from src.utils.hub import load_from_hub

model = load_from_hub("loihuynh/semisub-cxr-supervised-0.1")
model.eval()

# Run inference on a preprocessed image tensor (1, 3, 224, 224)
import torch
with torch.no_grad():
    logits = model(image_tensor)
    probabilities = torch.sigmoid(logits)

Training Details

Architecture: ResNet-50 backbone + Dropout + Linear head (6 classes)
Backbone: resnet50_imagenet (ImageNet pretrained)
Training setting: supervised
Labeled data ratio: 0.1 (10% of training set)
Seed: 42
Best epoch: 1

Dataset: CheXpert
Method: Supervised baseline
Loss: Binary Cross-Entropy (with masking for pseudo-labels)
Image size: 224x224

Citation

If you use this model, please cite the CheXpert dataset:

bibtex

@inproceedings{irvin2019chexpert,
  title={CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison},
  author={Irvin, Jeremy and others},
  booktitle={AAAI},
  year={2019}
}