Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
cd darknet
make
action1.weights - 13 pedestrian actions from Stanford Action Dataset - training by YOLOv2
actionVOC.weights - 8 pedestrian actions from re-built PASCAL VOC Dataset' - training by YOLOv3
actionVOC_544.weights - improve the resolution and generate the appropriate anchors using k means clustering
coco_traffic_2017.weights - 14 types of objects in the traffic scenario - collected from COCO Dataset
Please download weights https://drive.google.com/file/d/1vXJTkrWZxue_nqbCpKQK23t48BpTWWxV/view?usp=sharing
action1.cfg action1.data
actionVOC. cfg actionVOC.data
actionVOC_544.cfg actionVOC_544.data
coco_traffic_2017.cfg coco_traffic_2017.data
action1.data
actionVOC. cfg
actionVOC_544.cfg
coco_traffic_2017.cfg
Modified Stanford Action Dataset: https://drive.google.com/file/d/1w9uiDB1TDKfCcnve7ilYJ1PeXUTYg0OU/view?usp=sharing
Modified PACSAL VOC Dataset: https://drive.google.com/file/d/1wwztVMlmmwuA8mM5K3460tvnyIizFMbv/view?usp=sharing
Modified COCO Dataset: https://pan.baidu.com/s/1JKzGP4qGJUUgpn11ywKF2A
Full COCO Dataset: http://cocodataset.org/#download - if you want to add or remove some categories you can download 'annotations' 'train2017' 'val2017'
6. Command for detecting using the trained models: (replace '~' with the specific name of the model you want to use)
detect in images:
./darknet detector test cfg/~.data cfg/~.cfg backup/~.weights test_image.jpg
detect in videos:
./darknet detector demo cfg/~.data cfg/~.cfg backup/~.weights test_video.mp4
by YOLOv3:
./darknet detector train cfg/~.data cfg/~.cfg darknet53.conv.74
by YOLOv2:
./darknet detector train cfg/~.data cfg/~.cfg darknet19_448.conv.23