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# smids_10x_deit_tiny_sgd_001_fold2

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: 0.3073
- Accuracy: 0.8902

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5172        | 1.0   | 750   | 0.5682          | 0.7687   |
| 0.3347        | 2.0   | 1500  | 0.4469          | 0.8153   |
| 0.3177        | 3.0   | 2250  | 0.3953          | 0.8469   |
| 0.3776        | 4.0   | 3000  | 0.3688          | 0.8502   |
| 0.2886        | 5.0   | 3750  | 0.3556          | 0.8519   |
| 0.2396        | 6.0   | 4500  | 0.3328          | 0.8502   |
| 0.2545        | 7.0   | 5250  | 0.3237          | 0.8586   |
| 0.2435        | 8.0   | 6000  | 0.3188          | 0.8569   |
| 0.2366        | 9.0   | 6750  | 0.3065          | 0.8686   |
| 0.232         | 10.0  | 7500  | 0.3041          | 0.8652   |
| 0.2399        | 11.0  | 8250  | 0.2971          | 0.8785   |
| 0.2717        | 12.0  | 9000  | 0.2941          | 0.8769   |
| 0.2579        | 13.0  | 9750  | 0.2863          | 0.8852   |
| 0.1661        | 14.0  | 10500 | 0.2895          | 0.8802   |
| 0.1655        | 15.0  | 11250 | 0.2865          | 0.8785   |
| 0.1921        | 16.0  | 12000 | 0.2897          | 0.8802   |
| 0.1525        | 17.0  | 12750 | 0.2854          | 0.8835   |
| 0.1653        | 18.0  | 13500 | 0.2861          | 0.8819   |
| 0.1849        | 19.0  | 14250 | 0.2939          | 0.8702   |
| 0.1923        | 20.0  | 15000 | 0.2850          | 0.8835   |
| 0.1967        | 21.0  | 15750 | 0.2874          | 0.8802   |
| 0.1373        | 22.0  | 16500 | 0.2916          | 0.8802   |
| 0.1229        | 23.0  | 17250 | 0.2891          | 0.8869   |
| 0.1054        | 24.0  | 18000 | 0.2911          | 0.8802   |
| 0.1456        | 25.0  | 18750 | 0.2869          | 0.8869   |
| 0.2052        | 26.0  | 19500 | 0.2987          | 0.8835   |
| 0.1723        | 27.0  | 20250 | 0.2918          | 0.8835   |
| 0.1277        | 28.0  | 21000 | 0.2937          | 0.8902   |
| 0.1569        | 29.0  | 21750 | 0.2956          | 0.8902   |
| 0.1514        | 30.0  | 22500 | 0.2954          | 0.8885   |
| 0.1603        | 31.0  | 23250 | 0.2954          | 0.8902   |
| 0.1428        | 32.0  | 24000 | 0.2940          | 0.8918   |
| 0.1564        | 33.0  | 24750 | 0.3002          | 0.8835   |
| 0.1386        | 34.0  | 25500 | 0.3023          | 0.8852   |
| 0.1564        | 35.0  | 26250 | 0.2982          | 0.8869   |
| 0.183         | 36.0  | 27000 | 0.3004          | 0.8885   |
| 0.1456        | 37.0  | 27750 | 0.3058          | 0.8869   |
| 0.1394        | 38.0  | 28500 | 0.3047          | 0.8869   |
| 0.121         | 39.0  | 29250 | 0.3021          | 0.8902   |
| 0.1192        | 40.0  | 30000 | 0.3035          | 0.8852   |
| 0.1706        | 41.0  | 30750 | 0.3048          | 0.8918   |
| 0.1421        | 42.0  | 31500 | 0.3036          | 0.8885   |
| 0.1223        | 43.0  | 32250 | 0.3066          | 0.8852   |
| 0.1116        | 44.0  | 33000 | 0.3060          | 0.8885   |
| 0.1122        | 45.0  | 33750 | 0.3075          | 0.8885   |
| 0.1411        | 46.0  | 34500 | 0.3066          | 0.8885   |
| 0.1644        | 47.0  | 35250 | 0.3072          | 0.8885   |
| 0.0953        | 48.0  | 36000 | 0.3070          | 0.8902   |
| 0.1109        | 49.0  | 36750 | 0.3072          | 0.8902   |
| 0.1061        | 50.0  | 37500 | 0.3073          | 0.8902   |


### 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_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8901830282861897, "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