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name: medmnist description: "Use MedMNIST for standardized 2D and 3D biomedical image dataset loading, local NPZ inspection, safe export, and task-aware evaluation workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

MedMNIST operating skill

Use this skill when a task names MedMNIST or asks for standardized biomedical image benchmark datasets, MedMNIST+ resolutions, .npz split files, 2D/3D PyTorch dataset classes, no-PyTorch array access, standard export, or AUC/ACC evaluation. This is an operating guide for the public medmnist package, not a clinical decision aid and not the external paper-experiment repository.

Start here

1. Run the safe package diagnostic in the install overview or use scripts/check_install.py --help and scripts/check_install.py before claiming the package or its dependencies are ready.
2. Identify the exact dataset flag, task type, split, image size, root, and whether the input is an official NPZ or a local synthetic fixture. Inspect medmnist.INFO[flag] before interpreting labels.
3. Follow exactly one focused route:

User intent	Read next
Install/import, select a subset, inspect metadata, load 2D/3D data, use splits/sizes/transforms/mmap/RGB, or read NPZ without torch	dataset-loading
Compute metrics, prepare score arrays, parse standard result CSVs, export PNG/GIF/CSV, create montages, or use save/evaluate	evaluation-and-export

4. Keep a separate writable data root and output directory. Do not download all subsets, delete a default root, run the development-only broad test loop, or mix real medical data with a synthetic fixture without labeling the source.
5. For a current-checkout question, read repository provenance first. If the commit, package metadata, or public entry points differ, request a repo-skill refresh rather than trusting stale details.

Installation and baseline check

The public package is installed with:

python -m pip install medmnist
python -c "import medmnist; print(medmnist.__version__)"

The inspected baseline is 3.0.2. Its documented runtime requirements include NumPy, pandas, scikit-learn, scikit-image, tqdm, Pillow, Fire, PyTorch, and torchvision. The selected MedMNIST workflows need only CPU behavior; no CUDA device is required. For a read-only version/import/registry check, run:

python scripts/check_install.py

Read the shared API overview for package object relationships and the minimal environment contract. Read shared troubleshooting for import, dependency, root, network, data, and medical-use boundaries.

Operating boundaries

• Use the official Zenodo distribution when obtaining data and preserve the published checksum when validating a download. download=True is an intentional network action, not a harmless import check.
• root must exist before dataset/evaluator construction. Prefer an explicit project-local or temporary root instead of relying on ~/.medmnist.
• Always record the exact flag, split, size, task, label shape, and whether the file is official or synthetic in a handoff.
• MedMNIST is a benchmark/data API and is not intended for clinical use. Respect the per-dataset license; DermaMNIST is CC BY-NC 4.0 while the other listed subsets are documented as CC BY 4.0.
• Do not infer training, weights, or paper-reproduction workflows from this package. The external experiments repository is outside this skill's source contract.