
<!-- 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_tiny_sgd_001_fold5

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: 0.2805
- Accuracy: 0.88

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5873        | 1.0   | 750   | 0.5427          | 0.8017   |
| 0.4134        | 2.0   | 1500  | 0.4078          | 0.8383   |
| 0.4003        | 3.0   | 2250  | 0.3567          | 0.8583   |
| 0.322         | 4.0   | 3000  | 0.3309          | 0.8733   |
| 0.3592        | 5.0   | 3750  | 0.3090          | 0.8767   |
| 0.2384        | 6.0   | 4500  | 0.3021          | 0.8717   |
| 0.2287        | 7.0   | 5250  | 0.2872          | 0.8833   |
| 0.2763        | 8.0   | 6000  | 0.2770          | 0.8883   |
| 0.301         | 9.0   | 6750  | 0.2801          | 0.89     |
| 0.2498        | 10.0  | 7500  | 0.2717          | 0.8933   |
| 0.2639        | 11.0  | 8250  | 0.2693          | 0.8967   |
| 0.2576        | 12.0  | 9000  | 0.2726          | 0.8967   |
| 0.2998        | 13.0  | 9750  | 0.2655          | 0.905    |
| 0.2222        | 14.0  | 10500 | 0.2676          | 0.8933   |
| 0.2757        | 15.0  | 11250 | 0.2607          | 0.8933   |
| 0.1644        | 16.0  | 12000 | 0.2662          | 0.91     |
| 0.2069        | 17.0  | 12750 | 0.2656          | 0.9033   |
| 0.2175        | 18.0  | 13500 | 0.2618          | 0.9067   |
| 0.2174        | 19.0  | 14250 | 0.2668          | 0.9      |
| 0.1626        | 20.0  | 15000 | 0.2708          | 0.8983   |
| 0.1772        | 21.0  | 15750 | 0.2632          | 0.9017   |
| 0.1739        | 22.0  | 16500 | 0.2644          | 0.9017   |
| 0.2129        | 23.0  | 17250 | 0.2644          | 0.8983   |
| 0.1768        | 24.0  | 18000 | 0.2642          | 0.8983   |
| 0.1436        | 25.0  | 18750 | 0.2692          | 0.8933   |
| 0.1864        | 26.0  | 19500 | 0.2647          | 0.8983   |
| 0.13          | 27.0  | 20250 | 0.2627          | 0.8967   |
| 0.1786        | 28.0  | 21000 | 0.2674          | 0.8967   |
| 0.1885        | 29.0  | 21750 | 0.2653          | 0.895    |
| 0.1896        | 30.0  | 22500 | 0.2757          | 0.8867   |
| 0.1887        | 31.0  | 23250 | 0.2629          | 0.8983   |
| 0.1377        | 32.0  | 24000 | 0.2703          | 0.89     |
| 0.1805        | 33.0  | 24750 | 0.2693          | 0.8917   |
| 0.1524        | 34.0  | 25500 | 0.2706          | 0.89     |
| 0.1113        | 35.0  | 26250 | 0.2737          | 0.8883   |
| 0.153         | 36.0  | 27000 | 0.2742          | 0.8867   |
| 0.1281        | 37.0  | 27750 | 0.2787          | 0.8817   |
| 0.112         | 38.0  | 28500 | 0.2764          | 0.885    |
| 0.1149        | 39.0  | 29250 | 0.2767          | 0.885    |
| 0.136         | 40.0  | 30000 | 0.2752          | 0.8833   |
| 0.1297        | 41.0  | 30750 | 0.2749          | 0.8867   |
| 0.1614        | 42.0  | 31500 | 0.2776          | 0.8833   |
| 0.1176        | 43.0  | 32250 | 0.2769          | 0.8817   |
| 0.1355        | 44.0  | 33000 | 0.2814          | 0.8817   |
| 0.1418        | 45.0  | 33750 | 0.2806          | 0.8833   |
| 0.1165        | 46.0  | 34500 | 0.2801          | 0.8817   |
| 0.1556        | 47.0  | 35250 | 0.2815          | 0.88     |
| 0.1322        | 48.0  | 36000 | 0.2803          | 0.8817   |
| 0.1369        | 49.0  | 36750 | 0.2803          | 0.8833   |
| 0.1026        | 50.0  | 37500 | 0.2805          | 0.88     |


### 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_10x_deit_tiny_sgd_001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.88, "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