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# 378A1_results_coord

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4924
- Accuracy: 0.8946

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2529        | 1.0   | 37   | 1.0701          | 0.6207   |
| 0.6771        | 2.0   | 74   | 0.6678          | 0.7687   |
| 0.4363        | 3.0   | 111  | 0.5622          | 0.8010   |
| 0.2884        | 4.0   | 148  | 0.3808          | 0.8690   |
| 0.2382        | 5.0   | 185  | 0.3492          | 0.8810   |
| 0.1213        | 6.0   | 222  | 0.3485          | 0.8895   |
| 0.1238        | 7.0   | 259  | 0.4012          | 0.8827   |
| 0.0878        | 8.0   | 296  | 0.4311          | 0.8639   |
| 0.0839        | 9.0   | 333  | 0.4417          | 0.8656   |
| 0.0406        | 10.0  | 370  | 0.3993          | 0.8844   |
| 0.0509        | 11.0  | 407  | 0.4922          | 0.8690   |
| 0.0347        | 12.0  | 444  | 0.4840          | 0.8741   |
| 0.033         | 13.0  | 481  | 0.4572          | 0.8827   |
| 0.0222        | 14.0  | 518  | 0.4376          | 0.8861   |
| 0.0197        | 15.0  | 555  | 0.4397          | 0.8912   |
| 0.0179        | 16.0  | 592  | 0.4464          | 0.8946   |
| 0.0167        | 17.0  | 629  | 0.4526          | 0.8946   |
| 0.0154        | 18.0  | 666  | 0.4588          | 0.8929   |
| 0.0148        | 19.0  | 703  | 0.4642          | 0.8929   |
| 0.0135        | 20.0  | 740  | 0.4691          | 0.8929   |
| 0.0131        | 21.0  | 777  | 0.4732          | 0.8946   |
| 0.0125        | 22.0  | 814  | 0.4776          | 0.8946   |
| 0.0119        | 23.0  | 851  | 0.4809          | 0.8946   |
| 0.0116        | 24.0  | 888  | 0.4841          | 0.8946   |
| 0.0112        | 25.0  | 925  | 0.4863          | 0.8946   |
| 0.0111        | 26.0  | 962  | 0.4885          | 0.8946   |
| 0.0108        | 27.0  | 999  | 0.4903          | 0.8946   |
| 0.0108        | 28.0  | 1036 | 0.4912          | 0.8946   |
| 0.0105        | 29.0  | 1073 | 0.4921          | 0.8946   |
| 0.0108        | 30.0  | 1110 | 0.4924          | 0.8946   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3

label_0 label_1 label_2 label_3