Python File: https://github.com/namas191297/yolov8-segmentation-end2end-onnxruntime/blob/main/add_postprocessing_yolov8.py
In this post, I'll walk through the python file and the process of converting a YOLOv8 model from PyTorch to ONNX, incorporating essential pre-processing and post-processing steps directly into the ONNX model. This end-to-end approach simplifies deployment by encapsulating all necessary operations within a single model, eliminating the need for external processing scripts. Why Convert YOLOv8 to ONNX?
ONNX is an open format built to represent machine learning models. Converting YOLOv8 to ONNX offers several advantages:
Interoperability: Use the model across different frameworks and platforms.
Performance: Leverage optimized inference engines for faster predictions.
Deployment: Simplify deployment in environments with limited Python support.
1. Export YOLOv8 from PyTorch to ONNX
The first step involves exporting the trained YOLOv8 PyTorch model to the ONNX format. This can be achieved using the ultralytics library, which provides seamless tools for model export.
2. Integrate Pre-Processing into the ONNX Model
Pre-processing steps typically include resizing images, normalizing pixel values, and formatting data to match the model's input requirements. By embedding these steps into the ONNX model, we ensure that raw image inputs are automatically pre-processed before inference.
Key pre-processing operations:
Resize: Adjust image dimensions to match model input size.
Normalization: Scale pixel values to a specific range (e.g., 0-1).
Layout Conversion: Change image data layout from HWC (Height, Width, Channels) to CHW (Channels, Height, Width).
3. Incorporate Post-Processing within the ONNX Model
Post-processing involves operations like Non-Maximum Suppression (NMS) to filter overlapping bounding boxes and scaling bounding boxes back to the original image dimensions. Additionally, for segmentation tasks, mask processing is essential.
Key post-processing operations:
NMS: Eliminates redundant bounding boxes based on Intersection over Union (IoU) thresholds.
Bounding Box Scaling: Adjusts box coordinates to the original image size.
Mask Processing: Resizes and applies masks to the detected objects.
4. Finalize the ONNX Model
After integrating both pre-processing and post-processing steps, the final ONNX model is a self-contained pipeline. This model accepts raw images as input and outputs processed detection results, including bounding boxes and masks.
5. Testing the Final Model with Webcam Inference
To ensure the converted model works as expected, it's crucial to perform inference tests. Using a webcam, we can validate real-time detection and segmentation capabilities.
Converting a YOLOv8 PyTorch model to ONNX with integrated pre-processing and post-processing steps streamlines the deployment process, making it more efficient and platform-agnostic. By embedding these operations within the ONNX graph, developers can achieve real-time object detection and segmentation without relying on external scripts, facilitating smoother integration into various applications.
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