---
tags:
- generated_from_trainer
datasets:
- preprocessed1024_config
metrics:
- accuracy
- f1
model-index:
- name: convnext-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: 0.5785175879396985
    - name: F1
      type: f1
      value:
        f1: 0.565251065728165
---

<!-- 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

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: 1.1521
- Accuracy: {'accuracy': 0.5785175879396985}
- F1: {'f1': 0.565251065728165}

## 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                          |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:---------------------------:|
| 1.3433        | 1.0   | 796  | 1.1893          | {'accuracy': 0.4566582914572864}  | {'f1': 0.32080438921262083} |
| 1.1242        | 2.0   | 1592 | 1.0867          | {'accuracy': 0.48555276381909546} | {'f1': 0.4061780745199038}  |
| 1.0569        | 3.0   | 2388 | 1.1587          | {'accuracy': 0.49120603015075376} | {'f1': 0.40970823779940124} |
| 0.9327        | 4.0   | 3184 | 0.9901          | {'accuracy': 0.5452261306532663}  | {'f1': 0.4885626990630958}  |
| 0.8723        | 5.0   | 3980 | 0.9824          | {'accuracy': 0.5728643216080402}  | {'f1': 0.5365052338942904}  |
| 0.7803        | 6.0   | 4776 | 1.0071          | {'accuracy': 0.571608040201005}   | {'f1': 0.5246756181464156}  |
| 0.7198        | 7.0   | 5572 | 1.0233          | {'accuracy': 0.5741206030150754}  | {'f1': 0.5405969058526473}  |
| 0.6589        | 8.0   | 6368 | 1.0902          | {'accuracy': 0.5816582914572864}  | {'f1': 0.5421523761661359}  |
| 0.6055        | 9.0   | 7164 | 1.0980          | {'accuracy': 0.5835427135678392}  | {'f1': 0.5601877104043351}  |
| 0.5722        | 10.0  | 7960 | 1.1521          | {'accuracy': 0.5785175879396985}  | {'f1': 0.565251065728165}   |


### Framework versions

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