
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# SMIDS_10x_deit_base_rmsprop_lr001_fold4

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: 0.5717
- Accuracy: 0.7583

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9805        | 1.0   | 750   | 1.0011          | 0.42     |
| 0.7818        | 2.0   | 1500  | 0.8233          | 0.53     |
| 0.7762        | 3.0   | 2250  | 0.7596          | 0.635    |
| 0.7825        | 4.0   | 3000  | 0.8278          | 0.5767   |
| 0.9799        | 5.0   | 3750  | 0.7639          | 0.6567   |
| 0.7349        | 6.0   | 4500  | 0.7176          | 0.665    |
| 0.7335        | 7.0   | 5250  | 0.7675          | 0.6517   |
| 0.7363        | 8.0   | 6000  | 0.6895          | 0.6867   |
| 0.7309        | 9.0   | 6750  | 0.7596          | 0.6267   |
| 0.7352        | 10.0  | 7500  | 0.6960          | 0.6983   |
| 0.689         | 11.0  | 8250  | 0.6851          | 0.6617   |
| 0.757         | 12.0  | 9000  | 0.6688          | 0.705    |
| 0.6975        | 13.0  | 9750  | 0.6666          | 0.685    |
| 0.6974        | 14.0  | 10500 | 0.7116          | 0.63     |
| 0.6522        | 15.0  | 11250 | 0.6469          | 0.715    |
| 0.6599        | 16.0  | 12000 | 0.6662          | 0.6983   |
| 0.6052        | 17.0  | 12750 | 0.6271          | 0.725    |
| 0.6735        | 18.0  | 13500 | 0.6574          | 0.7133   |
| 0.6651        | 19.0  | 14250 | 0.6089          | 0.735    |
| 0.6678        | 20.0  | 15000 | 0.6811          | 0.7033   |
| 0.6169        | 21.0  | 15750 | 0.6498          | 0.7      |
| 0.6553        | 22.0  | 16500 | 0.5996          | 0.7283   |
| 0.6653        | 23.0  | 17250 | 0.6431          | 0.7067   |
| 0.7034        | 24.0  | 18000 | 0.6219          | 0.69     |
| 0.6965        | 25.0  | 18750 | 0.7004          | 0.67     |
| 0.7587        | 26.0  | 19500 | 0.6728          | 0.68     |
| 0.6314        | 27.0  | 20250 | 0.6810          | 0.7017   |
| 0.7663        | 28.0  | 21000 | 0.6469          | 0.7267   |
| 0.6224        | 29.0  | 21750 | 0.6341          | 0.73     |
| 0.6309        | 30.0  | 22500 | 0.5844          | 0.7367   |
| 0.6991        | 31.0  | 23250 | 0.6014          | 0.7167   |
| 0.5838        | 32.0  | 24000 | 0.5989          | 0.725    |
| 0.5973        | 33.0  | 24750 | 0.6025          | 0.7283   |
| 0.5899        | 34.0  | 25500 | 0.5599          | 0.7467   |
| 0.6428        | 35.0  | 26250 | 0.5768          | 0.7233   |
| 0.5153        | 36.0  | 27000 | 0.6044          | 0.7317   |
| 0.4928        | 37.0  | 27750 | 0.5801          | 0.76     |
| 0.6528        | 38.0  | 28500 | 0.5899          | 0.75     |
| 0.6083        | 39.0  | 29250 | 0.5681          | 0.75     |
| 0.6219        | 40.0  | 30000 | 0.5771          | 0.7417   |
| 0.5651        | 41.0  | 30750 | 0.5627          | 0.7667   |
| 0.6089        | 42.0  | 31500 | 0.5529          | 0.7633   |
| 0.576         | 43.0  | 32250 | 0.5724          | 0.76     |
| 0.5715        | 44.0  | 33000 | 0.5872          | 0.7183   |
| 0.5662        | 45.0  | 33750 | 0.5660          | 0.7567   |
| 0.5593        | 46.0  | 34500 | 0.5916          | 0.74     |
| 0.4885        | 47.0  | 35250 | 0.5806          | 0.7467   |
| 0.5997        | 48.0  | 36000 | 0.5874          | 0.7517   |
| 0.5097        | 49.0  | 36750 | 0.5727          | 0.76     |
| 0.499         | 50.0  | 37500 | 0.5717          | 0.7583   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/deit-base-patch16-224", "model-index": [{"name": "SMIDS_10x_deit_base_rmsprop_lr001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7583333333333333, "name": "Accuracy"}]}]}]}
transformers safetensors 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