frigate/converters/yolo4

Following the https://github.com/jkjung-avt/tensorrt_demos#demo-5-yolov4

A build.sh file will convert pre-trained yolov3 and yolov4 models through ONNX to TensorRT engines. The implementation with a "yolo_layer" plugin has been updated to speed up inference time of the yolov3/yolov4 models.

Current "yolo_layer" plugin implementation is based on TensorRT's IPluginV2IOExt. It only works for TensorRT 6+. "yolo_layer" developed plugin by referencing similar plugin code by wang-xinyu and dongfangduoshou123. So big thanks to both of them.

Output will be copied to the ./model folder

Available models

TensorRT engine	mAP @ IoU=0.5:0.95	mAP @ IoU=0.5	FPS on Nano
yolov3-tiny-288 (FP16)	0.077	0.158	35.8
yolov3-tiny-416 (FP16)	0.096	0.202	25.5
yolov3-288 (FP16)	0.331	0.601	8.16
yolov3-416 (FP16)	0.373	0.664	4.93
yolov3-608 (FP16)	0.376	0.665	2.53
yolov3-spp-288 (FP16)	0.339	0.594	8.16
yolov3-spp-416 (FP16)	0.391	0.664	4.82
yolov3-spp-608 (FP16)	0.410	0.685	2.49
yolov4-tiny-288 (FP16)	0.179	0.344	36.6
yolov4-tiny-416 (FP16)	0.196	0.387	25.5
yolov4-288 (FP16)	0.376	0.591	7.93
yolov4-416 (FP16)	0.459	0.700	4.62
yolov4-608 (FP16)	0.488	0.736	2.35
yolov4-csp-256 (FP16)	0.336	0.502	12.8
yolov4-csp-512 (FP16)	0.436	0.630	4.26
yolov4x-mish-320 (FP16)	0.400	0.581	4.79
yolov4x-mish-640 (FP16)	0.470	0.668	1.46

Please update frigate/converters/yolo4/assets/run.sh to add necessary models

Note:

This will consume pretty significant amound of memory. You might consider extending swap on Jetson Nano

Usage:

cd ./frigate/converters/yolo4/ ./build.sh