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# smids_5x_deit_tiny_rms_0001_fold3

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: 1.0235
- Accuracy: 0.9017

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3072        | 1.0   | 375   | 0.3497          | 0.8733   |
| 0.1839        | 2.0   | 750   | 0.4255          | 0.87     |
| 0.1528        | 3.0   | 1125  | 0.4557          | 0.8567   |
| 0.1267        | 4.0   | 1500  | 0.3726          | 0.89     |
| 0.1353        | 5.0   | 1875  | 0.4467          | 0.8917   |
| 0.0943        | 6.0   | 2250  | 0.4927          | 0.91     |
| 0.1102        | 7.0   | 2625  | 0.6801          | 0.8833   |
| 0.1057        | 8.0   | 3000  | 0.6555          | 0.88     |
| 0.032         | 9.0   | 3375  | 0.7410          | 0.8783   |
| 0.0843        | 10.0  | 3750  | 0.8478          | 0.8667   |
| 0.0459        | 11.0  | 4125  | 0.6987          | 0.8917   |
| 0.0092        | 12.0  | 4500  | 0.7040          | 0.8917   |
| 0.0349        | 13.0  | 4875  | 0.7908          | 0.885    |
| 0.0111        | 14.0  | 5250  | 0.7260          | 0.8983   |
| 0.0286        | 15.0  | 5625  | 0.7556          | 0.89     |
| 0.0202        | 16.0  | 6000  | 0.7922          | 0.885    |
| 0.0017        | 17.0  | 6375  | 0.7780          | 0.89     |
| 0.0426        | 18.0  | 6750  | 0.7356          | 0.9033   |
| 0.0036        | 19.0  | 7125  | 0.7906          | 0.88     |
| 0.0088        | 20.0  | 7500  | 0.8591          | 0.8883   |
| 0.014         | 21.0  | 7875  | 0.9590          | 0.8867   |
| 0.0           | 22.0  | 8250  | 0.9929          | 0.8783   |
| 0.0363        | 23.0  | 8625  | 0.9559          | 0.89     |
| 0.0156        | 24.0  | 9000  | 0.9344          | 0.88     |
| 0.0345        | 25.0  | 9375  | 0.8898          | 0.8917   |
| 0.0005        | 26.0  | 9750  | 0.9066          | 0.9      |
| 0.0104        | 27.0  | 10125 | 0.9018          | 0.8983   |
| 0.0026        | 28.0  | 10500 | 0.8354          | 0.89     |
| 0.0098        | 29.0  | 10875 | 1.0679          | 0.885    |
| 0.0077        | 30.0  | 11250 | 0.8084          | 0.8933   |
| 0.007         | 31.0  | 11625 | 0.9761          | 0.8833   |
| 0.0079        | 32.0  | 12000 | 0.8798          | 0.8867   |
| 0.0211        | 33.0  | 12375 | 0.9152          | 0.8967   |
| 0.0205        | 34.0  | 12750 | 0.8595          | 0.8967   |
| 0.0           | 35.0  | 13125 | 0.9123          | 0.8983   |
| 0.0           | 36.0  | 13500 | 1.0918          | 0.8817   |
| 0.0001        | 37.0  | 13875 | 0.9598          | 0.8917   |
| 0.0           | 38.0  | 14250 | 0.9005          | 0.8933   |
| 0.0           | 39.0  | 14625 | 0.9817          | 0.895    |
| 0.003         | 40.0  | 15000 | 1.0214          | 0.8933   |
| 0.0           | 41.0  | 15375 | 1.0132          | 0.895    |
| 0.0012        | 42.0  | 15750 | 1.0443          | 0.8933   |
| 0.0           | 43.0  | 16125 | 1.0086          | 0.895    |
| 0.0           | 44.0  | 16500 | 1.0148          | 0.895    |
| 0.0           | 45.0  | 16875 | 1.0171          | 0.895    |
| 0.0           | 46.0  | 17250 | 1.0091          | 0.8967   |
| 0.0           | 47.0  | 17625 | 1.0118          | 0.8983   |
| 0.0           | 48.0  | 18000 | 1.0184          | 0.9017   |
| 0.0           | 49.0  | 18375 | 1.0213          | 0.9017   |
| 0.0           | 50.0  | 18750 | 1.0235          | 0.9017   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+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_5x_deit_tiny_rms_0001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9016666666666666, "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