---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: vit-base-patch16-224-in21k-finetuned-hongrui_mammogram_v_1
  results: []
datasets:
- hongrui/mammogram_v_1
pipeline_tag: image-classification
---

<!-- 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-base-patch16-224-in21k-finetuned-hongrui_mammogram_v_1

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7419
- Accuracy: 0.6991
- F1: 0.6767
- Precision: 0.6830
- Recall: 0.6991

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8576        | 1.0   | 171  | 0.8431          | 0.6678   | 0.6067 | 0.7751    | 0.6678 |
| 0.8297        | 2.0   | 342  | 0.7965          | 0.6791   | 0.6182 | 0.6758    | 0.6791 |
| 0.8303        | 3.0   | 513  | 0.7872          | 0.6842   | 0.6360 | 0.6704    | 0.6842 |
| 0.7814        | 4.0   | 684  | 0.7717          | 0.6843   | 0.6597 | 0.6601    | 0.6843 |
| 0.7768        | 5.0   | 855  | 0.7694          | 0.6906   | 0.6544 | 0.6775    | 0.6906 |
| 0.7415        | 6.0   | 1026 | 0.7572          | 0.6962   | 0.6718 | 0.6764    | 0.6962 |
| 0.7351        | 7.0   | 1197 | 0.7549          | 0.6922   | 0.6569 | 0.6648    | 0.6922 |
| 0.7197        | 8.0   | 1368 | 0.7479          | 0.6986   | 0.6855 | 0.6926    | 0.6986 |
| 0.7087        | 9.0   | 1539 | 0.7445          | 0.6979   | 0.6697 | 0.6792    | 0.6979 |
| 0.6977        | 10.0  | 1710 | 0.7419          | 0.6991   | 0.6767 | 0.6830    | 0.6991 |

![Multi Class ROC Curve](https://cdn-uploads.huggingface.co/production/uploads/662eb39820de310d1558dd55/CToH2mNHE4zN6ihs3OqUz.png)

### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1