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---
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
|