Instructions to use nazarkozak/vitpose-base-simple-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nazarkozak/vitpose-base-simple-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir vitpose-base-simple-mlx nazarkozak/vitpose-base-simple-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Upload README.md with huggingface_hub
Browse files
README.md
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---
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license: apache-2.0
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tags:
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- pose-estimation
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- vitpose
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- mlx
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- mlx-swift
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- on-device
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- apple-silicon
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- keypoint-detection
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library_name: mlx
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base_model: usyd-community/vitpose-base-simple
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---
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# ViTPose base-simple — MLX
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ViTPose (`vitpose-base-simple`) converted to **MLX** for on-device human pose
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estimation on Apple Silicon. Weights are float16.
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Built for **[MLXPose](https://github.com/NazarKozak/MLXPose)** — a native MLX Swift
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ViTPose implementation. The Swift forward pass is numerically verified against the
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Hugging Face reference (heatmaps `max|Δ|=1.5e-6`, decoded keypoints `max 3e-5 px`).
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- Backbone: plain ViT-base (12 layers, dim 768), patch 16, input 256×192.
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- Head: simple decoder → 17 COCO keypoint heatmaps (64×48).
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- Conversion: [`convert_vitpose_to_mlx.py`](https://github.com/NazarKozak/MLXPose/blob/main/scripts/convert_vitpose_to_mlx.py).
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## Files
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- `weights.safetensors` — MLX float16 weights.
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- `config.json` — original ViTPose config.
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## License
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Apache-2.0. Pretrained weights derive from COCO/MPII training data — review dataset
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terms for your use case.
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