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# vit-base-patch16-224-Trial006_007_008-YEL_STEM4

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: 0.0236
- 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: 60
- eval_batch_size: 60
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 240
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7015        | 1.0   | 4    | 0.6622          | 0.5769   |
| 0.7592        | 2.0   | 8    | 0.6507          | 0.5769   |
| 0.5858        | 3.0   | 12   | 0.6202          | 0.5673   |
| 0.5612        | 4.0   | 16   | 0.4974          | 0.7019   |
| 0.4916        | 5.0   | 20   | 0.4718          | 0.7692   |
| 0.4925        | 6.0   | 24   | 0.3628          | 0.8173   |
| 0.4221        | 7.0   | 28   | 0.3077          | 0.875    |
| 0.4043        | 8.0   | 32   | 0.2262          | 0.9135   |
| 0.3757        | 9.0   | 36   | 0.1462          | 0.9712   |
| 0.2699        | 10.0  | 40   | 0.1450          | 0.9327   |
| 0.2677        | 11.0  | 44   | 0.0960          | 0.9615   |
| 0.2279        | 12.0  | 48   | 0.0745          | 0.9808   |
| 0.2113        | 13.0  | 52   | 0.0645          | 0.9808   |
| 0.1821        | 14.0  | 56   | 0.0649          | 0.9808   |
| 0.2025        | 15.0  | 60   | 0.0434          | 0.9904   |
| 0.1684        | 16.0  | 64   | 0.0433          | 0.9904   |
| 0.1521        | 17.0  | 68   | 0.0564          | 0.9808   |
| 0.258         | 18.0  | 72   | 0.0236          | 1.0      |
| 0.2594        | 19.0  | 76   | 0.0212          | 1.0      |
| 0.2045        | 20.0  | 80   | 0.0160          | 1.0      |
| 0.1947        | 21.0  | 84   | 0.0411          | 0.9904   |
| 0.1871        | 22.0  | 88   | 0.0245          | 0.9904   |
| 0.1702        | 23.0  | 92   | 0.0161          | 1.0      |
| 0.1591        | 24.0  | 96   | 0.0124          | 1.0      |
| 0.1387        | 25.0  | 100  | 0.0137          | 0.9904   |
| 0.146         | 26.0  | 104  | 0.0094          | 1.0      |
| 0.1005        | 27.0  | 108  | 0.0067          | 1.0      |
| 0.1612        | 28.0  | 112  | 0.0051          | 1.0      |
| 0.1821        | 29.0  | 116  | 0.0052          | 1.0      |
| 0.1489        | 30.0  | 120  | 0.0059          | 1.0      |
| 0.1793        | 31.0  | 124  | 0.0057          | 1.0      |
| 0.1624        | 32.0  | 128  | 0.0079          | 1.0      |
| 0.1516        | 33.0  | 132  | 0.0071          | 1.0      |
| 0.1195        | 34.0  | 136  | 0.0054          | 1.0      |
| 0.1854        | 35.0  | 140  | 0.0065          | 1.0      |
| 0.1329        | 36.0  | 144  | 0.0036          | 1.0      |
| 0.0951        | 37.0  | 148  | 0.0025          | 1.0      |
| 0.1886        | 38.0  | 152  | 0.0060          | 1.0      |
| 0.1151        | 39.0  | 156  | 0.0025          | 1.0      |
| 0.1353        | 40.0  | 160  | 0.0045          | 1.0      |
| 0.1455        | 41.0  | 164  | 0.0035          | 1.0      |
| 0.1824        | 42.0  | 168  | 0.0036          | 1.0      |
| 0.1328        | 43.0  | 172  | 0.0051          | 1.0      |
| 0.1349        | 44.0  | 176  | 0.0043          | 1.0      |
| 0.1348        | 45.0  | 180  | 0.0039          | 1.0      |
| 0.1201        | 46.0  | 184  | 0.0042          | 1.0      |
| 0.1087        | 47.0  | 188  | 0.0049          | 1.0      |
| 0.0875        | 48.0  | 192  | 0.0063          | 1.0      |
| 0.1814        | 49.0  | 196  | 0.0069          | 1.0      |
| 0.1342        | 50.0  | 200  | 0.0064          | 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_STEM4", "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