Have tested on Ubuntu16.04LTS with Jetson-TX2 and Ubuntu16.04LTS with gtx1060;
NOTE: You need change CMakeList.txt on Ubuntu16.04LTS with GTX1060.
git clone https://github.com/ChenYingpeng/caffe-yolov3
cd caffe-yolov3
mkdir build
cd build
cmake ..
make -j6
darknet2caffe link github
First,download model and put it into dir caffemodel.
$ ./x86_64/bin/demo ../prototxt/yolov4.prototxt ../caffemodel/yolov4.caffemodel ../images/dog.jpg
- Run
$
./x86_64/bin/eval ../prototxt/yolov4.prototxt ../caffemodel/yolov4.caffemodel /path/to/coco/val2017/
generate coco_results.json on results/.
-
Run $
python coco_eval/coco_eval.py --gt-json path/to/coco/annotations/instances_val2017.json --pred-json results/coco_results.json -
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
- 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
Baidu link model
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.
Appreciate the great work from the following repositories: