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mlx-community
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sapiens2-pose-0.4b-bf16

Keypoint Detection
MLX
Safetensors
sapiens2
sapiens
human-centric
pose
bf16
Model card Files
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 Community
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Add sapiens2-pose-0.4b-bf16 (bf16 MLX conversion of facebook/sapiens2-pose-0.4b)

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Files changed (5) hide show
  1. README.md +51 -0
  2. config.json +1353 -0
  3. model.safetensors +3 -0
  4. model.safetensors.index.json +335 -0
  5. preprocessor_config.json +29 -0
README.md ADDED
@@ -0,0 +1,51 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: other
3
+ license_name: sapiens2-license
4
+ license_link: https://github.com/facebookresearch/sapiens2/blob/main/LICENSE.md
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+ pipeline_tag: keypoint-detection
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+ library_name: mlx
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+ base_model: facebook/sapiens2-pose-0.4b
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+ tags:
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+ - sapiens
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+ - sapiens2
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+ - human-centric
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+ - pose
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+ - mlx
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+ - bf16
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+ ---
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+ # mlx-community/sapiens2-pose-0.4b-bf16
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+
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+ bf16 MLX conversion of [`facebook/sapiens2-pose-0.4b`](https://huggingface.co/facebook/sapiens2-pose-0.4b) (Meta's
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+ Sapiens2, ICLR 2026): 308-keypoint top-down pose heatmaps. Converted with mlx-vlm
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+ 0.7.0; the original float32 checkpoint is 2x this size.
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+
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+ What is in `model.safetensors` (0.85 GB):
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+
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+ - every parameter in bfloat16 (the reference runs inference in bf16 mixed
25
+ precision);
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+ - the q/k/v projections merged into one `wqkv` tensor per block, as the
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+ mlx-vlm Sapiens2 model expects.
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+
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+ Refer to the [original model card](https://huggingface.co/facebook/sapiens2-pose-0.4b) for the
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+ model description, intended use and license.
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+
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+ ## Use with mlx-vlm
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+
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+ ```bash
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+ pip install -U mlx-vlm
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+ ```
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+
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+ ```python
39
+ from mlx_vlm import load
40
+ from mlx_vlm.models.sapiens2.generate import Sapiens2Predictor, read_image
41
+
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+ model, _ = load("mlx-community/sapiens2-pose-0.4b-bf16")
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+ predictor = Sapiens2Predictor(model)
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+ # boxes: (N, 4) xyxy person boxes from a detector; defaults to the full image.
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+ output = predictor.infer(read_image("image.jpg"), boxes=boxes, flip_test=False)
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+ keypoints, scores = output["keypoints"], output["scores"] # (N, 308, 2), (N, 308)
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+ ```
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+
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+ Outputs are numpy arrays at the input resolution (dense tasks) or in
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+ source-image pixel coordinates (pose). See the mlx-vlm Sapiens2 README for
51
+ preprocessing details and the per-task output keys.
config.json ADDED
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