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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/.

  1. Run $ python coco_eval/coco_eval.py --gt-json path/to/coco/annotations/instances_val2017.json --pred-json results/coco_results.json

  2. 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
  1. Eval results Yolov4 input size 608x608 from offical model AlexeyAB/YoloV4.
 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

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: