ZahraAlipour/LiteHand-YOLOv8

AI model by ZahraAlipour

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LiteHand-YOLO is a lightweight, attention-enhanced YOLOv8n-based model for single-class hand detection. LiteHand-YOLO modifies the YOLOv8n architecture with attention modules. The project aims to reduce model complexity while maintaining hand-detection performance. The model uses a single detection class (nc: 1). Refer to the source repository for the architecture configuration and implementation. The source repository reports the following results: The repository also reports a CPU benchmark of 8.83 FPS and 113.22 ms latency on an Intel Core i7-6700 system with 8 GB RAM, using its stated 640 × 480, 300-frame test. These values are reported results from the project, not an independent reproduction. Results can vary with the dataset, hardware, software versions, and evaluation procedure. Consult the source repository for the benchmark implementation and comparison details. Before making this model public, complete this section. Do not publish the checkpoint publicly until you have checked that the dataset's terms permit the intended release.…

ZahraAlipour/LiteHand-YOLOv8 on Hugging Face A short preview, not the full document.

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Model overview

ZahraAlipour/LiteHand-YOLOv8 is a task-unspecified model repository published by ZahraAlipour on Hugging Face. A library was not reported.

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Model facts

Task: Not reported
Library: Not reported
Recent downloads (30 days): 0
Cumulative likes: 0
Hugging Face trending score: 0
Reported safetensors parameters: Not reported
Architecture: Not reported
License: Not reported — check the model card
Access: Not gated by Hugging Face
Created: 2026-10-10T06:36:32.000Z
Last modified: 2026-10-10T06:36:32.000Z

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