
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-base-patch16-224-Trial008-YEL_STEM3

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: 0.0916
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7743        | 1.0   | 1    | 0.8267          | 0.3636   |
| 0.7964        | 2.0   | 2    | 0.7547          | 0.3636   |
| 0.6369        | 3.0   | 3    | 0.6399          | 0.7273   |
| 0.5344        | 4.0   | 4    | 0.5082          | 0.9091   |
| 0.4342        | 5.0   | 5    | 0.4664          | 0.9091   |
| 0.3056        | 6.0   | 6    | 0.2145          | 0.9091   |
| 0.257         | 7.0   | 7    | 0.1395          | 0.9091   |
| 0.2064        | 8.0   | 8    | 0.1990          | 0.9091   |
| 0.2609        | 9.0   | 9    | 0.0916          | 1.0      |
| 0.1758        | 10.0  | 10   | 0.0321          | 1.0      |
| 0.1152        | 11.0  | 11   | 0.0256          | 1.0      |
| 0.1343        | 12.0  | 12   | 0.0413          | 1.0      |
| 0.0955        | 13.0  | 13   | 0.0319          | 1.0      |
| 0.0723        | 14.0  | 14   | 0.0112          | 1.0      |
| 0.13          | 15.0  | 15   | 0.0073          | 1.0      |
| 0.1918        | 16.0  | 16   | 0.0057          | 1.0      |
| 0.2469        | 17.0  | 17   | 0.0052          | 1.0      |
| 0.1001        | 18.0  | 18   | 0.0051          | 1.0      |
| 0.1331        | 19.0  | 19   | 0.0039          | 1.0      |
| 0.1511        | 20.0  | 20   | 0.0031          | 1.0      |
| 0.0956        | 21.0  | 21   | 0.0027          | 1.0      |
| 0.0952        | 22.0  | 22   | 0.0027          | 1.0      |
| 0.1679        | 23.0  | 23   | 0.0025          | 1.0      |
| 0.1075        | 24.0  | 24   | 0.0023          | 1.0      |
| 0.1507        | 25.0  | 25   | 0.0024          | 1.0      |
| 0.1267        | 26.0  | 26   | 0.0027          | 1.0      |
| 0.1141        | 27.0  | 27   | 0.0030          | 1.0      |
| 0.0767        | 28.0  | 28   | 0.0031          | 1.0      |
| 0.1746        | 29.0  | 29   | 0.0029          | 1.0      |
| 0.1101        | 30.0  | 30   | 0.0032          | 1.0      |
| 0.1632        | 31.0  | 31   | 0.0036          | 1.0      |
| 0.1346        | 32.0  | 32   | 0.0038          | 1.0      |
| 0.1024        | 33.0  | 33   | 0.0038          | 1.0      |
| 0.1198        | 34.0  | 34   | 0.0037          | 1.0      |
| 0.1217        | 35.0  | 35   | 0.0033          | 1.0      |
| 0.1433        | 36.0  | 36   | 0.0030          | 1.0      |
| 0.1255        | 37.0  | 37   | 0.0029          | 1.0      |
| 0.1369        | 38.0  | 38   | 0.0027          | 1.0      |
| 0.091         | 39.0  | 39   | 0.0026          | 1.0      |
| 0.1318        | 40.0  | 40   | 0.0025          | 1.0      |
| 0.0964        | 41.0  | 41   | 0.0025          | 1.0      |
| 0.1164        | 42.0  | 42   | 0.0024          | 1.0      |
| 0.0935        | 43.0  | 43   | 0.0023          | 1.0      |
| 0.0564        | 44.0  | 44   | 0.0022          | 1.0      |
| 0.1136        | 45.0  | 45   | 0.0021          | 1.0      |
| 0.1306        | 46.0  | 46   | 0.0021          | 1.0      |
| 0.0757        | 47.0  | 47   | 0.0021          | 1.0      |
| 0.0475        | 48.0  | 48   | 0.0020          | 1.0      |
| 0.1455        | 49.0  | 49   | 0.0020          | 1.0      |
| 0.1892        | 50.0  | 50   | 0.0020          | 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-Trial008-YEL_STEM3", "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