| Extended by | Abduragim Shtanchaev |

| Compatibility | OpenCV >= 4.9.0 |

Running pre-trained YOLO model in OpenCV

Deploying pre-trained models is a common task in machine learning, particularly when working with

hardware that does not support certain frameworks like PyTorch. This guide provides a comprehensive

overview of exporting pre-trained YOLO family models from PyTorch and deploying them using OpenCV's

DNN framework. For demonstration purposes, we will focus on the [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX/blob/main)

model, but the methodology applies to other supported models.

@note Currently, OpenCV supports the following YOLO models:

- [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX/blob/main),

- [YoloNas](https://github.com/Deci-AI/super-gradients/tree/master),

- [YOLOv8](https://github.com/ultralytics/ultralytics/tree/main),

- [YOLOv7](https://github.com/WongKinYiu/yolov7/tree/main),

- [YOLOv6](https://github.com/meituan/YOLOv6/blob/main),

- [YOLOv5](https://github.com/ultralytics/yolov5),

- [YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4).

This support includes pre and post-processing routines specific to these models. While other older

version of YOLO are also supported by OpenCV in Darknet format, they are out of the scope of this tutorial.

Assuming that we have successfully trained YOLOX model, the subsequent step involves exporting and

running this model with OpenCV. There are several critical considerations to address before

proceeding with this process. Let's delve into these aspects.

Understanding the nature of inputs and outputs associated with YOLO family detectors is pivotal.

These detectors, akin to most Deep Neural Networks (DNN), typically exhibit variation in input

sizes contingent upon the model's scale.

| Model Scale | Input Size |

|--------------|--------------|

| Small Models ^{[1](https://github.com/Megvii-BaseDetection/YOLOX/tree/main#standard-models)}| 416x416 |

| Midsize Models ^{[2](https://github.com/Megvii-BaseDetection/YOLOX/tree/main#standard-models)}| 640x640 |

| Large Models ^{[3](https://github.com/meituan/YOLOv6/tree/main#benchmark)}| 1280x1280 |

This table provides a quick reference to understand the different input dimensions commonly used in

various YOLO models inputs. These are standard input shapes. Make sure you use input size that you

trained model with, if it is differed from from the size mentioned in the table.

The next critical element in the process involves understanding the specifics of image pre-processing

for YOLO detectors. While the fundamental pre-processing approach remains consistent across the YOLO

family, there are subtle yet crucial differences that must be accounted for to avoid any degradation

in performance. Key among these are the `resize type` and the `padding value` applied post-resize.

For instance, the [YOLOX model](https://github.com/Megvii-BaseDetection/YOLOX/blob/ac58e0a5e68e57454b7b9ac822aced493b553c53/yolox/data/data_augment.py#L142)

utilizes a `LetterBox` resize method and a padding value of `114.0`. It is imperative to ensure that

these parameters, along with the normalization constants, are appropriately matched to the model being

exported.

Regarding the model's output, it typically takes the form of a tensor with dimensions [BxNxC+5] or

[BxNxC+4], where 'B' represents the batch size, 'N' denotes the number of anchors, and 'C' signifies

the number of classes (for instance, 80 classes if the model is trained on the COCO dataset).

The additional 5 in the former tensor structure corresponds to the objectness score (obj), confidence

score (conf), and the bounding box coordinates (cx, cy, w, h). Notably, the YOLOv8 model's output

is shaped as [BxNxC+4], where there is no explicit objectness score, and the object score is directly

inferred from the class score. For the YOLOX model, specifically, it is also necessary to incorporate

anchor points to rescale predictions back to the image domain. This step will be integrated into

the ONNX graph, a process that we will detail further in the subsequent sections.

Now that we know know the parameters of the pre-precessing we can go on and export the model from

Pytorch to ONNX graph. Since in this tutorial we are using YOLOX as our sample model, lets use its

export for demonstration purposes (the process is identical for the rest of the YOLO detectors).

To exporting YOLOX we can just use [export script](https://github.com/Megvii-BaseDetection/YOLOX/blob/ac58e0a5e68e57454b7b9ac822aced493b553c53/tools/export_onnx.py). Particularly we need following commands:

@code{.bash}

git clone https://github.com/Megvii-BaseDetection/YOLOX.git

cd YOLOX

wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth # download pre-trained weights

python3 -m tools.export_onnx --output-name yolox_s.onnx -n yolox-s -c yolox_s.pth --decode_in_inference

**NOTE:** Here `--decode_in_inference` is to include anchor box creation in the ONNX graph itself.

It sets [this value](https://github.com/Megvii-BaseDetection/YOLOX/blob/ac58e0a5e68e57454b7b9ac822aced493b553c53/yolox/models/yolo_head.py#L210C16-L210C39)

to `True`, which subsequently includes anchor generation function.

Below we demonstrated the minimal version of the export script (which could be used for models other

than YOLOX) in case it is needed. However, usually each YOLO repository has predefined export script.

@code{.py}

import onnx

import torch

from onnxsim import simplify

# load the model state dict

ckpt = torch.load(ckpt_file, map_location="cpu")

model.load_state_dict(ckpt)

# prepare dummy input

dummy_input = torch.randn(args.batch_size, 3, exp.test_size[0], exp.test_size[1])

#export the model

torch.onnx._export(

model,

dummy_input,

"yolox.onnx",

input_names=["input"],

output_names=["output"],

dynamic_axes={"input": {0: 'batch'},

"output": {0: 'batch'}})

# use onnx-simplifier to reduce reduent model.

onnx_model = onnx.load(args.output_name)

model_simp, check = simplify(onnx_model)

assert check, "Simplified ONNX model could not be validated"

onnx.save(model_simp, args.output_name)

Once we have our ONNX graph of the model, we just simply can run with OpenCV's sample. To that we need to make sure:

1. OpenCV is build with -DBUILD_EXAMLES=ON flag.

2. Navigate to the OpenCV's `build` directory

3. Run the following command:

@code{.cpp}

./bin/example_dnn_yolo_detector --input=<path_to_your_input_file> \

--classes=<path_to_class_names_file> \

--thr=<confidence_threshold> \

--nms=<non_maximum_suppression_threshold> \

--mean=<mean_normalization_value> \

--scale=<scale_factor> \

--yolo=<yolo_model_version> \

--padvalue=<padding_value> \

--paddingmode=<padding_mode> \

--backend=<computation_backend> \

--target=<target_computation_device>

- --input: File path to your input image or video. If omitted, it will capture frames from a camera.

- --classes: File path to a text file containing class names for object detection.

- --thr: Confidence threshold for detection (e.g., 0.5).

- --nms: Non-maximum suppression threshold (e.g., 0.4).

- --mean: Mean normalization value (e.g., 0.0 for no mean normalization).

- --scale: Scale factor for input normalization (e.g., 1.0).

- --yolo: YOLO model version (e.g., YOLOv3, YOLOv4, etc.).

- --padvalue: Padding value used in pre-processing (e.g., 114.0).

- --paddingmode: Method for handling image resizing and padding. Options: 0 (resize without extra processing), 1 (crop after resize), 2 (resize with aspect ratio preservation).

- --backend: Selection of computation backend (0 for automatic, 1 for Halide, 2 for OpenVINO, etc.).

- --target: Selection of target computation device (0 for CPU, 1 for OpenCL, etc.).

- --device: Camera device number (0 for default camera). If `--input` is not provided camera with index 0 will used by default.

Here `mean`, `scale`, `padvalue`, `paddingmode` should exactly match those that we discussed

in pre-processing section in order for the model to match result in PyTorch

To demonstrate how to run OpenCV YOLO samples without your own pretrained model, follow these instructions:

1. Ensure Python is installed on your platform.

2. Confirm that OpenCV is built with the `-DBUILD_EXAMPLES=ON` flag.

Run the YOLOX detector(with default values):

@code{.sh}

git clone https://github.com/opencv/opencv_extra.git

cd opencv_extra/testdata/dnn

python download_models.py yolox_s_inf_decoder

cd ..

export OPENCV_TEST_DATA_PATH=$(pwd)

cd <build directory of OpenCV>

./bin/example_dnn_yolo_detector

This will execute the YOLOX detector with your camera. For YOLOv8 (for instance), follow these additional steps:

@code{.sh}

cd opencv_extra/testdata/dnn

python download_models.py yolov8

cd ..

export OPENCV_TEST_DATA_PATH=$(pwd)

cd <build directory of OpenCV>

./bin/example_dnn_yolo_detector --model=onnx/models/yolov8n.onnx --yolo=yolov8 --mean=0.0 --scale=0.003921568627 --paddingmode=2 --padvalue=144.0 --thr=0.5 --nms=0.4 --rgb=0

Sometimes there is a need to make some custom adjustments in the inference pipeline. With OpenCV DNN

module this is also quite easy to achieve. Below we will outline the sample implementation details:

@snippet samples/dnn/yolo_detector.cpp read_net

- Read image and pre-process it:

@snippet samples/dnn/yolo_detector.cpp preprocess_params

@snippet samples/dnn/yolo_detector.cpp preprocess_call

@snippet samples/dnn/yolo_detector.cpp preprocess_call_func

- Inference:

@snippet samples/dnn/yolo_detector.cpp forward_buffers

@snippet samples/dnn/yolo_detector.cpp forward

- Post-Processing

All post-processing steps are implemented in function `yoloPostProcess`. Please pay attention,

that NMS step is not included into onnx graph. Sample uses OpenCV function for it.

@snippet samples/dnn/yolo_detector.cpp postprocess

- Draw predicted boxes