TensorRT Inference for Yolov6.
- NVIDIA T4
- tensorrt8.0
- deepstream6.0
cp export_onnx.py YOLOv6/deploy/ONNX/ cp effidehead.py YOLOv6/yolov6/models/python deploy/ONNX/export_onnx.py --weights yolov6s.pt --img 640 --batch 1 --tensorrtline29: parser.add_argument('--tensorrt', action='store_true', help='set Detect() tensorrt=True') line59: m.tensorrt = args.tensorrt line67: if(args.tensorrt): LOGGER.info('\n export ONNX file for tensorrt engine...') torch.onnx.export(model, img, export_file, verbose=False, opset_version=12, training=torch.onnx.TrainingMode.EVAL, do_constant_folding=True, input_names=['images'], output_names=['yololayer_002', 'yololayer_001', 'yololayer_000'], # output_names=['outputs'], )line12: def __init__(self, num_classes=80, anchors=1, num_layers=3, inplace=True, tensorrt=False, head_layers=None): # detection layer line26: self.tensorrt = tensorrt line76: if(self.tensorrt): # <---- add by yeah z.append(y) continue line95: if(self.tensorrt): return z # <--- add by yeahpython deploy/TENSORRT/onnx-tensorrt.py --image_path path to Inference Image Folder(only support folder) --result_path path to Inference Result --onnx path to Yolov6 onnx file --engine path to generate Yolov6 TensorRT engine python deploy/TENSORRT/onnx-tensorrt.py --image_path path to Inference Image Folder(only support folder) --result_path path to Inference Result --onnx path to Yolov6 onnx file --engine path to generate Yolov6 TensorRT engine --halfpython deploy/TENSORRT/onnx-tensorrt.py --image_path path to Inference Image Folder(only support folder) --result_path path to Inference Result --onnx path to Yolov6 onnx file --engine path to generate Yolov6 TensorRT engine --int8
python deploy/DEEPSTREAM/tensorrt_dynamic/eval_yolov6.py --image_path path to COCO Eval Image Folder(only support folder) --annotations path to COCO Annotations --onnx path to Yolov6 onnx file --engine path to generate Yolov6 TensorRT engine python deploy/DEEPSTREAM/tensorrt_dynamic/eval_yolov6.py --image_path path to COCO Eval Image Folder(only support folder) --annotations path to COCO Annotations --onnx path to Yolov6 onnx file --engine path to generate Yolov6 TensorRT engine --halfpython deploy/DEEPSTREAM/tensorrt_dynamic/eval_yolov6.py --image_path path to COCO Eval Image Folder(only support folder) --annotations path to COCO Annotations --onnx path to Yolov6 onnx file --engine path to generate Yolov6 TensorRT engine --int8 --calib_data_path path to calibration Image Folder --calib_file_path path to calibration table | YOLOv6s(640) FP32 | AP | area | maxDet |
|---|---|---|---|
| Average Precision | (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.414 |
| Average Precision | (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.610 |
| Average Precision | (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.439 |
| Average Precision | (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.224 |
| Average Precision | (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.458 |
| 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 ] = 0.333 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.522 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.552 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.341 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.612 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722 |
| YOLOv6s(640) FP16 | AP | area | maxDet |
|---|---|---|---|
| Average Precision | (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.413 |
| Average Precision | (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.610 |
| Average Precision | (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.439 |
| Average Precision | (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.224 |
| Average Precision | (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.458 |
| Average Precision | (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.574 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.333 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.522 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.552 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.342 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.612 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722 |
| YOLOv6s(640) INT8 | AP | area | maxDet |
|---|---|---|---|
| Average Precision | (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.318 |
| Average Precision | (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.492 |
| Average Precision | (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.337 |
| Average Precision | (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.163 |
| Average Precision | (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.359 |
| Average Precision | (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.450 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.278 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.441 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.468 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.266 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.522 |
| Average Recall | (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.619 |
cp path-to-tensorrt-engine deepstream e.g cp yolov6s.trt deepstreamcd deepstream export CUDA_VER=11.4 # for dGPU CUDA_VER=11.4 for Jetson make -j32 cd .. # video inference , output file: output_yolov6.mp4 deepstream-app -c deepstream_app_config_yoloV6.txt