---
tags:
- generated_from_trainer
datasets:
- preprocessed1024_config
metrics:
- accuracy
- f1
model-index:
- name: vit-mlo-512-breat_composition
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: preprocessed1024_config
      type: preprocessed1024_config
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value:
        accuracy: 0.5791457286432161
    - name: F1
      type: f1
      value:
        f1: 0.5749067914290308
---

<!-- 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-breat_composition

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: 1.3123
- Accuracy: {'accuracy': 0.5791457286432161}
- F1: {'f1': 0.5749067914290308}

## 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                          |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:---------------------------:|
| 1.2679        | 1.0   | 796  | 1.0281          | {'accuracy': 0.5062814070351759} | {'f1': 0.38950358034816535} |
| 0.9805        | 2.0   | 1592 | 0.9240          | {'accuracy': 0.5672110552763819} | {'f1': 0.5273112700912543}  |
| 0.9167        | 3.0   | 2388 | 0.9608          | {'accuracy': 0.5477386934673367} | {'f1': 0.45736748568671376} |
| 0.8292        | 4.0   | 3184 | 0.8973          | {'accuracy': 0.5891959798994975} | {'f1': 0.5783349603036094}  |
| 0.7695        | 5.0   | 3980 | 1.0477          | {'accuracy': 0.5571608040201005} | {'f1': 0.5379432393338944}  |
| 0.6912        | 6.0   | 4776 | 0.9479          | {'accuracy': 0.585427135678392}  | {'f1': 0.5766494177636581}  |
| 0.61          | 7.0   | 5572 | 1.1280          | {'accuracy': 0.5703517587939698} | {'f1': 0.5560158679652624}  |
| 0.5591        | 8.0   | 6368 | 1.1866          | {'accuracy': 0.5741206030150754} | {'f1': 0.5541999644498281}  |
| 0.5021        | 9.0   | 7164 | 1.1537          | {'accuracy': 0.582286432160804}  | {'f1': 0.566315815243799}   |
| 0.4262        | 10.0  | 7960 | 1.3123          | {'accuracy': 0.5791457286432161} | {'f1': 0.5749067914290308}  |


### Framework versions

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