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Fully End-to-End YOLO: Merging Resize, Core Model, and NMS into a Single ONNX File!
Tired of dealing with messy preprocessing (resize/normalization) and heavy Python/C++ post-processing (NMS) outside of your model? I’ve wrapped the entire pipeline—from raw uint8 images to final bounding boxes—into one single ONNX file.
This approach delivers true plug-and-play, end-to-end deployment for web frontends, edge devices, or localized runtimes.
Key Features
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Zero-Dependency Pipeline: No more manual bounding box parsing or custom NMS C++ modules. The model accepts a raw
[H, W, 3]uint8 image and outputs final, scaled coordinates directly. -
Mathematical NMS: Uses a batch-and-class isolated IoU graph lookup table (LUT) implemented directly in PyTorch operators—fully compatible with ONNX export.
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Fully Dynamic Input: Supports dynamic image resolutions, automatically scaling coordinates back to the original input size.
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Seamless Merging: Combines three independent stages (Preprocess, Core YOLO, and Post-NMS) into a unified computational graph using
onnx.compose.