---
tags:
- generated_from_trainer
datasets:
- preprocessed1024_config
metrics:
- accuracy
- f1
model-index:
- name: vit-cc-512-birads
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: preprocessed1024_config
      type: preprocessed1024_config
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value:
        accuracy: 0.4943467336683417
    - name: F1
      type: f1
      value:
        f1: 0.3929699341372617
---

<!-- 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-cc-512-birads

This model is a fine-tuned version of [](https://huggingface.co/) on the preprocessed1024_config dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1133
- Accuracy: {'accuracy': 0.4943467336683417}
- F1: {'f1': 0.3929699341372617}

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                          | F1                          |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:---------------------------:|
| 1.1037        | 1.0   | 796  | 1.0357          | {'accuracy': 0.4748743718592965}  | {'f1': 0.21465076660988078} |
| 1.0588        | 2.0   | 1592 | 1.0446          | {'accuracy': 0.4623115577889447}  | {'f1': 0.33094476503399495} |
| 1.0486        | 3.0   | 2388 | 1.0408          | {'accuracy': 0.47361809045226133} | {'f1': 0.3313643442345453}  |
| 1.0288        | 4.0   | 3184 | 1.0186          | {'accuracy': 0.5050251256281407}  | {'f1': 0.3404676010455165}  |
| 1.0284        | 5.0   | 3980 | 1.0288          | {'accuracy': 0.5037688442211056}  | {'f1': 0.3406391773730375}  |
| 0.997         | 6.0   | 4776 | 1.0183          | {'accuracy': 0.5087939698492462}  | {'f1': 0.3539488153998284}  |
| 0.9682        | 7.0   | 5572 | 1.0965          | {'accuracy': 0.4566582914572864}  | {'f1': 0.3695106771946128}  |
| 0.9313        | 8.0   | 6368 | 1.0554          | {'accuracy': 0.4962311557788945}  | {'f1': 0.38158088397057704} |
| 0.8938        | 9.0   | 7164 | 1.0930          | {'accuracy': 0.4943467336683417}  | {'f1': 0.38196414933207573} |
| 0.8697        | 10.0  | 7960 | 1.1133          | {'accuracy': 0.4943467336683417}  | {'f1': 0.3929699341372617}  |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1