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# ryan_model2

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: 0.7611
- Accuracy: 0.6954

## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1685        | 0.05  | 100  | 1.1497          | 0.5270   |
| 0.93          | 0.09  | 200  | 1.0087          | 0.5966   |
| 0.8567        | 0.14  | 300  | 1.1028          | 0.5607   |
| 0.9407        | 0.19  | 400  | 0.9464          | 0.6150   |
| 0.9323        | 0.23  | 500  | 0.9542          | 0.6165   |
| 0.8375        | 0.28  | 600  | 0.8750          | 0.6431   |
| 1.0136        | 0.32  | 700  | 0.9315          | 0.6077   |
| 1.0557        | 0.37  | 800  | 0.9124          | 0.6268   |
| 0.7398        | 0.42  | 900  | 0.8843          | 0.6384   |
| 0.7579        | 0.46  | 1000 | 0.8965          | 0.6338   |
| 0.8872        | 0.51  | 1100 | 0.8624          | 0.6444   |
| 0.889         | 0.56  | 1200 | 0.9395          | 0.6213   |
| 0.8863        | 0.6   | 1300 | 0.8294          | 0.6645   |
| 0.6924        | 0.65  | 1400 | 0.8748          | 0.6431   |
| 0.7978        | 0.7   | 1500 | 0.8624          | 0.6497   |
| 0.764         | 0.74  | 1600 | 0.8861          | 0.6389   |
| 0.7159        | 0.79  | 1700 | 0.8413          | 0.6504   |
| 0.7912        | 0.84  | 1800 | 0.8729          | 0.6376   |
| 0.8232        | 0.88  | 1900 | 0.7743          | 0.6776   |
| 0.7108        | 0.93  | 2000 | 0.8804          | 0.6361   |
| 0.7324        | 0.97  | 2100 | 0.7950          | 0.6743   |
| 0.5353        | 1.02  | 2200 | 0.9441          | 0.6285   |
| 0.5808        | 1.07  | 2300 | 0.8193          | 0.6670   |
| 0.5451        | 1.11  | 2400 | 0.9586          | 0.6258   |
| 0.5201        | 1.16  | 2500 | 0.8172          | 0.6745   |
| 0.5294        | 1.21  | 2600 | 0.8386          | 0.6713   |
| 0.5595        | 1.25  | 2700 | 0.8296          | 0.6622   |
| 0.488         | 1.3   | 2800 | 0.8134          | 0.6758   |
| 0.5577        | 1.35  | 2900 | 0.8476          | 0.6763   |
| 0.4918        | 1.39  | 3000 | 0.8701          | 0.6640   |
| 0.5549        | 1.44  | 3100 | 0.9492          | 0.6371   |
| 0.6421        | 1.48  | 3200 | 0.8248          | 0.6763   |
| 0.5423        | 1.53  | 3300 | 0.7948          | 0.6838   |
| 0.5654        | 1.58  | 3400 | 0.7697          | 0.6836   |
| 0.5051        | 1.62  | 3500 | 0.8189          | 0.6818   |
| 0.4797        | 1.67  | 3600 | 0.7995          | 0.6833   |
| 0.5645        | 1.72  | 3700 | 0.8068          | 0.6796   |
| 0.4865        | 1.76  | 3800 | 0.8162          | 0.6808   |
| 0.502         | 1.81  | 3900 | 0.7947          | 0.6859   |
| 0.5164        | 1.86  | 4000 | 0.8085          | 0.6801   |
| 0.4822        | 1.9   | 4100 | 0.7611          | 0.6954   |
| 0.4777        | 1.95  | 4200 | 0.8203          | 0.6823   |
| 0.5423        | 2.0   | 4300 | 0.7761          | 0.6896   |
| 0.2653        | 2.04  | 4400 | 0.8337          | 0.7004   |
| 0.2646        | 2.09  | 4500 | 0.9206          | 0.6911   |
| 0.2782        | 2.13  | 4600 | 0.9539          | 0.6924   |
| 0.2032        | 2.18  | 4700 | 0.8932          | 0.6999   |
| 0.2837        | 2.23  | 4800 | 0.9431          | 0.6914   |
| 0.3152        | 2.27  | 4900 | 0.9220          | 0.7022   |
| 0.4516        | 2.32  | 5000 | 0.9568          | 0.6904   |
| 0.2151        | 2.37  | 5100 | 0.9406          | 0.7075   |
| 0.2932        | 2.41  | 5200 | 0.9687          | 0.6904   |
| 0.3352        | 2.46  | 5300 | 0.9500          | 0.7024   |
| 0.2447        | 2.51  | 5400 | 0.9382          | 0.6982   |
| 0.371         | 2.55  | 5500 | 0.9664          | 0.6916   |
| 0.1435        | 2.6   | 5600 | 1.0167          | 0.6853   |
| 0.2489        | 2.65  | 5700 | 0.9714          | 0.6941   |
| 0.2744        | 2.69  | 5800 | 1.0301          | 0.6899   |
| 0.2139        | 2.74  | 5900 | 1.0056          | 0.6861   |
| 0.2953        | 2.78  | 6000 | 0.9620          | 0.7014   |
| 0.2672        | 2.83  | 6100 | 0.9992          | 0.6919   |
| 0.2384        | 2.88  | 6200 | 1.0486          | 0.6987   |
| 0.2759        | 2.92  | 6300 | 1.0390          | 0.6896   |
| 0.2098        | 2.97  | 6400 | 1.0927          | 0.6818   |
| 0.0427        | 3.02  | 6500 | 1.0394          | 0.6957   |
| 0.0582        | 3.06  | 6600 | 1.0990          | 0.7057   |
| 0.0494        | 3.11  | 6700 | 1.1617          | 0.6999   |
| 0.1249        | 3.16  | 6800 | 1.2645          | 0.6929   |
| 0.0786        | 3.2   | 6900 | 1.2227          | 0.7002   |
| 0.0728        | 3.25  | 7000 | 1.2736          | 0.6977   |
| 0.1319        | 3.29  | 7100 | 1.3114          | 0.6969   |
| 0.041         | 3.34  | 7200 | 1.3003          | 0.7022   |
| 0.0174        | 3.39  | 7300 | 1.3064          | 0.6997   |
| 0.0911        | 3.43  | 7400 | 1.3231          | 0.7009   |
| 0.0187        | 3.48  | 7500 | 1.3725          | 0.6979   |
| 0.1097        | 3.53  | 7600 | 1.3446          | 0.7034   |
| 0.1588        | 3.57  | 7700 | 1.3276          | 0.7060   |
| 0.0598        | 3.62  | 7800 | 1.3460          | 0.7029   |
| 0.0418        | 3.67  | 7900 | 1.3614          | 0.7027   |
| 0.0522        | 3.71  | 8000 | 1.3581          | 0.7062   |
| 0.0932        | 3.76  | 8100 | 1.3598          | 0.7072   |
| 0.092         | 3.81  | 8200 | 1.3826          | 0.7039   |
| 0.0199        | 3.85  | 8300 | 1.3744          | 0.7057   |
| 0.0251        | 3.9   | 8400 | 1.3652          | 0.7065   |
| 0.1199        | 3.94  | 8500 | 1.3612          | 0.7102   |
| 0.0629        | 3.99  | 8600 | 1.3649          | 0.7100   |


### 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