Convert YOLOv3 and YOLOv3-tiny (PyTorch version) into TensorRT models, through the torch2trt Python API.
git clone https://github.com/DocF/YOLOv3-Torch2TRT.git
$ cd weights/
$ bash download_weights.sh
Two special Python packages are needed:
-
tensorrt
-
torch2trt
Due to the upsampling operation in YOLO, according to torch2trt API introduction, you need to install the version with plugins.
Installation reference: https://github.com/NVIDIA-AI-IOT/torch2trt
python3 check.py
Acceleration Techs：
- FP16
- TensorRT
Here are some results on TITAN xp:
| Model name | Input Size | FP16 | Entire Mode*(FPS) | Backbone+FeatureNet(FPS) |
|---|---|---|---|---|
| YOLOv3 | 320×320 | 87.58 Hz | 102.95 Hz | |
| 320×320 | ✔️ | 83.63 Hz | 100.36 Hz | |
| YOLOv3-TRT | 320×320 | 110.74 Hz | 121.81 Hz | |
| 320×320 | ✔️ | 106.92 Hz | 124.95 Hz | |
| YOLOv3-tiny | 320×320 | 354.10 Hz | 668.71 Hz | |
| 320×320 | ✔️ | 379.11 Hz | 727.82 Hz | |
| YOLOv3-tiny-TRT | 320×320 | 684.75 Hz | 1035.11 Hz | |
| 320×320 | ✔️ | 649.71 Hz | 1012.66 Hz |
Entire Model* = Backbone + Feature Net + YOLO Head
python3 detect.py
This repo is based on PyTorch-YOLOv3. Thx for the great repo.