---
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: 0.5665829145728644
    - name: F1
      type: f1
      value:
        f1: 0.5549950963329491
---

<!-- 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: 1.1801
- Accuracy: {'accuracy': 0.5665829145728644}
- F1: {'f1': 0.5549950963329491}

## 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                          |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:---------------------------:|
| 1.3412        | 1.0   | 796  | 1.1931          | {'accuracy': 0.4547738693467337}  | {'f1': 0.31154642522501674} |
| 1.1149        | 2.0   | 1592 | 1.0845          | {'accuracy': 0.4886934673366834}  | {'f1': 0.40829339044510005} |
| 1.0531        | 3.0   | 2388 | 1.1650          | {'accuracy': 0.48304020100502515} | {'f1': 0.38992060973001436} |
| 0.917         | 4.0   | 3184 | 0.9950          | {'accuracy': 0.5452261306532663}  | {'f1': 0.50281030200465}    |
| 0.8633        | 5.0   | 3980 | 1.0152          | {'accuracy': 0.5552763819095478}  | {'f1': 0.511332789082197}   |
| 0.7747        | 6.0   | 4776 | 1.0201          | {'accuracy': 0.5703517587939698}  | {'f1': 0.523154780871296}   |
| 0.7133        | 7.0   | 5572 | 1.0345          | {'accuracy': 0.5640703517587939}  | {'f1': 0.5198008328503952}  |
| 0.659         | 8.0   | 6368 | 1.0702          | {'accuracy': 0.5785175879396985}  | {'f1': 0.5460580312777853}  |
| 0.5943        | 9.0   | 7164 | 1.1634          | {'accuracy': 0.5734924623115578}  | {'f1': 0.5501266468657362}  |
| 0.5699        | 10.0  | 7960 | 1.1801          | {'accuracy': 0.5665829145728644}  | {'f1': 0.5549950963329491}  |


### Framework versions

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