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# smids_5x_deit_small_rms_001_fold4

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5824
- Accuracy: 0.78

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9095        | 1.0   | 375   | 0.8145          | 0.53     |
| 0.8394        | 2.0   | 750   | 0.8071          | 0.565    |
| 0.7959        | 3.0   | 1125  | 0.7903          | 0.6133   |
| 0.7869        | 4.0   | 1500  | 0.7586          | 0.6367   |
| 0.7989        | 5.0   | 1875  | 0.7552          | 0.6      |
| 0.8124        | 6.0   | 2250  | 0.7289          | 0.66     |
| 0.7708        | 7.0   | 2625  | 0.7042          | 0.6733   |
| 0.7586        | 8.0   | 3000  | 0.7504          | 0.6583   |
| 0.6986        | 9.0   | 3375  | 0.6527          | 0.6983   |
| 0.6979        | 10.0  | 3750  | 0.6544          | 0.7017   |
| 0.682         | 11.0  | 4125  | 0.6621          | 0.7117   |
| 0.6815        | 12.0  | 4500  | 0.6293          | 0.7067   |
| 0.6311        | 13.0  | 4875  | 0.6466          | 0.7033   |
| 0.743         | 14.0  | 5250  | 0.5967          | 0.7383   |
| 0.6884        | 15.0  | 5625  | 0.5874          | 0.7533   |
| 0.6214        | 16.0  | 6000  | 0.5678          | 0.7567   |
| 0.6379        | 17.0  | 6375  | 0.6145          | 0.7267   |
| 0.5615        | 18.0  | 6750  | 0.5793          | 0.7417   |
| 0.5825        | 19.0  | 7125  | 0.5647          | 0.76     |
| 0.5806        | 20.0  | 7500  | 0.5298          | 0.7617   |
| 0.5732        | 21.0  | 7875  | 0.6497          | 0.7117   |
| 0.4981        | 22.0  | 8250  | 0.6229          | 0.7283   |
| 0.5878        | 23.0  | 8625  | 0.5476          | 0.77     |
| 0.5732        | 24.0  | 9000  | 0.5431          | 0.7783   |
| 0.5633        | 25.0  | 9375  | 0.5734          | 0.7617   |
| 0.5704        | 26.0  | 9750  | 0.5553          | 0.7683   |
| 0.537         | 27.0  | 10125 | 0.5504          | 0.7733   |
| 0.4571        | 28.0  | 10500 | 0.5331          | 0.7783   |
| 0.5264        | 29.0  | 10875 | 0.5680          | 0.7633   |
| 0.6141        | 30.0  | 11250 | 0.5510          | 0.765    |
| 0.5469        | 31.0  | 11625 | 0.5500          | 0.7933   |
| 0.4915        | 32.0  | 12000 | 0.5001          | 0.785    |
| 0.5227        | 33.0  | 12375 | 0.5958          | 0.7783   |
| 0.4961        | 34.0  | 12750 | 0.5665          | 0.78     |
| 0.4306        | 35.0  | 13125 | 0.5345          | 0.7683   |
| 0.461         | 36.0  | 13500 | 0.5456          | 0.7683   |
| 0.5254        | 37.0  | 13875 | 0.5228          | 0.78     |
| 0.4633        | 38.0  | 14250 | 0.5026          | 0.7917   |
| 0.4546        | 39.0  | 14625 | 0.5577          | 0.7633   |
| 0.4842        | 40.0  | 15000 | 0.5245          | 0.78     |
| 0.4453        | 41.0  | 15375 | 0.5350          | 0.785    |
| 0.3943        | 42.0  | 15750 | 0.5494          | 0.7867   |
| 0.4031        | 43.0  | 16125 | 0.5697          | 0.7833   |
| 0.3729        | 44.0  | 16500 | 0.5326          | 0.7933   |
| 0.3744        | 45.0  | 16875 | 0.5371          | 0.7817   |
| 0.4535        | 46.0  | 17250 | 0.5557          | 0.7817   |
| 0.4267        | 47.0  | 17625 | 0.5568          | 0.7767   |
| 0.372         | 48.0  | 18000 | 0.5642          | 0.77     |
| 0.3734        | 49.0  | 18375 | 0.5737          | 0.785    |
| 0.4125        | 50.0  | 18750 | 0.5824          | 0.78     |


### 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-small-patch16-224", "model-index": [{"name": "smids_5x_deit_small_rms_001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.78, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-small-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us