4.4 YOLOv8 Training and Deployment Pipeline

Last Version: 11/09/2025

This guide uses YOLOv8 as an example to explain the complete workflow: model training, quantization, and final inference deployment.

Model Training

Training Environment Setup

1. Clone the source code:

   git clone https://github.com/ultralytics/ultralytics

2. Install Dependencies:

   cd ultralytics
   pip install -e .

   If you encounter network issues downloading dependencies, you can use the Tsinghua mirror site for installation: https://pypi.tuna.tsinghua.edu.cn/simple/

Model Training and Export

The following code example demonstrates how to train a YOLOv8 model and export it to ONNX format:

from ultralytics import YOLO
#Load a COCO-pretrained YOLOv8n model
model = YOLO("yolov8n.yaml")
#Load a pretrained YOLO model (recommended for training)
model = YOLO("yolov8n.pt")
#Train the model using the 'coco8.yaml' dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100)
#Export the model to ONNX format
success = model.export(format="onnx",imgsz=(320,320),simplify=True,opset=13)

Key Parameter Explanation:

• yolov8n.yaml: Model configuration file.
• coco8.yaml: Training dataset configuration file. It must include two parts:
  • images: Directory containing training and validation set images.
  • labels: Directory containing the corresponding annotation files for the images.

Annotation File (labels) Format (Each line represents one target object):

<class_id> <x_center> <y_center> <width> <height>

Example:

0 0.445688 0.480615 0.075125 0.117295
0 0.640086 0.471742 0.0508281 0.0814344
20 0.643211 0.558852 0.129828 0.097623

• class_id: The target's class ID (integer).
• The subsequent four floating-point numbers represent the target's normalized bounding box coordinates in the order:
  • x_center: Normalized x-coordinate of the bounding box center.
  • y_center: Normalized y-coordinate of the bounding box center.
  • width: Normalized width of the bounding box.
  • height: Normalized height of the bounding box.

Note: For fine-tuning on your private dataset, start from a pretrained model and prepare your data following the coco8.yaml format.

Model Quantization (on x86 Platforms)

Important: Perform quantization on an x86 machine.

Quantization Tool Installation

1. Download the Quantization Tool: xquant-1.2.1 Download Link

2. Install the Tool:

   pip install xquant-1.2.1-py3-none-any.whl

3. Verify Installation:

   pip show xquant

   Successful installation output example:

Quantization Configuration

Calibration Dataset Download: Calibration Dataset

Example quantization configuration file:

{
  "model_parameters": {
    "onnx_model": "path/yolov8n.onnx",
    "working_dir": "yolov8",
    "skip_onnxsim": false
  },
  "calibration_parameters": {
    "input_parameters": [
      {
        "mean_value": [0, 0, 0],
        "std_value": [255, 255, 255],
        "color_format": "rgb",
        "data_list_path": "path/calib_list.txt"
      }
    ]
  },
  "quantization_parameters": {
    "truncate_var_names": [
      "/model.22/Reshape_output_0",
      "/model.22/Reshape_1_output_0",
      "/model.22/Reshape_2_output_0"
    ]
  }
}

Parameter Explanation:

• onnx_model: Path to the ONNX model to be quantized.
• mean_value and std_value: Image normalization parameters. Must match the configuration used during training.
• color_format: Color channel order (e.g., rgb or bgr).
• data_list_path: Path to the file listing calibration images.
• truncate_var_names: Names of nodes where quantization should be truncated (to prevent quantization error from affecting post-processing).

Calibration List File Example (calib_list.txt):

path/000000428562.jpg
path/000000000632.jpg
path/000000157756.jpg
path/000000044279.jpg

• path can be either a relative (relative to the config file location) or absolute path.

Note: It is recommended to use a subset of your training images for calibration to maintain consistent data distribution.

Truncation Node Identification

• The truncate_var_names parameter specifies nodes for truncation.
• YOLOv8 models include post-processing (coordinate decoding) logic.
• Truncating the model before these nodes helps prevent quantization errors from degrading post-processing accuracy.

Example:

"truncate_var_names": [
  "/model.22/Reshape_output_0",
  "/model.22/Reshape_1_output_0",
  "/model.22/Reshape_2_output_0"
]

Use the Netron visualizer to open your yolov8n.onnx model, inspect the graph, and confirm the names of the nodes that should be truncated or excluded from quantization.

Run Quantization

Run the following command to perform quantization:

python3 -m xquant --config path/yolov8_xquant_config.json

Upon successful quantization, the output model (e.g., yolov8n.q.onnx) will be generated.

Model Inference

1. Download Example Inference Code

   git clone https://gitee.com/bianbu/spacemit-demo.git

2. Navigate to the YOLOv8 Example Directory

   cd spacemit-demo/examples/CV/yolov8

3. Environment Setup (Example)

   # Navigate to the python subdirectory
   cd python
   sudo apt install python3-pip python3-venv
   
   # Create and activate a virtual environment
   python3 -m venv <venv_name>
   source name/bin/activate
   
   # Install Python dependencies
   pip install -r requirements.txt --index-url https://git.spacemit.com/api/v4/projects/33/packages/pypi/simple

4. Run Inference

   python test_yolov8.py --model <quantized_model_path> --image <test_image_path>

   Example Command:

   python test_yolov8.py --model ./yolov8n.q.onnx --image ./test_image.jpg

   Upon successful execution, the script will typically output a result image (e.g., result.jpg) and print the detected object information to the terminal.