Add docs

csrc/pose/normal/CMakeLists.txt

@@ -3,7 +3,7 @@ cmake_minimum_required(VERSION 2.8.12)
 set(CMAKE_CUDA_ARCHITECTURES 60 61 62 70 72 75 86)
 set(CMAKE_CUDA_COMPILER /usr/local/cuda/bin/nvcc)
 
-project(yolov8 LANGUAGES CXX CUDA)
+project(yolov8-pose LANGUAGES CXX CUDA)
 
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -O3 -g")
 set(CMAKE_CXX_STANDARD 14)

docs/Pose.md

@@ -0,0 +1,74 @@
+# YOLOv8-pose Model with TensorRT
+
+The yolov8-pose model conversion route is :
+    YOLOv8 PyTorch model -> ONNX -> TensorRT Engine
+
+***Notice !!!*** We don't support TensorRT API building !!!
+
+# Export Orin ONNX model by ultralytics
+
+You can leave this repo and use the original `ultralytics` repo for onnx export.
+
+### 1. Python script
+
+Usage:
+
+```python
+from ultralytics import YOLO
+
+# Load a model
+model = YOLO("yolov8s-pose.pt")  # load a pretrained model (recommended for training)
+success = model.export(format="engine", device=0)  # export the model to engine format
+assert success
+```
+
+After executing the above script, you will get an engine named `yolov8s-pose.engine` .
+
+### 2. CLI tools
+
+Usage:
+
+```shell
+yolo export model=yolov8s-pose.pt format=engine device=0
+```
+
+After executing the above command, you will get an engine named `yolov8s-pose.engine` too.
+
+## Inference with c++
+
+You can infer with c++ in [`csrc/pose/normal`](../csrc/pose/normal) .
+
+### Build:
+
+Please set you own librarys in [`CMakeLists.txt`](../csrc/pose/normal/CMakeLists.txt) and modify `KPS_COLORS` and `SKELETON` and  `LIMB_COLORS`  in [`main.cpp`](../csrc/pose/normal/main.cpp).
+
+Besides, you can modify the postprocess parameters such as `score_thres` and `iou_thres` and `topk` in [`main.cpp`](../csrc/pose/normal/main.cpp).
+
+```c++
+int topk = 100;
+float score_thres = 0.25f;
+float iou_thres = 0.65f;
+```
+
+And build:
+
+``` shell
+export root=${PWD}
+cd src/pose/normal
+mkdir build
+cmake ..
+make
+mv yolov8-pose ${root}
+cd ${root}
+```
+
+Usage:
+
+``` shell
+# infer image
+./yolov8-pose yolov8s-pose.engine data/bus.jpg
+# infer images
+./yolov8-pose yolov8s-pose.engine data
+# infer video
+./yolov8-pose yolov8s-pose.engine data/test.mp4 # the video path
+```

docs/Segment.md

@@ -159,7 +159,7 @@ After executing the above script, you will get an engine named `yolov8s-seg.engi
 Usage:
 
 ```shell
-yolo export model=yolov8s.pt format=engine device=0
+yolo export model=yolov8s-seg.pt format=engine device=0
 ```
 
 After executing the above command, you will get an engine named `yolov8s-seg.engine` too.