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Eric Rolph

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reacted to dronefreak's post with 👍 1 day ago
🚀 Excited to open-source the GWHD Wheat Head Detection Model Zoo on Hugging Face. This release includes: - 🤖 YOLOv8, YOLOv11, YOLOv26 and RF-DETR object detection models trained on GWHD (Global Wheat Head Dataset), spanning nano through x-large variants across both architecture families. - 🌾 Benchmarked on GWHD's dense, single-class wheat-head detection task — ~45 annotated heads per image on average, captured across multiple countries, genotypes, and growth stages, a genuinely hard small/dense-object setting. - 📊 Detailed model cards with mAP/precision/recall, per-class breakdowns, PR/F1 curves and confusion matrices (YOLO), qualitative detection showcases, and full training configurations for reproducibility. Headline numbers: - 🏆 Best mAP@50: 74.25% (YOLOv11x), 34.92% mAP@50:95, 83.37% precision. - ⚡ Best efficiency tradeoff: YOLOv26s hits 70.49% mAP@50 at just 22.8 GFLOPs (10.0M params) — within ~4 points of the top YOewer FLOPs (196.0 GFLOPs). The goal is to make benchmarking and experimenting with agricultural computer vision easier by providing ready-to-use pretrained checkpoints, all trained and evaluated under one shared pipeline (DetectionBench: https://github.com/dronefreak/DetectionBench). Full credit for the underlying dataset goes to Etienne David, Mario Serouart, Simon Madec, and the Global Wheat Head Detection 2020/2021) — this release is anunofficial, YOLO-ready reformatting of their work, not a new dataset. If you're working on precision at detection, or just want areproducible detector benchmark, I hope these resources are useful. 📦 Dataset: dronefreak/GWHD 🤖 Model Collection: https://huggingface.co/collections/dronefreak/gwhd-wheat-head-detection-model-zoo-6a7aea28b5431918cc46cec1 Feedback, bug reports, and contributions are always welcome.
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microsoft/VibeVoice-ASR-BitNet
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unsloth/Qwen3.6-35B-A3B-NVFP4
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