---
tags:
- generated_from_trainer
datasets:
- preprocessed1024_config
metrics:
- accuracy
- f1
model-index:
- name: convnext-mlo-512-breat_composition-ordinal
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: preprocessed1024_config
      type: preprocessed1024_config
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value:
        accuracy: 0.12185929648241206
    - name: F1
      type: f1
      value:
        f1: 0.05431131019036954
---

<!-- 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. -->

# convnext-mlo-512-breat_composition-ordinal

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: 0.0275
- Accuracy: {'accuracy': 0.12185929648241206}
- F1: {'f1': 0.05431131019036954}

## 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                           |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------:|:----------------------------:|
| 0.0233        | 1.0   | 796  | 0.0269          | {'accuracy': 0.042085427135678394} | {'f1': 0.02019288728149488}  |
| 0.0202        | 2.0   | 1592 | 0.0250          | {'accuracy': 0.09610552763819095}  | {'f1': 0.043839541547277934} |
| 0.0183        | 3.0   | 2388 | 0.0248          | {'accuracy': 0.07977386934673367}  | {'f1': 0.036940081442699245} |
| 0.0163        | 4.0   | 3184 | 0.0259          | {'accuracy': 0.17022613065326633}  | {'f1': 0.07273215244229736}  |
| 0.0144        | 5.0   | 3980 | 0.0258          | {'accuracy': 0.146356783919598}    | {'f1': 0.06383561643835617}  |
| 0.0117        | 6.0   | 4776 | 0.0249          | {'accuracy': 0.0992462311557789}   | {'f1': 0.045142857142857144} |
| 0.0105        | 7.0   | 5572 | 0.0256          | {'accuracy': 0.10238693467336683}  | {'f1': 0.04643874643874644}  |
| 0.0084        | 8.0   | 6368 | 0.0261          | {'accuracy': 0.12185929648241206}  | {'f1': 0.05431131019036954}  |
| 0.0071        | 9.0   | 7164 | 0.0270          | {'accuracy': 0.10238693467336683}  | {'f1': 0.04643874643874644}  |
| 0.0065        | 10.0  | 7960 | 0.0275          | {'accuracy': 0.12185929648241206}  | {'f1': 0.05431131019036954}  |


### Framework versions

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