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# vit-base-patch16-224-Trial006_007_008-YEL_STEM1

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0453
- Accuracy: 1.0

## 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: 5e-05
- train_batch_size: 30
- eval_batch_size: 30
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 120
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7064        | 1.0   | 5    | 0.6605          | 0.5373   |
| 0.5838        | 2.0   | 10   | 0.5289          | 0.8358   |
| 0.4909        | 3.0   | 15   | 0.3967          | 0.8209   |
| 0.3317        | 4.0   | 20   | 0.2759          | 0.9104   |
| 0.2813        | 5.0   | 25   | 0.1820          | 0.9403   |
| 0.2948        | 6.0   | 30   | 0.1286          | 0.9552   |
| 0.2253        | 7.0   | 35   | 0.0453          | 1.0      |
| 0.2125        | 8.0   | 40   | 0.0408          | 1.0      |
| 0.1288        | 9.0   | 45   | 0.0177          | 1.0      |
| 0.1084        | 10.0  | 50   | 0.0265          | 1.0      |
| 0.1642        | 11.0  | 55   | 0.0994          | 0.9403   |
| 0.149         | 12.0  | 60   | 0.0316          | 0.9851   |
| 0.1315        | 13.0  | 65   | 0.0325          | 0.9851   |
| 0.1101        | 14.0  | 70   | 0.1090          | 0.9701   |
| 0.1101        | 15.0  | 75   | 0.0094          | 1.0      |
| 0.0702        | 16.0  | 80   | 0.0070          | 1.0      |
| 0.1184        | 17.0  | 85   | 0.0634          | 0.9851   |
| 0.1506        | 18.0  | 90   | 0.0104          | 1.0      |
| 0.1027        | 19.0  | 95   | 0.0149          | 1.0      |
| 0.159         | 20.0  | 100  | 0.1021          | 0.9552   |
| 0.1205        | 21.0  | 105  | 0.0085          | 1.0      |
| 0.1511        | 22.0  | 110  | 0.0248          | 0.9851   |
| 0.2228        | 23.0  | 115  | 0.0993          | 0.9552   |
| 0.1431        | 24.0  | 120  | 0.0373          | 0.9851   |
| 0.1489        | 25.0  | 125  | 0.0161          | 1.0      |
| 0.0799        | 26.0  | 130  | 0.0382          | 0.9851   |
| 0.1411        | 27.0  | 135  | 0.0071          | 1.0      |
| 0.1457        | 28.0  | 140  | 0.0047          | 1.0      |
| 0.1434        | 29.0  | 145  | 0.0069          | 1.0      |
| 0.0913        | 30.0  | 150  | 0.0032          | 1.0      |
| 0.1354        | 31.0  | 155  | 0.0042          | 1.0      |
| 0.1253        | 32.0  | 160  | 0.0061          | 1.0      |
| 0.1065        | 33.0  | 165  | 0.0039          | 1.0      |
| 0.1199        | 34.0  | 170  | 0.0023          | 1.0      |
| 0.1274        | 35.0  | 175  | 0.0037          | 1.0      |
| 0.1118        | 36.0  | 180  | 0.0100          | 1.0      |
| 0.1237        | 37.0  | 185  | 0.0053          | 1.0      |
| 0.1311        | 38.0  | 190  | 0.0028          | 1.0      |
| 0.1833        | 39.0  | 195  | 0.0026          | 1.0      |
| 0.0858        | 40.0  | 200  | 0.0033          | 1.0      |
| 0.1503        | 41.0  | 205  | 0.0049          | 1.0      |
| 0.0547        | 42.0  | 210  | 0.0037          | 1.0      |
| 0.1647        | 43.0  | 215  | 0.0037          | 1.0      |
| 0.1066        | 44.0  | 220  | 0.0061          | 1.0      |
| 0.1277        | 45.0  | 225  | 0.0083          | 1.0      |
| 0.0885        | 46.0  | 230  | 0.0083          | 1.0      |
| 0.1339        | 47.0  | 235  | 0.0081          | 1.0      |
| 0.0904        | 48.0  | 240  | 0.0073          | 1.0      |
| 0.079         | 49.0  | 245  | 0.0080          | 1.0      |
| 0.0788        | 50.0  | 250  | 0.0086          | 1.0      |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224-Trial006_007_008-YEL_STEM1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 1.0, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us