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# smids_10x_deit_tiny_sgd_0001_fold1

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4530
- Accuracy: 0.8114

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0181        | 1.0   | 751   | 0.9693          | 0.5359   |
| 0.81          | 2.0   | 1502  | 0.8850          | 0.5993   |
| 0.7699        | 3.0   | 2253  | 0.8246          | 0.6377   |
| 0.6601        | 4.0   | 3004  | 0.7789          | 0.6578   |
| 0.653         | 5.0   | 3755  | 0.7391          | 0.6745   |
| 0.6463        | 6.0   | 4506  | 0.7047          | 0.6912   |
| 0.5744        | 7.0   | 5257  | 0.6756          | 0.7028   |
| 0.4963        | 8.0   | 6008  | 0.6490          | 0.7129   |
| 0.5329        | 9.0   | 6759  | 0.6286          | 0.7195   |
| 0.5165        | 10.0  | 7510  | 0.6094          | 0.7295   |
| 0.5717        | 11.0  | 8261  | 0.5949          | 0.7279   |
| 0.4844        | 12.0  | 9012  | 0.5809          | 0.7396   |
| 0.4587        | 13.0  | 9763  | 0.5699          | 0.7446   |
| 0.4195        | 14.0  | 10514 | 0.5589          | 0.7496   |
| 0.4521        | 15.0  | 11265 | 0.5504          | 0.7579   |
| 0.4327        | 16.0  | 12016 | 0.5411          | 0.7596   |
| 0.4611        | 17.0  | 12767 | 0.5341          | 0.7663   |
| 0.4248        | 18.0  | 13518 | 0.5294          | 0.7746   |
| 0.4694        | 19.0  | 14269 | 0.5215          | 0.7780   |
| 0.395         | 20.0  | 15020 | 0.5170          | 0.7880   |
| 0.3437        | 21.0  | 15771 | 0.5117          | 0.7880   |
| 0.4367        | 22.0  | 16522 | 0.5057          | 0.7947   |
| 0.3451        | 23.0  | 17273 | 0.5010          | 0.7930   |
| 0.4413        | 24.0  | 18024 | 0.4962          | 0.7930   |
| 0.3908        | 25.0  | 18775 | 0.4929          | 0.7930   |
| 0.4631        | 26.0  | 19526 | 0.4899          | 0.7930   |
| 0.3779        | 27.0  | 20277 | 0.4860          | 0.7930   |
| 0.4436        | 28.0  | 21028 | 0.4829          | 0.7963   |
| 0.3794        | 29.0  | 21779 | 0.4792          | 0.7997   |
| 0.3732        | 30.0  | 22530 | 0.4775          | 0.7963   |
| 0.3411        | 31.0  | 23281 | 0.4746          | 0.7980   |
| 0.4745        | 32.0  | 24032 | 0.4718          | 0.7980   |
| 0.4263        | 33.0  | 24783 | 0.4692          | 0.7997   |
| 0.3711        | 34.0  | 25534 | 0.4676          | 0.8030   |
| 0.3951        | 35.0  | 26285 | 0.4656          | 0.8047   |
| 0.4026        | 36.0  | 27036 | 0.4635          | 0.8047   |
| 0.4811        | 37.0  | 27787 | 0.4621          | 0.8063   |
| 0.3816        | 38.0  | 28538 | 0.4609          | 0.8063   |
| 0.2904        | 39.0  | 29289 | 0.4596          | 0.8047   |
| 0.4708        | 40.0  | 30040 | 0.4586          | 0.8097   |
| 0.3633        | 41.0  | 30791 | 0.4575          | 0.8080   |
| 0.367         | 42.0  | 31542 | 0.4565          | 0.8080   |
| 0.4048        | 43.0  | 32293 | 0.4557          | 0.8080   |
| 0.3531        | 44.0  | 33044 | 0.4549          | 0.8080   |
| 0.3608        | 45.0  | 33795 | 0.4542          | 0.8097   |
| 0.3794        | 46.0  | 34546 | 0.4538          | 0.8097   |
| 0.3429        | 47.0  | 35297 | 0.4534          | 0.8114   |
| 0.395         | 48.0  | 36048 | 0.4532          | 0.8114   |
| 0.3682        | 49.0  | 36799 | 0.4531          | 0.8114   |
| 0.3927        | 50.0  | 37550 | 0.4530          | 0.8114   |


### 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-tiny-patch16-224", "model-index": [{"name": "smids_10x_deit_tiny_sgd_0001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8113522537562604, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-tiny-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us