---
tags:
- generated_from_trainer
datasets:
- preprocessed1024_config
metrics:
- accuracy
- f1
model-index:
- name: vit-mlo-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: 0.4667085427135678
    - name: F1
      type: f1
      value:
        f1: 0.3786054240333243
---

<!-- 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-mlo-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: 1.0864
- Accuracy: {'accuracy': 0.4667085427135678}
- F1: {'f1': 0.3786054240333243}

## 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                          |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:---------------------------:|
| 1.103         | 1.0   | 796  | 1.0452          | {'accuracy': 0.4748743718592965}  | {'f1': 0.21465076660988078} |
| 1.0596        | 2.0   | 1592 | 1.0433          | {'accuracy': 0.4748743718592965}  | {'f1': 0.21465076660988078} |
| 1.0547        | 3.0   | 2388 | 1.0361          | {'accuracy': 0.4748743718592965}  | {'f1': 0.21465076660988078} |
| 1.047         | 4.0   | 3184 | 1.0395          | {'accuracy': 0.46796482412060303} | {'f1': 0.25128840471066954} |
| 1.0524        | 5.0   | 3980 | 1.0331          | {'accuracy': 0.4648241206030151}  | {'f1': 0.298317360340153}   |
| 1.0268        | 6.0   | 4776 | 1.0224          | {'accuracy': 0.47675879396984927} | {'f1': 0.23426509831984135} |
| 1.0043        | 7.0   | 5572 | 1.0609          | {'accuracy': 0.417713567839196}   | {'f1': 0.3663405670841817}  |
| 0.982         | 8.0   | 6368 | 1.0521          | {'accuracy': 0.44221105527638194} | {'f1': 0.3650005046420297}  |
| 0.9315        | 9.0   | 7164 | 1.0473          | {'accuracy': 0.47738693467336685} | {'f1': 0.3727220695970696}  |
| 0.9319        | 10.0  | 7960 | 1.0864          | {'accuracy': 0.4667085427135678}  | {'f1': 0.3786054240333243}  |


### Framework versions

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