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

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)