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Add C++ Classify inference example (#6868)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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DennisJ 2023-12-10 23:41:24 +08:00 • committed by GitHub
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38
examples/YOLOv8-ONNXRuntime-CPP/README.md
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@ -13,6 +13,10 @@ This example demonstrates how to perform inference using YOLOv8 in C++ with ONNX
- Faster than OpenCV's DNN inference on both CPU and GPU.
- Supports FP32 and FP16 CUDA acceleration.
## Note :coffee:
1.~~This repository should also work for YOLOv5, which needs a permute operator for the output of the YOLOv5 model, but this has not been implemented yet.~~ Benefit for ultralytics's latest release,a `Transpose` op is added to the Yolov8 model,while make v8 and v5 has the same output shape.Therefore,you can inference your yolov5/v7/v8 via this project.
## Exporting YOLOv8 Models 📦
To export YOLOv8 models, use the following Python script:
@ -33,6 +37,17 @@ Alternatively, you can use the following command for exporting the model in the
yolo export model=yolov8n.pt opset=12 simplify=True dynamic=False format=onnx imgsz=640,640
```
## Exporting YOLOv8 FP16 Models 📦
```python
import onnx
from onnxconverter_common import float16
model = onnx.load(R'YOUR_ONNX_PATH')
model_fp16 = float16.convert_float_to_float16(model)
onnx.save(model_fp16, R'YOUR_FP16_ONNX_PATH')
```
## Download COCO.yaml file 📂
In order to run example, you also need to download coco.yaml. You can download the file manually from [here](https://raw.githubusercontent.com/ultralytics/ultralytics/main/ultralytics/cfg/datasets/coco.yaml)
@ -79,16 +94,15 @@ make
## Usage 🚀
```c++
// CPU inference
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, 0.1, 0.5, false};
// GPU inference
DCSP_INIT_PARAM params{ model_path, YOLO_ORIGIN_V8, {imgsz_w, imgsz_h}, 0.1, 0.5, true};
// Load your image
cv::Mat img = cv::imread(img_path);
// Init Inference Session
char* ret = yoloDetector->CreateSession(params);
ret = yoloDetector->RunSession(img, res);
//change your param as you like
//Pay attention to your device and the onnx model type(fp32 or fp16)
DL_INIT_PARAM params;
params.rectConfidenceThreshold = 0.1;
params.iouThreshold = 0.5;
params.modelPath = "yolov8n.onnx";
params.imgSize = { 640, 640 };
params.cudaEnable = true;
params.modelType = YOLO_DETECT_V8;
yoloDetector->CreateSession(params);
Detector(yoloDetector);
```
This repository should also work for YOLOv5, which needs a permute operator for the output of the YOLOv5 model, but this has not been implemented yet.