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
File size: 2,542 Bytes
e2f3a68 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | {
"schema_version": "medmlx.reference.v1",
"model": "flexray",
"date": "2026-10-12",
"case": {
"dataset": "NLM Montgomery County CXR",
"case_id": "MCUCXR_0001_0",
"input_sha256": "754d3ef3986e8682624f2e1c2cd14aacd3a503869f965a38235b6b07465e12da"
},
"original": {
"code": "https://github.com/VictorButoi/FleXray",
"version_or_commit": "bf6d01426fd3ae012aff9253ec9c9952c58bc960",
"device": "cpu",
"precision": "float32",
"checkpoint_sha256": "716ce3a8fb2a6abe22b2f2188fd596ddc4642b273fb0d336d629448154fc6d24",
"command": "<paper-repo>/work/reference/flexray/.venv/bin/python -I -B <paper-repo>/benchmarks/reference/flexray.py"
},
"mlx": {
"run_record": "results/runs/flexray.json",
"output_path": "<paper-repo>/work/runs/flexray/out/source_image_label.png",
"output_sha256": "b15043cce30dd30b470f153c7722f903405965aa04010258e8b039a258bd57a9",
"additional_outputs": {
"probabilities": {
"path": "<paper-repo>/work/runs/flexray/out/probabilities.npy",
"sha256": "7e2c354963f329e6bf3001586d732b8ff6dd1e02f12c0f32bbe898b7cdd39f75"
}
}
},
"comparison": {
"metric": "minimum_per_label_dice",
"value": 0.9999704176268988,
"per_label": {
"0": {
"dice": 0.9999866929450898,
"original_voxels": 13564053,
"mlx_voxels": 13564414
},
"1": {
"dice": 0.9999704176268988,
"original_voxels": 6101787,
"mlx_voxels": 6101426
}
},
"threshold": 0.99,
"differing_voxel_count": 361,
"probabilities_diagnostic": {
"cosine_similarity": 0.9999999999978441,
"max_abs_diff": 1.3709068298339844e-05
},
"result": "pass"
},
"notes": "Unmodified hash-admitted original UNet, preprocessing and InferenceRunner; flux0375 only, lungs channel 59, sigmoid, threshold 0.5, no TTA. The PNG source-grid export follows the package's original fixture recipe: threshold, crop padding, then nearest resize. Dice includes both binary labels; all 61 probability channels are also compared as a diagnostic. No source_sha256 exists in the run record: it records the original released safetensors directly; member_safetensors_sha256 is its original-source identity. The checkpoint directory moved under research/radnn. Raw outputs, source archive, dependency versions and log: work/reference/flexray/. Montgomery image remains local under its recorded restriction. One-case reproducibility comparison; no accuracy or performance qualification.",
"failure": null
}
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