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# ryan_model314

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2532
- Na Accuracy: 0.947
- Ordinal Accuracy: 0.5952

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Na Accuracy | Ordinal Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:----------------:|
| 0.3042        | 0.16  | 100  | 0.3673          | 0.928       | 0.4671           |
| 0.2904        | 0.32  | 200  | 0.2977          | 0.933       | 0.5790           |
| 0.2648        | 0.48  | 300  | 0.2831          | 0.944       | 0.5940           |
| 0.3036        | 0.64  | 400  | 0.2776          | 0.949       | 0.5871           |
| 0.2656        | 0.8   | 500  | 0.2846          | 0.931       | 0.6101           |
| 0.2954        | 0.96  | 600  | 0.2532          | 0.947       | 0.5952           |
| 0.1991        | 1.12  | 700  | 0.2603          | 0.942       | 0.6078           |
| 0.1678        | 1.28  | 800  | 0.2905          | 0.942       | 0.6332           |
| 0.2514        | 1.44  | 900  | 0.2566          | 0.94        | 0.6090           |
| 0.2328        | 1.6   | 1000 | 0.2884          | 0.94        | 0.5617           |
| 0.1826        | 1.76  | 1100 | 0.2870          | 0.943       | 0.6044           |
| 0.2013        | 1.92  | 1200 | 0.2937          | 0.941       | 0.5905           |
| 0.0663        | 2.08  | 1300 | 0.2954          | 0.938       | 0.6251           |
| 0.1503        | 2.24  | 1400 | 0.3188          | 0.937       | 0.5986           |
| 0.0611        | 2.4   | 1500 | 0.3393          | 0.945       | 0.5998           |
| 0.0743        | 2.56  | 1600 | 0.3182          | 0.942       | 0.6482           |
| 0.0908        | 2.72  | 1700 | 0.3332          | 0.942       | 0.6482           |
| 0.1108        | 2.88  | 1800 | 0.3256          | 0.943       | 0.6459           |
| 0.0786        | 3.04  | 1900 | 0.3222          | 0.944       | 0.6540           |
| 0.043         | 3.2   | 2000 | 0.3501          | 0.941       | 0.6482           |
| 0.0472        | 3.36  | 2100 | 0.3455          | 0.943       | 0.6609           |
| 0.032         | 3.52  | 2200 | 0.3562          | 0.94        | 0.6517           |
| 0.0434        | 3.68  | 2300 | 0.3499          | 0.94        | 0.6597           |
| 0.0341        | 3.84  | 2400 | 0.3611          | 0.94        | 0.6482           |
| 0.0305        | 4.0   | 2500 | 0.3635          | 0.939       | 0.6609           |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

not applicable poor fair good great excellent