorial I put it in "~/Downloads/yolo.weights".



- Parse the 80 classes Darknet YOLOv2 model into Tensorflow format (~/Documents/Database/yolo-tf/yolo2/darknet/80/model.ckpt). A warning like "xxx bytes remaining" indicates the file "yolo.weights" is not compatiable with the original Darknet YOLOv2 model (defined in the function `model.yolo2.inference.darknet`). **Make sure the 80 classes data is cached before parsing**.



```

python3 parse_darknet_yolo2.py ~/Downloads/yolo.weights -c config.ini config/yolo2/darknet-80.ini -d

```



- Transferring the 80 classes Darknet YOLOv2 model into a 20 classes model (~/Documents/Database/yolo-tf/yolo2/darknet/20) except the final convolutional layer. **Be ware the "-d" command-line argument will delete the model files and should be used only once when initializing the model**.



```

python3 train.py -c config.ini config/yolo2/darknet-20.ini -t ~/Documents/Database/yolo-tf/yolo2/darknet/80/model.ckpt -e yolo2_darknet/conv -d

```



- Using the following command in another terminal and opening the address "localhost:6006" in a web browser to monitor the training process.



```

tensorboard --logdir ~/Documents/Database/yolo-tf/yolo2/darknet/20

```



- If you think your model is stabilized, press Ctrl+C to cancel and restart the training with a greater batch size.



```

python3 train.py -c config.ini config/yolo2/darknet-20.ini -b 16

```



- Detect objects from an image file.



```

python3 detect.py $IMAGE_FILE -c config.ini config/yolo2/darknet-20.ini

```



- Detect objects with a camera.



```

python3 detect_camera.py -c config.ini config/yolo2/darknet-20.ini

```



## Checklist



- [x] Batch normalization

- [x] Passthrough layer

- [ ] Multi-scale training

- [ ] Dimension cluster

- [x] Extendable configuration (via "-c" command-line argument)

- [x] PASCAL VOC dataset supporting

- [x] MS COCO dataset supporting

- [x] Data augmentation: random crop

- [x] Data augmentation: random flip horizontally

- [x] Multi-thread data batch queue

- [x] Darknet model file (.weights) parser

- [x] Partial model transferring before training

- [x] Detection from image

- [x] Detection from camera

- [ ] Multi-GPU supporting

- [ ] Faster NMS using C/C++ or GPU

- [ ] Performance evaluation



## License



This project is released as the open source software with the GNU Lesser General Public License version 3 ([LGPL v3](http://www.gnu.org/licenses/lgpl-3.0.html)).



# Acknowledgements



This project is mainly inspired by the following projects:



* [YOLO (Darknet)](https://pjreddie.com/darknet/yolo/).

* [Darkflow](https://github.com/thtrieu/darkflow).