---
library_name: transformers
license: apache-2.0
base_model: facebook/convnext-tiny-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: convnext-tiny-224-finetuned-barkley
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.9936145510835913
    - name: Recall
      type: recall
      value: 0.993421052631579
    - name: F1
      type: f1
      value: 0.993419541966282
    - name: Accuracy
      type: accuracy
      value: 0.9939393939393939
---

<!-- 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-barkley

This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0794
- Precision: 0.9936
- Recall: 0.9934
- F1: 0.9934
- Accuracy: 0.9939
- Top1 Accuracy: 0.9934
- Error Rate: 0.0061

## 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy | Top1 Accuracy | Error Rate |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|
| 1.576         | 1.0   | 38   | 1.5660          | 0.3007    | 0.3684 | 0.2952 | 0.3479   | 0.3684        | 0.6521     |
| 1.5469        | 2.0   | 76   | 1.5353          | 0.3141    | 0.4079 | 0.3215 | 0.3854   | 0.4079        | 0.6146     |
| 1.5081        | 3.0   | 114  | 1.4782          | 0.5684    | 0.4671 | 0.3961 | 0.4436   | 0.4671        | 0.5564     |
| 1.4278        | 4.0   | 152  | 1.3718          | 0.7088    | 0.6053 | 0.5840 | 0.5866   | 0.6053        | 0.4134     |
| 1.2938        | 5.0   | 190  | 1.1909          | 0.8582    | 0.8355 | 0.8378 | 0.8290   | 0.8355        | 0.1710     |
| 1.0696        | 6.0   | 228  | 0.9353          | 0.9243    | 0.9211 | 0.9215 | 0.9205   | 0.9211        | 0.0795     |
| 0.789         | 7.0   | 266  | 0.6347          | 0.9680    | 0.9671 | 0.9673 | 0.9691   | 0.9671        | 0.0309     |
| 0.506         | 8.0   | 304  | 0.3910          | 0.9750    | 0.9737 | 0.9739 | 0.9752   | 0.9737        | 0.0248     |
| 0.2876        | 9.0   | 342  | 0.2126          | 0.9808    | 0.9803 | 0.9802 | 0.9814   | 0.9803        | 0.0186     |
| 0.1722        | 10.0  | 380  | 0.1409          | 0.9809    | 0.9803 | 0.9799 | 0.9818   | 0.9803        | 0.0182     |
| 0.1082        | 11.0  | 418  | 0.0794          | 0.9936    | 0.9934 | 0.9934 | 0.9939   | 0.9934        | 0.0061     |
| 0.0715        | 12.0  | 456  | 0.0577          | 0.9936    | 0.9934 | 0.9934 | 0.9939   | 0.9934        | 0.0061     |
| 0.0492        | 13.0  | 494  | 0.0440          | 0.9872    | 0.9868 | 0.9867 | 0.9879   | 0.9868        | 0.0121     |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1