---
license: apache-2.0
base_model: facebook/convnext-tiny-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: convnext-tiny-224-finetuned
  results: []
---

<!-- 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-tiny-224-finetuned

This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0987
- Logloss: 1.0987
- Accuracy: {'accuracy': 0.5808823529411765}

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Logloss | Accuracy                         |
|:-------------:|:-------:|:----:|:---------------:|:-------:|:--------------------------------:|
| No log        | 0.9412  | 8    | 1.1618          | 1.1618  | {'accuracy': 0.5882352941176471} |
| 0.9966        | 2.0     | 17   | 1.1464          | 1.1464  | {'accuracy': 0.5845588235294118} |
| 0.9619        | 2.9412  | 25   | 1.1380          | 1.1380  | {'accuracy': 0.5772058823529411} |
| 0.9133        | 4.0     | 34   | 1.1249          | 1.1249  | {'accuracy': 0.5735294117647058} |
| 0.8577        | 4.9412  | 42   | 1.1043          | 1.1043  | {'accuracy': 0.5882352941176471} |
| 0.8092        | 6.0     | 51   | 1.0899          | 1.0899  | {'accuracy': 0.5955882352941176} |
| 0.8092        | 6.9412  | 59   | 1.0927          | 1.0927  | {'accuracy': 0.5845588235294118} |
| 0.772         | 8.0     | 68   | 1.0834          | 1.0834  | {'accuracy': 0.5845588235294118} |
| 0.7128        | 8.9412  | 76   | 1.0730          | 1.0730  | {'accuracy': 0.5845588235294118} |
| 0.6902        | 10.0    | 85   | 1.0788          | 1.0788  | {'accuracy': 0.5882352941176471} |
| 0.645         | 10.9412 | 93   | 1.0649          | 1.0649  | {'accuracy': 0.5808823529411765} |
| 0.591         | 12.0    | 102  | 1.0631          | 1.0631  | {'accuracy': 0.5845588235294118} |
| 0.578         | 12.9412 | 110  | 1.0764          | 1.0764  | {'accuracy': 0.5845588235294118} |
| 0.578         | 14.0    | 119  | 1.0658          | 1.0658  | {'accuracy': 0.5808823529411765} |
| 0.5377        | 14.9412 | 127  | 1.0674          | 1.0674  | {'accuracy': 0.5808823529411765} |
| 0.516         | 16.0    | 136  | 1.0798          | 1.0798  | {'accuracy': 0.5808823529411765} |
| 0.4974        | 16.9412 | 144  | 1.0804          | 1.0804  | {'accuracy': 0.5808823529411765} |
| 0.4649        | 18.0    | 153  | 1.0818          | 1.0818  | {'accuracy': 0.5955882352941176} |
| 0.4422        | 18.9412 | 161  | 1.0742          | 1.0742  | {'accuracy': 0.5808823529411765} |
| 0.4222        | 20.0    | 170  | 1.0862          | 1.0862  | {'accuracy': 0.5735294117647058} |
| 0.4222        | 20.9412 | 178  | 1.0935          | 1.0935  | {'accuracy': 0.5772058823529411} |
| 0.4136        | 22.0    | 187  | 1.0907          | 1.0907  | {'accuracy': 0.5772058823529411} |
| 0.4006        | 22.9412 | 195  | 1.0967          | 1.0967  | {'accuracy': 0.5735294117647058} |
| 0.4032        | 24.0    | 204  | 1.0931          | 1.0931  | {'accuracy': 0.5772058823529411} |
| 0.3805        | 24.9412 | 212  | 1.1000          | 1.1000  | {'accuracy': 0.5845588235294118} |
| 0.3654        | 26.0    | 221  | 1.1078          | 1.1078  | {'accuracy': 0.5661764705882353} |
| 0.3654        | 26.9412 | 229  | 1.0959          | 1.0959  | {'accuracy': 0.5772058823529411} |
| 0.3678        | 28.0    | 238  | 1.0986          | 1.0986  | {'accuracy': 0.5808823529411765} |
| 0.3789        | 28.2353 | 240  | 1.0987          | 1.0987  | {'accuracy': 0.5808823529411765} |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1