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
| license: apache-2.0 | |
| tags: | |
| - pose-estimation | |
| - vitpose | |
| - mlx | |
| - mlx-swift | |
| - on-device | |
| - apple-silicon | |
| - keypoint-detection | |
| library_name: mlx | |
| base_model: usyd-community/vitpose-base-simple | |
| # ViTPose base-simple β MLX | |
| ViTPose (`vitpose-base-simple`) converted to **MLX** for on-device human pose | |
| estimation on Apple Silicon. Weights are float16. | |
| Built for **[MLXPose](https://github.com/NazarKozak/MLXPose)** β a native MLX Swift | |
| ViTPose implementation. The Swift forward pass is numerically verified against the | |
| Hugging Face reference (heatmaps `max|Ξ|=1.5e-6`, decoded keypoints `max 3e-5 px`). | |
| - Backbone: plain ViT-base (12 layers, dim 768), patch 16, input 256Γ192. | |
| - Head: simple decoder β 17 COCO keypoint heatmaps (64Γ48). | |
| - Conversion: [`convert_vitpose_to_mlx.py`](https://github.com/NazarKozak/MLXPose/blob/main/scripts/convert_vitpose_to_mlx.py). | |
| ## Files | |
| - `weights.safetensors` β MLX float16 weights. | |
| - `config.json` β original ViTPose config. | |
| ## License | |
| Apache-2.0. Pretrained weights derive from COCO/MPII training data β review dataset | |
| terms for your use case. | |