## Requirements
- TensorRT 8.0+
- OpenCV 3.4.0+
- ultralytics<=8.2.103
## Different versions of yolov8
Currently, we support yolov8
- For yolov8 , download .pt from [https://github.com/ultralytics/assets/releases](https://github.com/ultralytics/assets/releases), then follow how-to-run in current page.
## Config
- Choose the model n/s/m/l/x/n2/s2/m2/l2/x2/n6/s6/m6/l6/x6 from command line arguments.
- Check more configs in [include/config.h](./include/config.h)
## How to Run, yolov8n as example
1. generate .wts from pytorch with .pt, or download .wts from model zoo
```
// download https://github.com/ultralytics/assets/releases/yolov8n.pt
// download https://github.com/lindsayshuo/yolov8-p2/releases/download/VisDrone_train_yolov8x_p2_bs1_epochs_100_imgsz_1280_last.pt (only for 10 cls p2 model)
cp {tensorrtx}/yolov8/gen_wts.py {ultralytics}/ultralytics
cd {ultralytics}/ultralytics
python gen_wts.py -w yolov8n.pt -o yolov8n.wts -t detect
// a file 'yolov8n.wts' will be generated.
// For p2 model
// download https://github.com/lindsayshuo/yolov8_p2_tensorrtx/releases/download/VisDrone_train_yolov8x_p2_bs1_epochs_100_imgsz_1280_last/VisDrone_train_yolov8x_p2_bs1_epochs_100_imgsz_1280_last.pt (only for 10 cls p2 model)
cd {ultralytics}/ultralytics
python gen_wts.py -w VisDrone_train_yolov8x_p2_bs1_epochs_100_imgsz_1280_last.pt -o VisDrone_train_yolov8x_p2_bs1_epochs_100_imgsz_1280_last.wts -t detect (only for 10 cls p2 model)
// a file 'VisDrone_train_yolov8x_p2_bs1_epochs_100_imgsz_1280_last.wts' will be generated.
// For yolov8_5u_det model
// download https://github.com/ultralytics/assets/releases/yolov5nu.pt
cd {ultralytics}/ultralytics
python gen_wts.py -w yolov5nu.pt -o yolov5nu.wts -t detect
// a file 'yolov5nu.wts' will be generated.
```
2. build tensorrtx/yolov8 and run
### Detection
```
cd {tensorrtx}/yolov8/
mkdir build
cd build
cp {ultralytics}/ultralytics/yolov8.wts {tensorrtx}/yolov8/build
cmake ..
make
sudo ./yolov8_det -s [.wts] [.engine] [n/s/m/l/x/n2/s2/m2/l2/x2/n6/s6/m6/l6/x6] // serialize model to plan file
sudo ./yolov8_det -d [.engine] [image folder] [c/g] // deserialize and run inference, the images in [image folder] will be processed.
// For example yolov8n
sudo ./yolov8_det -s yolov8n.wts yolov8.engine n
sudo ./yolov8_det -d yolov8n.engine ../images c //cpu postprocess
sudo ./yolov8_det -d yolov8n.engine ../images g //gpu postprocess
// For p2 model:
// change the "const static int kNumClass" in config.h to 10;
sudo ./yolov8_det -s VisDrone_train_yolov8x_p2_bs1_epochs_100_imgsz_1280_last.wts VisDrone_train_yolov8x_p2_bs1_epochs_100_imgsz_1280_last.engine x2
wget https://github.com/lindsayshuo/yolov8-p2/releases/download/VisDrone_train_yolov8x_p2_bs1_epochs_100_imgsz_1280_last/0000008_01999_d_0000040.jpg
cp -r 0000008_01999_d_0000040.jpg ../images
sudo ./yolov8_det -d VisDrone_train_yolov8x_p2_bs1_epochs_100_imgsz_1280_last.engine ../images c //cpu postprocess
sudo ./yolov8_det -d VisDrone_train_yolov8x_p2_bs1_epochs_100_imgsz_1280_last.engine ../images g //gpu postprocess
// For yolov8_5u_det(YOLOv5u with the anchor-free, objectness-free split head structure based on YOLOv8 features) model:
sudo ./yolov8_5u_det -s [.wts] [.engine] [n/s/m/l/x//n6/s6/m6/l6/x6]
sudo ./yolov8_5u_det -d yolov5xu.engine ../images c //cpu postprocess
sudo ./yolov8_5u_det -d yolov5xu.engine ../images g //gpu postprocess
```
### Instance Segmentation
```
# Build and serialize TensorRT engine
./yolov8_seg -s yolov8s-seg.wts yolov8s-seg.engine s
# Download the labels file
wget -O coco.txt https://raw.githubusercontent.com/amikelive/coco-labels/master/coco-labels-2014_2017.txt
# Run inference with labels file
./yolov8_seg -d yolov8s-seg.engine ../images c coco.txt
```
### Classification
```
cd {tensorrtx}/yolov8/
// Download inference images
wget https://github.com/lindsayshuo/infer_pic/releases/download/pics/1709970363.6990473rescls.jpg
mkdir samples
cp -r 1709970363.6990473rescls.jpg samples
// Download ImageNet labels
wget https://github.com/joannzhang00/ImageNet-dataset-classes-labels/blob/main/imagenet_classes.txt
// update kClsNumClass in config.h if your model is trained on custom dataset
mkdir build
cd build
cp {ultralytics}/ultralytics/yolov8n-cls.wts {tensorrtx}/yolov8/build
cmake ..
make
sudo ./yolov8_cls -s [.wts] [.engine] [n/s/m/l/x] // serialize model to plan file
sudo ./yolov8_cls -d [.engine] [image folder] // deserialize and run inference, the images in [image folder] will be processed.
// For example yolov8n
sudo ./yolov8_cls -s yolov8n-cls.wts yolov8-cls.engine n
sudo ./yolov8_cls -d yolov8n-cls.engine ../samples
```
### Pose Estimation
```
cd {tensorrtx}/yolov8/
// update "kPoseNumClass = 1" in config.h
mkdir build
cd build
cp {ultralytics}/ultralytics/yolov8-pose.wts {tensorrtx}/yolov8/build
cmake ..
make
sudo ./yolov8_pose -s [.wts] [.engine] [n/s/m/l/x/n2/s2/m2/l2/x2/n6/s6/m6/l6/x6] // serialize model to plan file
sudo ./yolov8_pose -d [.engine] [image folder] [c/g] // deserialize and run inference, the images in [image folder] will be processed.
// For example yolov8-pose
sudo ./yolov8_pose -s yolov8n-pose.wts yolov8n-pose.engine n
sudo ./yolov8_pose -d yolov8n-pose.engine ../images c //cpu postprocess
sudo ./yolov8_pose -d yolov8n-pose.engine ../images g //gpu postprocess
```
### Oriented Bounding Boxes (OBB) Estimation
```
cd {tensorrtx}/yolov8/
// update "kObbNumClass = 15" "kInputH = 1024" "kInputW = 1024" in config.h
wget https://github.com/lindsayshuo/infer_pic/releases/download/pics/obb.png
mkdir images
mv obb.png ./images
mkdir build
cd build
cp {ultralytics}/ultralytics/yolov8-obb.wts {tensorrtx}/yolov8/build
cmake ..
make
sudo ./yolov8_obb -s [.wts] [.engine] [n/s/m/l/x/n2/s2/m2/l2/x2/n6/s6/m6/l6/x6] // serialize model to plan file
sudo ./yolov8_obb -d [.engine] [image folder] [c/g] // deserialize and run inference, the images in [image folder] will be processed.
// For example yolov8-obb
sudo ./yolov8_obb -s yolov8n-obb.wts yolov8n-obb.engine n
sudo ./yolov8_obb -d yolov8n-obb.engine ../images c //cpu postprocess
sudo ./yolov8_obb -d yolov8n-obb.engine ../images g //gpu postprocess
```
4. optional, load and run the tensorrt model in python
```
// install python-tensorrt, pycuda, etc.
// ensure the yolov8n.engine and libmyplugins.so have been built
python yolov8_det_trt.py # Detection
python yolov8_seg_trt.py # Segmentation
python yolov8_cls_trt.py # Classification
python yolov8_pose_trt.py # Pose Estimation
python yolov8_5u_det_trt.py # yolov8_5u_det(YOLOv5u with the anchor-free, objectness-free split head structure based on YOLOv8 features) model
python yolov8_obb_trt.py # Oriented Bounding Boxes (OBB) Estimation
```
# 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
3. set the macro `USE_INT8` in config.h, change `kInputQuantizationFolder` into your image folder path and make
4. serialize the model and test
