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

This model is a fine-tuned version of [VIT](https://huggingface.co/VIT) on the Mammogram V1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1157
- Accuracy: 0.9625
- Precision: 0.9745
- Recall: 0.9625
- F1: 0.9682

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4204        | 1.0   | 1112 | 0.1572          | 0.9797   | 0.9740    | 0.9797 | 0.9767 |
| 0.3987        | 2.0   | 2224 | 0.2308          | 0.9253   | 0.9745    | 0.9253 | 0.9482 |
| 0.2347        | 3.0   | 3336 | 0.1360          | 0.9516   | 0.9737    | 0.9516 | 0.9622 |
| 0.1283        | 4.0   | 4448 | 0.1255          | 0.9564   | 0.9743    | 0.9564 | 0.9649 |
| 0.1304        | 5.0   | 5560 | 0.1157          | 0.9625   | 0.9745    | 0.9625 | 0.9682 |


### Framework versions

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