---
base_model: swinv2
tags:
- image-classification
- breast cancer
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: swinV2-CBIS
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# swinV2-CBIS

This model is a fine-tuned version of [swinv2](https://huggingface.co/swinv2) on the CBIS-DDSM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6876
- Accuracy: 0.6085
- Precision: 0.3702
- Recall: 0.6085
- F1: 0.4604

## 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: 3e-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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7205        | 1.0   | 165  | 0.6842          | 0.6085   | 0.3702    | 0.6085 | 0.4604 |
| 0.6924        | 2.0   | 330  | 0.6847          | 0.6085   | 0.3702    | 0.6085 | 0.4604 |
| 0.6867        | 3.0   | 495  | 0.6876          | 0.6085   | 0.3702    | 0.6085 | 0.4604 |


### Framework versions

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