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# SMIDS_1x_beit_large_RMSProp_lr0001_fold4

This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6000
- Accuracy: 0.815

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8472        | 1.0   | 150  | 0.8172          | 0.535    |
| 0.8357        | 2.0   | 300  | 0.7626          | 0.575    |
| 0.7417        | 3.0   | 450  | 1.0817          | 0.5183   |
| 0.7486        | 4.0   | 600  | 0.6990          | 0.6733   |
| 0.6159        | 5.0   | 750  | 0.6956          | 0.73     |
| 0.4599        | 6.0   | 900  | 0.5974          | 0.7733   |
| 0.5588        | 7.0   | 1050 | 0.5615          | 0.77     |
| 0.5341        | 8.0   | 1200 | 0.5690          | 0.78     |
| 0.4203        | 9.0   | 1350 | 0.8792          | 0.6967   |
| 0.3692        | 10.0  | 1500 | 0.5905          | 0.7867   |
| 0.4063        | 11.0  | 1650 | 0.5008          | 0.7833   |
| 0.3247        | 12.0  | 1800 | 0.4941          | 0.8183   |
| 0.3083        | 13.0  | 1950 | 0.8584          | 0.7233   |
| 0.2712        | 14.0  | 2100 | 0.4810          | 0.8233   |
| 0.2177        | 15.0  | 2250 | 0.5915          | 0.82     |
| 0.2187        | 16.0  | 2400 | 1.0070          | 0.775    |
| 0.1356        | 17.0  | 2550 | 0.7579          | 0.8      |
| 0.2473        | 18.0  | 2700 | 0.8294          | 0.7917   |
| 0.1911        | 19.0  | 2850 | 0.6477          | 0.8233   |
| 0.1643        | 20.0  | 3000 | 0.8079          | 0.8217   |
| 0.1467        | 21.0  | 3150 | 1.0468          | 0.795    |
| 0.0844        | 22.0  | 3300 | 1.0327          | 0.81     |
| 0.1669        | 23.0  | 3450 | 0.9522          | 0.81     |
| 0.1267        | 24.0  | 3600 | 1.1393          | 0.8117   |
| 0.061         | 25.0  | 3750 | 1.0417          | 0.8217   |
| 0.1083        | 26.0  | 3900 | 1.2259          | 0.8167   |
| 0.0551        | 27.0  | 4050 | 1.0429          | 0.8217   |
| 0.1133        | 28.0  | 4200 | 1.1700          | 0.8283   |
| 0.0467        | 29.0  | 4350 | 1.1179          | 0.805    |
| 0.0107        | 30.0  | 4500 | 1.3740          | 0.8183   |
| 0.1007        | 31.0  | 4650 | 1.4054          | 0.8017   |
| 0.044         | 32.0  | 4800 | 1.1759          | 0.8117   |
| 0.0147        | 33.0  | 4950 | 1.3960          | 0.81     |
| 0.0492        | 34.0  | 5100 | 1.3772          | 0.81     |
| 0.0098        | 35.0  | 5250 | 1.4632          | 0.825    |
| 0.0001        | 36.0  | 5400 | 1.5057          | 0.8217   |
| 0.0319        | 37.0  | 5550 | 1.5697          | 0.8017   |
| 0.0001        | 38.0  | 5700 | 1.7666          | 0.8083   |
| 0.0224        | 39.0  | 5850 | 1.5219          | 0.7967   |
| 0.06          | 40.0  | 6000 | 1.6612          | 0.8033   |
| 0.0535        | 41.0  | 6150 | 1.5448          | 0.8017   |
| 0.082         | 42.0  | 6300 | 1.6636          | 0.7967   |
| 0.0586        | 43.0  | 6450 | 1.9039          | 0.7967   |
| 0.0041        | 44.0  | 6600 | 1.8444          | 0.8067   |
| 0.0298        | 45.0  | 6750 | 1.5298          | 0.8117   |
| 0.0001        | 46.0  | 6900 | 1.5615          | 0.81     |
| 0.0016        | 47.0  | 7050 | 1.5713          | 0.8083   |
| 0.0001        | 48.0  | 7200 | 1.5807          | 0.8083   |
| 0.0           | 49.0  | 7350 | 1.6174          | 0.815    |
| 0.0001        | 50.0  | 7500 | 1.6000          | 0.815    |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_1x_beit_large_RMSProp_lr0001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.815, "name": "Accuracy"}]}]}]}
transformers pytorch beit image-classification generated_from_trainer dataset:imagefolder base_model:microsoft/beit-large-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us