---
base_model: VIT
tags:
- image-classification
- breast cancer
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Vit-CBIS
  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-CBIS

This model is a fine-tuned version of [VIT](https://huggingface.co/VIT) on the CBIS-DDSM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6894
- Accuracy: 0.6032
- Precision: 0.6313
- Recall: 0.6032
- F1: 0.6083

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.698         | 1.0   | 165  | 0.7030          | 0.4550   | 0.5327    | 0.4550 | 0.4356 |
| 0.692         | 2.0   | 330  | 0.6853          | 0.5714   | 0.5532    | 0.5714 | 0.5578 |
| 0.6999        | 3.0   | 495  | 0.6894          | 0.6032   | 0.6313    | 0.6032 | 0.6083 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1