使用 pytorch 重新实现 YOLOv2 算法.zip

## Yolov2 Pytorch Implementation This repository aims to learn and understand the YOLO algorithm. I am a beginner of deep learning, and I found the best way to learn a deep learning algorithm is to implement it from scratch. So if you also feel this way, just follow this repo! The code in this projects is clear and easier to understand, and I also documented it as much as possible. <div style="color:#0000FF" align="center"> <img src="images/result3.png" width="350"/> <img src="images/result1.png" width="350"/> </div> ## Purpose - [x] train pascal voc - [x] multi-GPUs support - [x] test - [x] pascal voc validation - [x] data augmentation - [x] pretrained network - [x] reorg layer - [x] multi-scale training - [ ] reproduce original paper's mAP ## Main Results | | training set | test set | mAP@416 | mAP@544 | | :--: | :--: | :--: | :--: | :--: | |this repo|VOC2007+2012|VOC2007|72.7|74.6| |original paper|VOC2007+2012|VOC2007|76.8|78.6| Running time: ~19ms (52FPS) on GTX 1080 ## Prerequisites - python 3.5.x - pytorch 0.4.1 - tensorboardX - opencv3 - pillow ## Preparation First clone the code git clone https://github.com/tztztztztz/yolov2.pytorch.git Install dependencies pip install -r requirements.txt Then create some folder mkdir output mkdir data ## Demo Download the pretrained weights ``` wget http://pjreddie.com/media/files/yolo-voc.weights ``` You can run the demo with `cpu` mode python demo.py Or with `gpu` mode python demo.py --cuda true ## Training on PASCAL VOC ### Prepare the data 1. Download the training data. ```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 # download 2012 data wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar ``` 2. Extract the training data, all the data will be in one directory named `VOCdevit`. We use `$VOCdevit` to represent the data root path ```bash tar xvf VOCtrainval_06-Nov-2007.tar tar xvf VOCtest_06-Nov-2007.tar tar xvf VOCdevkit_08-Jun-2007.tar # 2012 data tar xvf VOCtrainval_11-May-2012.tar ``` 3. It should have this basic structure ``` $VOCdevkit/ # development kit $VOCdevkit/VOCcode/ # VOC utility code $VOCdevkit/VOC2007 # image sets, annotations, etc. ``` 4. Create symlinks for the PASCAL VOC dataset ``` cd yolov2.pytorch mkdir data cd data mkdir VOCdevkit2007 cd VOCdevkit2007 ln -s $VOCdevit/VOC2007 VOC2007 # mkdir VOCdevkit2012 # cd VOCdevkit2012 # ln -s $VOCdevit/VOC2012 VOC2012 ``` ### Download pretrained network cd yolov2.pytorch cd data mkdir pretrained cd pretrained wget https://pjreddie.com/media/files/darknet19_448.weights ### Train the model python train.py --cuda true If you want use multiple GPUs to accelerate the training. you can use the command below. python train.py --cuda true --mGPUs true **NOTE**: Multi-scale training uses more GPU memory. If you have only one GPU with 8G memory, it's better to set `multi-scale=False` in `config/config.py`. See [link](https://github.com/tztztztztz/yolov2.pytorch/blob/master/config/config.py#L31). ## Testing python test.py --cuda true