YOLO-Craft

说明与用途

YOLO‑Craft 提供轻量级脚本化工作流，用于 YOLOv8 模型的微调与部署，包括主动学习、交互式标注、边缘设备导出和可视化评估。

问题

Fine‑tuning YOLOv8 models for custom computer vision projects was hindered by heavy MLOps tools that bundled large frameworks, complex wrappers, or required cloud setups.

结果

YOLO‑Craft enables lightweight, end‑to‑end fine‑tuning and deployment of YOLOv8 models without heavy frameworks, improving workflow efficiency and device compatibility.

工作流

• 1. Run watcher.py to capture low‑confidence frames from a stream or webcam.
• 2. Use labler.py to draw bounding boxes interactively, switching target classes with hotkeys and handling background‑only frames.
• 3. Train or fine‑tune YOLOv8 on the labeled data.
• 4. Export the trained model with export_model.py to TensorRT, TFLite, CoreML, or ONNX for deployment on edge devices.
• 5. Generate visual metrics and performance reports with report.py.

证据

When fine‑tuning YOLOv8 models for custom computer vision projects, I kept running into the same issue: most MLOps tools are packed with heavy frameworks, complex wrappers, or forced cloud setups.

来源

Active Learning Loop (watcher.py): Automatically captures low-confidence frames from streams or webcams to continuously improve your model where it struggles most.

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Multi-Class Labeler (labler.py): Interactive bounding box drawing with quick hotkeys (like using l to switch target classes) and built-in support for background-only frames.

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Universal Edge Export (export_model.py): Exports trained models directly to TensorRT, TFLite, CoreML, or ONNX for real-time deployment on devices like Raspberry Pi or NVIDIA Jetson.

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Visual Evaluation & Reports (report.py): Generates automated visual metrics and before-and-after performance comparisons.

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