pytorch 的 yolov3.zip

# YOLOV3 --- # Introduction This is my own YOLOV3 written in pytorch, and is also the first time i have reproduced a object detection model.The dataset used is PASCAL VOC. The eval tool is the voc2010. Now the mAP gains the goal score. Subsequently, i will continue to update the code to make it more concise , and add the new and efficient tricks. `Note` : Now this repository supports the model compression in the new branch [model_compression](https://github.com/Peterisfar/YOLOV3/tree/model_compression) --- ## Results | name | Train Dataset | Val Dataset | mAP(others) | mAP(mine) | notes | | :----- | :----- | :------ | :----- | :-----| :-----| | YOLOV3-448-544 | 2007trainval + 2012trainval | 2007test | 0.769 | 0.768 \| - | baseline(augument + step lr) | | YOLOV3-\*-544 | 2007trainval + 2012trainval | 2007test | 0.793 | 0.803 \| - | \+multi-scale training | | YOLOV3-\*-544 | 2007trainval + 2012trainval | 2007test | 0.806 | 0.811 \| - | \+focal loss(note the conf_loss in the start is lower) | | YOLOV3-\*-544 | 2007trainval + 2012trainval | 2007test | 0.808 | 0.813 \| - | \+giou loss | | YOLOV3-\*-544 | 2007trainval + 2012trainval | 2007test | 0.812 | 0.821 \| - | \+label smooth | | YOLOV3-\*-544 | 2007trainval + 2012trainval | 2007test | 0.822 | 0.826 \| - | \+mixup | | YOLOV3-\*-544 | 2007trainval + 2012trainval | 2007test | 0.833 | 0.832 \| 0.840 | \+cosine lr | | YOLOV3-\*-* | 2007trainval + 2012trainval | 2007test | 0.858 | 0.858 \| 0.860 | \+multi-scale test and flip, nms threshold is 0.45 | `Note` : * YOLOV3-448-544 means train image size is 448 and test image size is 544. `"*"` means the multi-scale. * mAP(mine)'s format is (use_difficult mAP | no_difficult mAP). * In the test, the nms threshold is 0.5(except the last one) and the conf_score is 0.01.`others` nms threshold is 0.45(0.45 will increase the mAP) * Now only support the single gpu to train and test. --- ## Environment * Nvida GeForce RTX 2080 Ti * CUDA10.0 * CUDNN7.0 * ubuntu 16.04 * python 3.5 ```bash # install packages pip3 install -r requirements.txt --user ``` --- ## Brief * [x] Data Augment (RandomHorizontalFlip, RandomCrop, RandomAffine, Resize) * [x] Step lr Schedule * [x] Multi-scale Training (320 to 640) * [x] focal loss * [x] GIOU * [x] Label smooth * [x] Mixup * [x] cosine lr * [x] Multi-scale Test and Flip --- ## Prepared work ### 1、Git clone YOLOV3 repository ```Bash git clone https://github.com/Peterisfar/YOLOV3.git ``` update the `"PROJECT_PATH"` in the params.py. ### 2、Download dataset * Download Pascal VOC dataset : [VOC 2012_trainval](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar) 、[VOC 2007_trainval](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar)、[VOC2007_test](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar). put them in the dir, and update the `"DATA_PATH"` in the params.py. * Convert data format : Convert the pascal voc *.xml format to custom format (Image_path0   xmin0,ymin0,xmax0,ymax0,class0   xmin1,ymin1...) ```bash cd YOLOV3 && mkdir data cd utils python3 voc.py # get train_annotation.txt and test_annotation.txt in data/ ``` ### 3、Download weight file * Darknet pre-trained weight : [darknet53-448.weights](https://pjreddie.com/media/files/darknet53_448.weights) * This repository test weight : [best.pt](https://pan.baidu.com/s/1MdE2zfIND9NYd9mWytMX8g) Make dir `weight/` in the YOLOV3 and put the weight file in. --- ## Train Run the following command to start training and see the details in the `config/yolov3_config_voc.py` ```Bash WEIGHT_PATH=weight/darknet53_448.weights CUDA_VISIBLE_DEVICES=0 nohup python3 -u train.py --weight_path $WEIGHT_PATH --gpu_id 0 > nohup.log 2>&1 & ``` `Notes:` * Training steps could run the `"cat nohup.log"` to print the log. * It supports to resume training adding `--resume`, it will load `last.pt` automaticly. --- ## Test You should define your weight file path `WEIGHT_FILE` and test data's path `DATA_TEST` ```Bash WEIGHT_PATH=weight/best.pt DATA_TEST=./data/test # your own images CUDA_VISIBLE_DEVICES=0 python3 test.py --weight_path $WEIGHT_PATH --gpu_id 0 --visiual $DATA_TEST --eval ``` The images can be seen in the `data/` --- ## TODO * [ ] Mish * [ ] OctConv * [ ] Custom data --- ## Reference * tensorflow : https://github.com/Stinky-Tofu/Stronger-yolo * pytorch : https://github.com/ultralytics/yolov3 * keras : https://github.com/qqwweee/keras-yolo3