rai-check-dl
Deep learning audits for image classification, medical imaging, and scientific AI.
Features
- Image classification accuracy and per-class recall checks
- Robustness checks for recorded or callback-generated transformation predictions
- Built-in brightness, contrast, horizontal-flip, and Gaussian-noise transformations
- Grad-CAM heatmaps with PyTorch hooks or TensorFlow
GradientTape - Medical imaging checks for patient leakage across splits and site-level accuracy bias
- Segmentation Dice, IoU, mask-quality, and subgroup checks
- Object-detection mAP, IoU-threshold, class-coverage, and subgroup checks
- DICOM metadata, scanner/protocol bias, near-duplicate leakage, calibration, and patient aggregation
- Grad-CAM transformation stability and optional localization-overlap checks
Python API
from rai_audit.dl import ImageClassificationAudit
report = ImageClassificationAudit(
y_true=y_true,
y_pred=y_pred,
transformed_predictions={"sensor_noise": noisy_predictions},
persist=False,
).run()
To evaluate transformations directly, pass images and a predictor callback.
CLI
Audit recorded predictions from CSV:
rai-check dl run --data predictions.csv --task image --format html
Medical imaging CSV audits can add --patient-id, --split, and --site.
Examples
1 maintainer
Metadata
1 maintainer
Release files for rai-check-dl 0.0.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| rai_check_dl-0.0.1.tar.gz | 18.9 kB | With provenance | Details for rai_check_dl-0.0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| rai_check_dl-0.0.1-py3-none-any.whl | Python 3 | none | any | Details for rai_check_dl-0.0.1-py3-none-any.whl |
Total release size: 41.7 kB
Release files / rai_check_dl-0.0.1.tar.gz
| Download URL | rai_check_dl-0.0.1.tar.gz |
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| Size | 18.9 kB |
| Tags | Source |
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| Size | 22.8 kB |
| Tags | Python 3 |
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