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https://github.com/ChenYingpeng/caffe-yolov3
A real-time object detection framework of Yolov3/v4 based on caffe
https://github.com/ChenYingpeng/caffe-yolov3
caffe caffe-yolov4 yolov3 yolov4
Last synced: 3 months ago
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A real-time object detection framework of Yolov3/v4 based on caffe
- Host: GitHub
- URL: https://github.com/ChenYingpeng/caffe-yolov3
- Owner: ChenYingpeng
- Created: 2018-06-15T00:43:16.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-05-26T09:17:30.000Z (over 4 years ago)
- Last Synced: 2024-08-01T03:22:38.785Z (6 months ago)
- Topics: caffe, caffe-yolov4, yolov3, yolov4
- Language: C++
- Homepage:
- Size: 410 KB
- Stars: 473
- Watchers: 15
- Forks: 231
- Open Issues: 64
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-yolo-object-detection - ChenYingpeng/caffe-yolov3 - yolov3?style=social"/> : A real-time object detection framework of Yolov3/v4 based on caffe. (Other Versions of YOLO)
- awesome-yolo-object-detection - ChenYingpeng/caffe-yolov3 - yolov3?style=social"/> : A real-time object detection framework of Yolov3/v4 based on caffe. (Other Versions of YOLO)
README
# caffe-yolov3
# Paltform
Have tested on Ubuntu16.04LTS with Jetson-TX2 and Ubuntu16.04LTS with gtx1060;
NOTE: You need change CMakeList.txt on Ubuntu16.04LTS with GTX1060.
# Install
git clone https://github.com/ChenYingpeng/caffe-yolov3
cd caffe-yolov3
mkdir build
cd build
cmake ..
make -j6
# Darknet2Caffe
darknet2caffe link [github](https://github.com/ChenYingpeng/darknet2caffe)
# Demo
First,download model and put it into dir caffemodel.
$ `./x86_64/bin/demo ../prototxt/yolov4.prototxt ../caffemodel/yolov4.caffemodel ../images/dog.jpg`
# Eval
1. Run
$ `./x86_64/bin/eval ../prototxt/yolov4.prototxt ../caffemodel/yolov4.caffemodel /path/to/coco/val2017/`
generate `coco_results.json` on `results/`.
2. Run
$ `python coco_eval/coco_eval.py --gt-json path/to/coco/annotations/instances_val2017.json --pred-json results/coco_results.json`
3. Eval results Yolov4 input size 608x608 from this repo.
```
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.664
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.461
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.241
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.492
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.575
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.331
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.517
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.544
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.363
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.609
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.710
```
4. Eval results Yolov4 input size 608x608 from offical model [AlexeyAB/YoloV4](https://github.com/AlexeyAB/darknet).
```
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.505
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.749
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.557
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.357
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.559
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.613
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.368
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.598
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.634
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.500
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.680
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.757
```
# Download Model
Baidu link [model](https://pan.baidu.com/s/1yiCrnmsOm0hbweJBiiUScQ)
# Note
1.Only inference on GPU platform,such as RTX2080, GTX1060,Jetson Tegra X1,TX2,nano,Xavier etc.
2.Support model such as yolov4,yolov3,yolov3-spp,yolov3-tiny etc.
### References
Appreciate the great work from the following repositories:
- [official/Yolo](https://pjreddie.com/darknet/yolo/)
- [AlexeyAB/YoloV4](https://github.com/AlexeyAB/darknet)