Image Segmentation
MLX
Safetensors
English
native-mlx
apple-silicon
medical-imaging
x-ray
segmentation
noncommercial
research
Instructions to use MedMLX/flexray-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use MedMLX/flexray-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download MedMLX/flexray-mlx --local-dir flexray-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download native-run.json from MedMLX/flexray-mlx: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/MedMLX/flexray-mlx/resolve/main/native-run.json
- Command line
-
hf download hf://MedMLX/flexray-mlx/native-run.json
-
curl -L -o native-run.json https://huggingface.co/MedMLX/flexray-mlx/resolve/main/native-run.json
4.86 kB
| { | |
| "schema_version": "medmlx.run.v1", | |
| "model": "flexray", | |
| "configuration": "FleXray flux0375 single member, label 'lungs', threshold 0.5, no TTA", | |
| "date": "2026-10-08", | |
| "package": { | |
| "name": "flexray-mlx", | |
| "path": "<repositories>/flexray-mlx", | |
| "git_revision": "d768bb15d04ee677ecec0c469fd3170256b345df", | |
| "branch": "main", | |
| "dirty": false, | |
| "remote": "https://github.com/MedMLX/flexray-mlx" | |
| }, | |
| "environment": { | |
| "machine": "MacBook Pro (2021), Apple M1 Max, 10-core CPU, 24-core GPU, 64 GB", | |
| "macos": "27.0", | |
| "python": "3.12.12", | |
| "mlx_version": "0.32.3", | |
| "device": "gpu" | |
| }, | |
| "command": { | |
| "entry_point": "flexray_mlx.medmlx:run", | |
| "kwargs": { | |
| "image_path": "<home>/Documents/Codex/2026-10-08/https-drive-google-com-drive-folders/outputs/medmlx/datasets/qualification-2026-10-08/montgomery_cxr/CXR_png/MCUCXR_0001_0.png", | |
| "weights_path": "<repositories>/radnn/checkpoints/flexray", | |
| "output_dir": "<paper-repo>/work/runs/flexray/out", | |
| "label_name": "lungs", | |
| "overwrite": true | |
| }, | |
| "invocation": "cd <repositories>/flexray-mlx && .venv/bin/python <paper-repo>/work/runs/_harness/run_one.py flexray_mlx.medmlx <kwargs.json> <run_result.json>", | |
| "process": "fresh Python process per model" | |
| }, | |
| "checkpoint": { | |
| "path": "<repositories>/radnn/checkpoints/flexray", | |
| "member_safetensors_sha256": "716ce3a8fb2a6abe22b2f2188fd596ddc4642b273fb0d336d629448154fc6d24", | |
| "registry_sha256_match": true, | |
| "conversion": "released safetensors snapshot loaded directly" | |
| }, | |
| "inputs": [ | |
| { | |
| "role": "image", | |
| "path": "<home>/Documents/Codex/2026-10-08/https-drive-google-com-drive-folders/outputs/medmlx/datasets/qualification-2026-10-08/montgomery_cxr/CXR_png/MCUCXR_0001_0.png", | |
| "sha256": "754d3ef3986e8682624f2e1c2cd14aacd3a503869f965a38235b6b07465e12da", | |
| "dataset": "NLM Montgomery County CXR", | |
| "case_id": "MCUCXR_0001_0", | |
| "derivation": "original PNG", | |
| "figure_use": "restricted (NLM terms limit sharing outside the research group)" | |
| } | |
| ], | |
| "outputs": { | |
| "mask": { | |
| "path": "<paper-repo>/work/runs/flexray/out/source_image_label.png", | |
| "sha256": "b15043cce30dd30b470f153c7722f903405965aa04010258e8b039a258bd57a9" | |
| }, | |
| "display_source": { | |
| "path": "<paper-repo>/work/runs/flexray/out/source_image.png", | |
| "sha256": "df159b66ce277c081f77d55bf52274dad1d64a9976a47d20bb15fe612899b83f" | |
| }, | |
| "probabilities": { | |
| "path": "<paper-repo>/work/runs/flexray/out/probabilities.npy", | |
| "sha256": "7e2c354963f329e6bf3001586d732b8ff6dd1e02f12c0f32bbe898b7cdd39f75" | |
| }, | |
| "prediction": { | |
| "path": "<paper-repo>/work/runs/flexray/out/prediction.json", | |
| "sha256": "05fc9b5a3aa5a310bd722193c3af34838502fa652414c77be30be49fec1984e8" | |
| } | |
| }, | |
| "checks": [ | |
| { | |
| "name": "mask_reopens_on_source_grid", | |
| "result": "pass", | |
| "detail": { | |
| "mask": [ | |
| 4020, | |
| 4892 | |
| ], | |
| "source": [ | |
| 4020, | |
| 4892 | |
| ] | |
| } | |
| }, | |
| { | |
| "name": "mask_binary", | |
| "result": "pass", | |
| "detail": [ | |
| 0, | |
| 1 | |
| ] | |
| }, | |
| { | |
| "name": "probabilities_shape_finite_unit_interval", | |
| "result": "pass", | |
| "detail": { | |
| "shape": [ | |
| 61, | |
| 256, | |
| 256 | |
| ], | |
| "range": [ | |
| 0.0, | |
| 1.0 | |
| ] | |
| } | |
| }, | |
| { | |
| "name": "prediction_json_reopens", | |
| "result": "pass", | |
| "detail": null | |
| }, | |
| { | |
| "name": "lungs_overlap_manual_lung_masks", | |
| "result": "pass", | |
| "detail": { | |
| "lung Dice vs Montgomery manual left+right masks (informational)": 0.8435 | |
| } | |
| } | |
| ], | |
| "duration_s": 1.455, | |
| "timing_boundary": "wall clock around the single run() call: weight loading, preprocessing, MLX inference and export; interpreter start-up and package import excluded; one run, no warmup", | |
| "mlx_peak_gb": 1.86, | |
| "memory_definition": "mx.get_peak_memory() after mx.reset_peak_memory() immediately before run(); decimal GB", | |
| "panel": "paper/figures/runs/flexray.png", | |
| "status": "success", | |
| "failure_cause": null, | |
| "notes": "Panel shows a Montgomery radiograph; NLM terms restrict redistribution, so the panel is for internal review only until figure use is cleared.", | |
| "key_check": "61 sigmoid channels on 256x256; lung Dice 0.844 vs manual masks", | |
| "figure_use": "restricted", | |
| "figure_use_review": { | |
| "date": "2026-10-09", | |
| "source_url": "https://data.lhncbc.nlm.nih.gov/public/Tuberculosis-Chest-X-ray-Datasets/Montgomery-County-CXR-Set/MontgomerySet/NLM-MontgomeryCXRSet-ReadMe.pdf", | |
| "terms": "Do not share the dataset outside the research group/organization; attribute NLM in publications.", | |
| "panel_status": "Internal review only; existing restriction retained." | |
| } | |
| } | |