This is a based on argusswift/YOLOv4-pytorch.
- Nvida GeForce RTX 2080TI
- CUDA10.1
- CUDNN7.0
- Ubuntu
- Python 3.6 Anaconda Inc.
Run the installation script to install all the dependencies. You need to provide the conda install path (e.g. ~/anaconda3) and the name for the created conda environment (here YOLOv4-pytorch).
pip3 install -r requirements.txt --userNote: The install script has been tested on an Ubuntu 18.04 and Window 10 system. In case of issues, check the detailed installation instructions.
git clone github.com/argusswift/YOLOv4-pytorch.gitUpdate the "PROJECT_PATH" in the config/yolov4_config.py.
# Download the data.
cd $HOME/data
wget https://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
wget https://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
wget https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
# Extract the data.
tar xf VOCtrainval_11-May-2012.tar
tar xf VOCtrainval_06-Nov-2007.tar
tar xf VOCtest_06-Nov-2007.tar- Download links:{VOC 2012_trainval 、VOC 2007_trainval、VOC2007_test}、
#step1: download the following data and annotation
2017 Train images [118K/18GB]
2017 Val images [5K/1GB]
2017 Test images [41K/6GB]
2017 Train/Val annotations [241MB]
#step2: arrange the data to the following structure
COCO
---train
---test
---val
---annotations- Download links:{train2017_img 、train2017_ann 、val2017_img 、val2017_ann 、test2017_img 、test2017_list }
- Put them in the dir, and update the
"DATA_PATH"in the config/yolov4_config.py. - (for COCO) Use coco_to_voc.py to transfer COCO datatype to VOC datatype.
- Convert data format :use utils/voc.py or utils/coco.py convert the pascal voc *.xml format (COCO *.json format)to *.txt format (Image_path xmin0,ymin0,xmax0,ymax0,class0 xmin1,ymin1,xmax1,ymax1,class1 ...).
- Darknet pre-trained weight : yolov4
- Mobilenet pre-trained weight : mobilenetv2(code:args),mobilenetv3(code:args)
- Make dir
weight/in the YOLOv4 and put the weight file in. - set MODEL_TYPE in config/yolov4_config.py when you run training program.
- Put pictures of your dataset into the JPEGImages folder, and Annotations files into the Annotations folder.
- Use the xml_to_txt.py file to write the list of training and test files to ImageSets/Main/*.txt.
- Convert data format :use utils/voc.py or utils/coco.py convert the pascal voc *.xml format (COCO *.json format)to *.txt format (Image_path xmin0,ymin0,xmax0,ymax0,class0 xmin1,ymin1,xmax1,ymax1,class1 ...).
Run the following command to start training and see the details in the config/yolov4_config.py and you should set DATA_TYPE is VOC or COCO when you run training program.
CUDA_VISIBLE_DEVICES=0 nohup python -u train.py --weight_path weight/yolov4.weights --gpu_id 0 > nohup.log 2>&1 &Also * It supports to resume training adding --resume, it will load last.pt automaticly by using commad
CUDA_VISIBLE_DEVICES=0 nohup python -u train.py --weight_path weight/last.pt --gpu_id 0 > nohup.log 2>&1 &Modify your detecte img path:DATA_TEST=/path/to/your/test_data # your own images
for VOC dataset:
CUDA_VISIBLE_DEVICES=0 python3 eval_voc.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --eval --mode det
for COCO dataset:
CUDA_VISIBLE_DEVICES=0 python3 eval_coco.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --eval --mode detThe images can be seen in the output/. you could see pictures like follows:
Modify:
- video_path:/path/to/your/video
- weight_path:/path/to/your/weight
- output_dir:/path/to/save/dir
CUDA_VISIBLE_DEVICES=0 python3 video_test.py --weight_path best.pt --gpu_id 0 --video_path video.mp4 --output_dir --output_dirModify your evaluate dataset path:DATA_PATH=/path/to/your/test_data # your own images
for VOC dataset:
CUDA_VISIBLE_DEVICES=0 python3 eval_voc.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --eval --mode val
If you want to see the picture above, you should use follow commands:
# To get ground truths of your dataset
python3 utils/get_gt_txt.py
# To plot P-R curve and calculate mean average precision
python3 utils/get_map.py Modify your evaluate dataset path:DATA_PATH=/path/to/your/test_data # your own images
CUDA_VISIBLE_DEVICES=0 python3 eval_coco.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --eval --mode val
type=bbox
Running per image evaluation... DONE (t=0.34s).
Accumulating evaluation results... DONE (t=0.08s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.438
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.469
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.253
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.486
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.567
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.342
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.571
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.632
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.458
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.691
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.790python3 utils/modelsize.pySet showatt=Ture in val_voc.py and you will see the heatmaps emerged from network' output
for VOC dataset:
CUDA_VISIBLE_DEVICES=0 python3 eval_voc.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --eval
for COCO dataset:
CUDA_VISIBLE_DEVICES=0 python3 eval_coco.py --weight_path weight/best.pt --gpu_id 0 --visiual $DATA_TEST --evalThe heatmaps can be seen in the output/ like this:
