Registry / ai-ml / torchxrayvision

torchxrayvision

library1.4.0pypypi✓ verified 85d ago

A library for chest X-ray datasets and pretrained models in PyTorch. Version 1.4.0 released Sept 2024. Provides easy access to common datasets (ChestX-ray14, CheXpert, MIMIC-CXR, PadChest, etc.) and pre-trained models for classification, segmentation, and representation learning.

ai-ml

pip install torchxrayvision

github ↗package ↗

INSTALL

IMPORT

SIG · TORCHXRAYVISION

torchxrayvision

ai-mlpythonv1.4.0

harness data pending

Install & Compatibility

Where this runs

No compatibility data collected yet for this library.

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

torchxrayvision

✓ import torchxrayvision as xrv

✗ import xrv

Wrong because xrv is not a top-level module; torchxrayvision is the correct package name but the library often used with alias xrv.

datasets

✓ from torchxrayvision import datasets

✗ import torchxrayvision.datasets as datasets or from torchxrayvision.datasets import ...

datasets is a submodule within torchxrayvision, but the common pattern is to use xrv.datasets after import torchxrayvision as xrv.

models

✓ from torchxrayvision import models

Load a pretrained DenseNet on CheXpert labels, create a PadChest dataset, and run inference on a single image.

import torchxrayvision as xrv # Load a pretrained model model = xrv.models.DenseNet(weights='densenet121-res224-chex') dataset = xrv.datasets.PC_Dataset( imgpath='images', csvpath='labels.csv', views=['PA'] ) # Process an image import skimage.io img = skimage.io.imread('image.png') img = xrv.datasets.normalize(img, 255) # Run inference with torch.no_grad(): outputs = model(img.unsqueeze(0))

Debug

Known issues

breakingIn version 1.3.2, automatic upsampling was changed to use interpolate instead of skimage's resize, which may affect reproducibility for models expecting the old preprocessing.

fix

If you need the old behavior, manually apply skimage.transform.resize before passing images to models.

affects: >=1.3.2

gotchaThe dataset classes download large files (e.g., CheXpert is ~300GB). Ensure you have sufficient disk space and a stable internet connection.

fix

Use the download=False flag and manually place data in the expected location.

affects: all

gotchaModel weights are automatically downloaded to ~/.cache/torchxrayvision/ by default. This may fill up disk space over time.

fix

Set the environment variable XRV_DATA_DIR or use the cache_dir parameter to redirect.

affects: all

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'torchxrayvision'

Package not installed or installed in wrong environment.

fix

Run 'pip install torchxrayvision' in the correct Python environment.

ValueError: unknown url: .../chexpert/CheXpert-v1.0-small.zip

The dataset URL has changed or the download link is broken. The remote server may be offline.

fix

Download the dataset manually from the original source and place it in the expected directory, then pass download=False.

RuntimeError: Image must be a numpy array or PyTorch tensor

Input image is of an unsupported type (e.g., PIL Image).

fix

Convert input to a numpy array using np.array(img) or use torch.from_numpy().

Upgrade

Version history

1.4.0latest on PyPI · released Sep 18, 2025

Audit

Dependencies

torchrequiredCore dependency for tensor operations and model inference

torchvisionrequiredUsed for transforms and dataset utilities

numpyrequiredImage array manipulation

skimagerequiredImage preprocessing (resize etc.)

pydicomoptionalDICOM file handling (optional, needed for some datasets)

Agent activity

13 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/mlmed/torchxrayvision ↗

packagehttps://pypi.org/project/torchxrayvision/ ↗