---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- medical-imaging
- chest-xray
- tumor-detection
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-xray-tumor
  results: []
---

<!-- 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. -->

# vit-xray-tumor

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the chest-xray-tumor dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2989
- Accuracy: 0.9574

## 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: 1e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.5283        | 3.6765  | 125  | 0.2948          | 0.9606   |
| 0.516         | 7.3529  | 250  | 0.2843          | 0.9601   |
| 0.4878        | 11.0294 | 375  | 0.2756          | 0.9601   |
| 0.459         | 14.7059 | 500  | 0.2801          | 0.9601   |
| 0.4462        | 18.3824 | 625  | 0.2761          | 0.9595   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3