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# smids_3x_deit_base_adamax_001_fold3

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9898
- Accuracy: 0.8967

## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4508        | 1.0   | 225   | 0.3728          | 0.8383   |
| 0.2776        | 2.0   | 450   | 0.4888          | 0.8267   |
| 0.2821        | 3.0   | 675   | 0.2991          | 0.8833   |
| 0.1623        | 4.0   | 900   | 0.3264          | 0.8783   |
| 0.1557        | 5.0   | 1125  | 0.3651          | 0.8833   |
| 0.1468        | 6.0   | 1350  | 0.4934          | 0.8667   |
| 0.0701        | 7.0   | 1575  | 0.6415          | 0.8583   |
| 0.1055        | 8.0   | 1800  | 0.4741          | 0.8717   |
| 0.0972        | 9.0   | 2025  | 0.4804          | 0.875    |
| 0.0586        | 10.0  | 2250  | 0.5016          | 0.8817   |
| 0.0818        | 11.0  | 2475  | 0.5231          | 0.8767   |
| 0.0651        | 12.0  | 2700  | 0.4690          | 0.89     |
| 0.0158        | 13.0  | 2925  | 0.6006          | 0.885    |
| 0.0389        | 14.0  | 3150  | 0.5509          | 0.8883   |
| 0.0218        | 15.0  | 3375  | 0.5621          | 0.88     |
| 0.0109        | 16.0  | 3600  | 0.5877          | 0.8933   |
| 0.0157        | 17.0  | 3825  | 0.5304          | 0.895    |
| 0.0158        | 18.0  | 4050  | 0.5697          | 0.895    |
| 0.0096        | 19.0  | 4275  | 0.6524          | 0.8983   |
| 0.0005        | 20.0  | 4500  | 0.6404          | 0.89     |
| 0.0001        | 21.0  | 4725  | 0.6926          | 0.8983   |
| 0.0045        | 22.0  | 4950  | 0.6417          | 0.8817   |
| 0.0215        | 23.0  | 5175  | 0.6424          | 0.89     |
| 0.0001        | 24.0  | 5400  | 0.7974          | 0.8683   |
| 0.0122        | 25.0  | 5625  | 0.7040          | 0.88     |
| 0.0           | 26.0  | 5850  | 0.7184          | 0.9083   |
| 0.0001        | 27.0  | 6075  | 0.6230          | 0.9067   |
| 0.0           | 28.0  | 6300  | 0.7043          | 0.9      |
| 0.0064        | 29.0  | 6525  | 0.7463          | 0.8983   |
| 0.0053        | 30.0  | 6750  | 0.7408          | 0.8983   |
| 0.0035        | 31.0  | 6975  | 0.7858          | 0.8983   |
| 0.0           | 32.0  | 7200  | 0.8160          | 0.9067   |
| 0.0           | 33.0  | 7425  | 0.8603          | 0.8967   |
| 0.0           | 34.0  | 7650  | 0.8311          | 0.9      |
| 0.0           | 35.0  | 7875  | 0.8519          | 0.905    |
| 0.0           | 36.0  | 8100  | 0.8622          | 0.8967   |
| 0.0032        | 37.0  | 8325  | 0.8530          | 0.8983   |
| 0.0           | 38.0  | 8550  | 0.9174          | 0.8967   |
| 0.0           | 39.0  | 8775  | 0.9290          | 0.9017   |
| 0.0           | 40.0  | 9000  | 0.9267          | 0.9017   |
| 0.0           | 41.0  | 9225  | 0.9188          | 0.8933   |
| 0.0           | 42.0  | 9450  | 0.9352          | 0.8933   |
| 0.0           | 43.0  | 9675  | 0.9338          | 0.9      |
| 0.0           | 44.0  | 9900  | 0.9386          | 0.9      |
| 0.0           | 45.0  | 10125 | 0.9613          | 0.8983   |
| 0.0           | 46.0  | 10350 | 0.9675          | 0.8933   |
| 0.0026        | 47.0  | 10575 | 0.9741          | 0.8967   |
| 0.0           | 48.0  | 10800 | 0.9808          | 0.8967   |
| 0.0           | 49.0  | 11025 | 0.9868          | 0.8967   |
| 0.0           | 50.0  | 11250 | 0.9898          | 0.8967   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/deit-base-patch16-224", "model-index": [{"name": "smids_3x_deit_base_adamax_001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8966666666666666, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us