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
metadata
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 — MLX
ViTPose (vitpose-base-simple) converted to MLX for on-device human pose
estimation on Apple Silicon. Weights are float16.
Built for MLXPose — a native MLX Swift
ViTPose implementation. The Swift forward pass is numerically verified against the
Hugging Face reference (heatmaps max|Δ|=1.5e-6, decoded keypoints max 3e-5 px).
- Backbone: plain ViT-base (12 layers, dim 768), patch 16, input 256×192.
- Head: simple decoder → 17 COCO keypoint heatmaps (64×48).
- Conversion:
convert_vitpose_to_mlx.py.
Files
weights.safetensors— MLX float16 weights.config.json— original ViTPose config.
License
Apache-2.0. Pretrained weights derive from COCO/MPII training data — review dataset terms for your use case.