Add docs

docs/Jetson.md

@@ -3,6 +3,7 @@
 Only test on `Jetson-NX 4GB`
 
 ENVS:
+
 - Jetpack 4.6.3
 - CUDA-10.2
 - CUDNN-8.2.1
@@ -20,7 +21,8 @@ If you have other environment-related issues, please discuss in issue.
 `yolov8s.pt` is your trained pytorch model, or the official pre-trained model.
 
 Do not use any model other than pytorch model.
-Do not use [`build.py`](../build.py) to export engine if you don't know how to install pytorch and other environments on jetson.
+Do not use [`build.py`](../build.py) to export engine if you don't know how to install pytorch and other environments on
+jetson.
 
 ***!!! Please use the PC to execute the following script !!!***
 
@@ -79,7 +81,8 @@ Usage:
 `yolov8s-seg.pt` is your trained pytorch model, or the official pre-trained model.
 
 Do not use any model other than pytorch model.
-Do not use [`build.py`](../build.py) to export engine if you don't know how to install pytorch and other environments on jetson.
+Do not use [`build.py`](../build.py) to export engine if you don't know how to install pytorch and other environments on
+jetson.
 
 ***!!! Please use the PC to execute the following script !!!***
 
@@ -106,7 +109,8 @@ Here is a demo: [`csrc/jetson/segment`](../csrc/jetson/segment) .
 
 #### Build:
 
-Please modify `CLASS_NAMES` and `COLORS` and postprocess parameters in [`main.cpp`](../csrc/jetson/segment/main.cpp) for yourself.
+Please modify `CLASS_NAMES` and `COLORS` and postprocess parameters in [`main.cpp`](../csrc/jetson/segment/main.cpp) for
+yourself.
 
 ```c++
 int topk = 100;
@@ -140,8 +144,6 @@ Usage:
 ./yolov8-seg yolov8s-seg.engine data/test.mp4 # the video path
 ```
 
-
-
 ## Normal Posture
 
 ### 1. Export Posture Normal ONNX
@@ -149,7 +151,8 @@ Usage:
 `yolov8s-pose.pt` is your trained pytorch model, or the official pre-trained model.
 
 Do not use any model other than pytorch model.
-Do not use [`build.py`](../build.py) to export engine if you don't know how to install pytorch and other environments on jetson.
+Do not use [`build.py`](../build.py) to export engine if you don't know how to install pytorch and other environments on
+jetson.
 
 ***!!! Please use the PC to execute the following script !!!***
 
@@ -176,7 +179,8 @@ Here is a demo: [`csrc/jetson/pose`](../csrc/jetson/pose) .
 
 #### Build:
 
-Please modify `KPS_COLORS` and `SKELETON` and `LIMB_COLORS` and postprocess parameters in [`main.cpp`](../csrc/jetson/pose/main.cpp) for yourself.
+Please modify `KPS_COLORS` and `SKELETON` and `LIMB_COLORS` and postprocess parameters
+in [`main.cpp`](../csrc/jetson/pose/main.cpp) for yourself.
 
 ```c++
 int topk = 100;

docs/Normal.md

@@ -2,23 +2,39 @@
 
 ## Export TensorRT Engine
 
-### 1. Python script
+### 1. ONNX -> TensorRT
 
-Usage:
+You can export your onnx model by `ultralytics` API.
 
-```python
+``` shell
+yolo export model=yolov8s.pt format=onnx opset=11 simplify=True
+```
 
+or run this python script:
+
+```python
 from ultralytics import YOLO
 
 # Load a model
 model = YOLO("yolov8s.pt")  # load a pretrained model (recommended for training)
-success = model.export(format="engine", device=0)  # export the model to engine format
+success = model.export(format="onnx", opset=11, simplify=True)  # export the model to onnx format
 assert success
 ```
 
-After executing the above script, you will get an engine named `yolov8s.engine` .
+Then build engine by Trtexec Tools.
+
+You can export TensorRT engine by [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec) tools.
+
+Usage:
+
+``` shell
+/usr/src/tensorrt/bin/trtexec \
+--onnx=yolov8s.onnx \
+--saveEngine=yolov8s.engine \
+--fp16
+```
 
-### 2. CLI tools
+### 2. Direct to TensorRT (NOT RECOMMAND!!)
 
 Usage:
 
@@ -26,7 +42,19 @@ Usage:
 yolo export model=yolov8s.pt format=engine device=0
 ```
 
-After executing the above command, you will get an engine named `yolov8s.engine` too.
+or run python script:
+
+```python
+
+from ultralytics import YOLO
+
+# Load a model
+model = YOLO("yolov8s.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.engine` .
 
 ## Inference with c++
 
@@ -34,9 +62,11 @@ You can infer with c++ in [`csrc/detect/normal`](../csrc/detect/normal) .
 
 ### Build:
 
-Please set you own librarys in [`CMakeLists.txt`](../csrc/detect/normal/CMakeLists.txt) and modify `CLASS_NAMES` and `COLORS` in [`main.cpp`](../csrc/detect/normal/main.cpp).
+Please set you own librarys in [`CMakeLists.txt`](../csrc/detect/normal/CMakeLists.txt) and modify `CLASS_NAMES`
+and `COLORS` in [`main.cpp`](../csrc/detect/normal/main.cpp).
 
-Besides, you can modify the postprocess parameters such as `num_labels` and `score_thres` and `iou_thres` and `topk` in [`main.cpp`](../csrc/detect/normal/main.cpp).
+Besides, you can modify the postprocess parameters such as `num_labels` and `score_thres` and `iou_thres` and `topk`
+in [`main.cpp`](../csrc/detect/normal/main.cpp).
 
 ```c++
 int num_labels = 80;

docs/Pose.md

@@ -1,7 +1,7 @@
 # YOLOv8-pose Model with TensorRT
 
 The yolov8-pose model conversion route is :
-    YOLOv8 PyTorch model -> ONNX -> TensorRT Engine
+YOLOv8 PyTorch model -> ONNX -> TensorRT Engine
 
 ***Notice !!!*** We don't support TensorRT API building !!!
 
@@ -9,10 +9,48 @@ The yolov8-pose model conversion route is :
 
 You can leave this repo and use the original `ultralytics` repo for onnx export.
 
-### 1. Python script
+### 1. ONNX -> TensorRT
+
+You can export your onnx model by `ultralytics` API.
+
+``` shell
+yolo export model=yolov8s-pose.pt format=onnx opset=11 simplify=True
+```
+
+or run this python script:
+
+```python
+from ultralytics import YOLO
+
+# Load a model
+model = YOLO("yolov8s-pose.pt")  # load a pretrained model (recommended for training)
+success = model.export(format="onnx", opset=11, simplify=True)  # export the model to onnx format
+assert success
+```
+
+Then build engine by Trtexec Tools.
+
+You can export TensorRT engine by [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec) tools.
+
+Usage:
+
+``` shell
+/usr/src/tensorrt/bin/trtexec \
+--onnx=yolov8s-pose.onnx \
+--saveEngine=yolov8s-pose.engine \
+--fp16
+```
+
+### 2. Direct to TensorRT (NOT RECOMMAND!!)
 
 Usage:
 
+```shell
+yolo export model=yolov8s-pose.pt format=engine device=0
+```
+
+or run python script:
+
 ```python
 from ultralytics import YOLO
 
@@ -24,15 +62,31 @@ assert success
 
 After executing the above script, you will get an engine named `yolov8s-pose.engine` .
 
-### 2. CLI tools
+# Inference
+
+## Infer with python script
+
+You can infer images with the engine by [`infer-pose.py`](../infer-pose.py) .
 
 Usage:
 
-```shell
+``` shell
-yolo export model=yolov8s-pose.pt format=engine device=0
+python3 infer-pose.py \
+--engine yolov8s-pose.engine \
+--imgs data \
+--show \
+--out-dir outputs \
+--device cuda:0
 ```
 
-After executing the above command, you will get an engine named `yolov8s-pose.engine` too.
+#### Description of all arguments
+
+- `--engine` : The Engine you export.
+- `--imgs` : The images path you want to detect.
+- `--show` : Whether to show detection results.
+- `--out-dir` : Where to save detection results images. It will not work when use `--show` flag.
+- `--device` : The CUDA deivce you use.
+- `--profile` : Profile the TensorRT engine.
 
 ## Inference with c++
 
@@ -40,9 +94,11 @@ 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).
+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).
+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;

docs/Segment.md

@@ -1,7 +1,7 @@
 # YOLOv8-seg Model with TensorRT
 
 The yolov8-seg model conversion route is :
-    YOLOv8 PyTorch model -> ONNX -> TensorRT Engine
+YOLOv8 PyTorch model -> ONNX -> TensorRT Engine
 
 ***Notice !!!*** We don't support TensorRT API building !!!
 
@@ -96,7 +96,9 @@ You can infer segment engine with c++ in [`csrc/segment/simple`](../csrc/segment
 
 ### Build:
 
-Please set you own librarys in [`CMakeLists.txt`](../csrc/segment/simple/CMakeLists.txt) and modify you own config in [`main.cpp`](../csrc/segment/simple/main.cpp) such as `CLASS_NAMES`, `COLORS`, `MASK_COLORS` and postprocess parameters .
+Please set you own librarys in [`CMakeLists.txt`](../csrc/segment/simple/CMakeLists.txt) and modify you own config
+in [`main.cpp`](../csrc/segment/simple/main.cpp) such as `CLASS_NAMES`, `COLORS`, `MASK_COLORS` and postprocess
+parameters .
 
 ```c++
 int topk = 100;
@@ -119,7 +121,8 @@ cd ${root}
 
 ***Notice !!!***
 
-If you have build OpenCV(>=4.7.0) by yourself, it provides a new api [`cv::dnn::NMSBoxesBatched`](https://docs.opencv.org/4.x/d6/d0f/group__dnn.html#ga977aae09fbf7c804e003cfea1d4e928c) .
+If you have build OpenCV(>=4.7.0) by yourself, it provides a new
+api [`cv::dnn::NMSBoxesBatched`](https://docs.opencv.org/4.x/d6/d0f/group__dnn.html#ga977aae09fbf7c804e003cfea1d4e928c) .
 It is a gread api about efficient in-class nms . It will be used by default!
 
 ***!!!***
@@ -139,22 +142,39 @@ Usage:
 
 You can leave this repo and use the original `ultralytics` repo for onnx export.
 
-### 1. Python script
+### 1. ONNX -> TensorRT
 
-Usage:
+You can export your onnx model by `ultralytics` API.
+
+``` shell
+yolo export model=yolov8s-seg.pt format=onnx opset=11 simplify=True
+```
+
+or run this python script:
 
 ```python
 from ultralytics import YOLO
 
 # Load a model
 model = YOLO("yolov8s-seg.pt")  # load a pretrained model (recommended for training)
-success = model.export(format="engine", device=0)  # export the model to engine format
+success = model.export(format="onnx", opset=11, simplify=True)  # export the model to onnx format
 assert success
 ```
 
-After executing the above script, you will get an engine named `yolov8s-seg.engine` .
+Then build engine by Trtexec Tools.
 
-### 2. CLI tools
+You can export TensorRT engine by [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec) tools.
+
+Usage:
+
+``` shell
+/usr/src/tensorrt/bin/trtexec \
+--onnx=yolov8s-seg.onnx \
+--saveEngine=yolov8s-seg.engine \
+--fp16
+```
+
+### 2. Direct to TensorRT (NOT RECOMMAND!!)
 
 Usage:
 
@@ -162,7 +182,18 @@ Usage:
 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.
+or run python script:
+
+```python
+from ultralytics import YOLO
+
+# Load a model
+model = YOLO("yolov8s-seg.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-seg.engine` .
 
 ## Inference with c++
 
@@ -170,9 +201,11 @@ You can infer with c++ in [`csrc/segment/normal`](../csrc/segment/normal) .
 
 ### Build:
 
-Please set you own librarys in [`CMakeLists.txt`](../csrc/segment/normal/CMakeLists.txt) and modify `CLASS_NAMES` and `COLORS` in [`main.cpp`](../csrc/segment/normal/main.cpp).
+Please set you own librarys in [`CMakeLists.txt`](../csrc/segment/normal/CMakeLists.txt) and modify `CLASS_NAMES`
+and `COLORS` in [`main.cpp`](../csrc/segment/normal/main.cpp).
 
-Besides, you can modify the postprocess parameters such as `num_labels` and `score_thres` and `iou_thres` and `topk` in [`main.cpp`](../csrc/segment/normal/main.cpp).
+Besides, you can modify the postprocess parameters such as `num_labels` and `score_thres` and `iou_thres` and `topk`
+in [`main.cpp`](../csrc/segment/normal/main.cpp).
 
 ```c++
 int topk = 100;