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    "text": "I've been building panlabel — a fast Rust CLI that converts between dataset annotation formats — and I'm a few releases behind on sharing updates. Here's a quick catch-up.\n\nv0.3.0 added Hugging Face ImageFolder support, including remote Hub import via --hf-repo. You can point it at a HF dataset repo and it figures out the layout (metadata.jsonl, parquet shards, even zip-style splits that contain YOLO or COCO inside).\n\nv0.4.0 overhauled auto-detection so it gives you concrete evidence when format detection is ambiguous (\"found YOLO labels/ but missing images/\") instead of a generic error. Also added Docker images.\n\nv0.5.0 brought split-aware YOLO reading for Roboflow/Ultralytics Hub exports and conversion report explainability — every adapter now explains its deterministic policies so you know exactly what happens to your data.\n\nv0.6.0 is the big one. Five new format adapters:\n\n→ LabelMe JSON (per-image, with polygon-to-bbox envelope)\n→ Apple CreateML JSON (center-based coords)\n→ KITTI (autonomous driving standard — 15 fields per line)\n→ VGG Image Annotator (VIA) JSON\n→ RetinaNet Keras CSV\n\nThat brings panlabel to 13 supported formats with full read, write, and auto-detection. Also in v0.6.0: YOLO confidence token support, dry-run mode for previewing conversions, and content-based CSV detection.\n\nSingle binary, no Python dependencies. Install via pip, brew, cargo, or grab a pre-built binary from GitHub releases.\n\nThis is the kind of project I enjoy just steadily plodding away at — ticking off one format at a time until every common object detection annotation format is covered. Still sticking with detection bboxes for now, but the format list keeps growing.\n\n#ObjectDetection #Rust #MachineLearning #ComputerVision #OpenSource",
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