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@ -6,7 +6,6 @@ The Pytorch implementation is [WongKinYiu/yolov9](https://github.com/WongKinYiu/
<a href="https://github.com/WuxinrongY"><img src="https://avatars.githubusercontent.com/u/53141838?v=4?s=48" width="40px;" alt=""/></a>
## Progress
- [x] YOLOv9-c:
- [x] FP32
@ -71,7 +70,6 @@ python gen_wts.py
```
2. build tensorrtx/yolov9 and run
```
cd {tensorrtx}/yolov9/
// update kNumClass in config.h if your model is trained on custom dataset
@ -97,12 +95,11 @@ sudo ./yolov9 -d yolov9-c.engine ../images
python yolov9_trt.py
```
# INT8 Quantization
1. Prepare calibration images, you can randomly select 1000s images from your train set. For coco, you can also download my calibration images `coco_calib` from [GoogleDrive](https://drive.google.com/drive/folders/1s7jE9DtOngZMzJC1uL307J2MiaGwdRSI?usp=sharing) or [BaiduPan](https://pan.baidu.com/s/1GOm_-JobpyLMAqZWCDUhKg) pwd: a9wh
2. unzip it in yolov8/build
2. unzip it in yolov9/build
3. set the macro `USE_INT8` in config.h and change the path of calibration images in config.h, such as 'gCalibTablePath="./coco_calib/";'