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# 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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0128
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
- Top1 Accuracy: 1.0
- Error Rate: 0.0

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|
| 1.6288        | 1.0   | 38   | 1.6005          | 0.2133    | 0.2697 | 0.2043 | 0.2371   | 0.2697        | 0.7629     |
| 1.6059        | 2.0   | 76   | 1.5802          | 0.2384    | 0.2763 | 0.2243 | 0.2473   | 0.2763        | 0.7527     |
| 1.5808        | 3.0   | 114  | 1.5570          | 0.2778    | 0.3026 | 0.2595 | 0.2744   | 0.3026        | 0.7256     |
| 1.5555        | 4.0   | 152  | 1.5291          | 0.3831    | 0.375  | 0.3491 | 0.3511   | 0.375         | 0.6489     |
| 1.5232        | 5.0   | 190  | 1.4933          | 0.4252    | 0.4408 | 0.4154 | 0.4147   | 0.4408        | 0.5853     |
| 1.4784        | 6.0   | 228  | 1.4484          | 0.5076    | 0.5197 | 0.4926 | 0.4972   | 0.5197        | 0.5028     |
| 1.4242        | 7.0   | 266  | 1.3902          | 0.6857    | 0.6382 | 0.6307 | 0.6249   | 0.6382        | 0.3751     |
| 1.3586        | 8.0   | 304  | 1.3186          | 0.7728    | 0.7171 | 0.7166 | 0.7134   | 0.7171        | 0.2866     |
| 1.276         | 9.0   | 342  | 1.2236          | 0.8547    | 0.8026 | 0.8109 | 0.8060   | 0.8026        | 0.1940     |
| 1.1778        | 10.0  | 380  | 1.1122          | 0.8899    | 0.8553 | 0.8609 | 0.8601   | 0.8553        | 0.1399     |
| 1.0543        | 11.0  | 418  | 0.9839          | 0.9064    | 0.8947 | 0.8958 | 0.9005   | 0.8947        | 0.0995     |
| 0.921         | 12.0  | 456  | 0.8418          | 0.9541    | 0.9539 | 0.9537 | 0.9575   | 0.9539        | 0.0425     |
| 0.773         | 13.0  | 494  | 0.6935          | 0.9624    | 0.9605 | 0.9605 | 0.9652   | 0.9605        | 0.0348     |
| 0.6204        | 14.0  | 532  | 0.5515          | 0.9688    | 0.9671 | 0.9672 | 0.9708   | 0.9671        | 0.0292     |
| 0.4835        | 15.0  | 570  | 0.4146          | 0.9704    | 0.9671 | 0.9676 | 0.9697   | 0.9671        | 0.0303     |
| 0.3641        | 16.0  | 608  | 0.3043          | 0.9805    | 0.9803 | 0.9802 | 0.9830   | 0.9803        | 0.0170     |
| 0.2706        | 17.0  | 646  | 0.2247          | 0.9805    | 0.9803 | 0.9802 | 0.9830   | 0.9803        | 0.0170     |
| 0.1998        | 18.0  | 684  | 0.1705          | 0.9873    | 0.9868 | 0.9868 | 0.9889   | 0.9868        | 0.0111     |
| 0.1446        | 19.0  | 722  | 0.1271          | 0.9937    | 0.9934 | 0.9934 | 0.9944   | 0.9934        | 0.0056     |
| 0.1106        | 20.0  | 760  | 0.1047          | 0.9873    | 0.9868 | 0.9868 | 0.9889   | 0.9868        | 0.0111     |
| 0.0872        | 21.0  | 798  | 0.0780          | 0.9937    | 0.9934 | 0.9934 | 0.9944   | 0.9934        | 0.0056     |
| 0.0614        | 22.0  | 836  | 0.0739          | 0.9873    | 0.9868 | 0.9868 | 0.9889   | 0.9868        | 0.0111     |
| 0.0491        | 23.0  | 874  | 0.0517          | 0.9937    | 0.9934 | 0.9934 | 0.9944   | 0.9934        | 0.0056     |
| 0.0365        | 24.0  | 912  | 0.0401          | 0.9871    | 0.9868 | 0.9868 | 0.9878   | 0.9868        | 0.0122     |
| 0.0255        | 25.0  | 950  | 0.0336          | 0.9937    | 0.9934 | 0.9934 | 0.9944   | 0.9934        | 0.0056     |
| 0.0212        | 26.0  | 988  | 0.0377          | 0.9873    | 0.9868 | 0.9868 | 0.9889   | 0.9868        | 0.0111     |
| 0.0175        | 27.0  | 1026 | 0.0195          | 0.9937    | 0.9934 | 0.9934 | 0.9944   | 0.9934        | 0.0056     |
| 0.0125        | 28.0  | 1064 | 0.0214          | 0.9936    | 0.9934 | 0.9934 | 0.9933   | 0.9934        | 0.0067     |
| 0.0155        | 29.0  | 1102 | 0.0128          | 1.0       | 1.0    | 1.0    | 1.0      | 1.0           | 0.0        |
| 0.0104        | 30.0  | 1140 | 0.0159          | 0.9937    | 0.9934 | 0.9934 | 0.9944   | 0.9934        | 0.0056     |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1

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