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chest-xray-pneumonia-detector
ausjahk/chest-xray-pneumonia-detector
chest-xray-pneumonia-detector is a image classification model from ausjahk. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- resnet
- Access
- Public
- Created
- Aug 30, 2026
- Updated
- Aug 31, 2026
- SHA
- a97f46dd
Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
chest-xray-pneumonia-detector
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.7072
- Accuracy: 0.8269
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2971 | 1.0 | 326 | 0.4242 | 0.8381 |
| 0.1803 | 2.0 | 652 | 0.7687 | 0.7837 |
| 0.1434 | 3.0 | 978 | 0.5564 | 0.8381 |
| 0.1007 | 4.0 | 1304 | 0.6760 | 0.8237 |
| 0.1053 | 5.0 | 1630 | 0.7072 | 0.8269 |
Framework versions
- Transformers 5.15.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Image Classification
- Library
- transformers
- Type
- resnet
- License
- apache-2.0
- Created
- Aug 30, 2026
- Updated
- Aug 31, 2026