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# SMIDS_10x_deit_base_rmsprop_lr001_fold2

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: 1.3855
- Accuracy: 0.8120

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8251        | 1.0   | 750   | 0.8193          | 0.5408   |
| 0.7601        | 2.0   | 1500  | 0.7577          | 0.6273   |
| 0.7431        | 3.0   | 2250  | 0.7231          | 0.6206   |
| 0.6907        | 4.0   | 3000  | 0.6769          | 0.7105   |
| 0.6853        | 5.0   | 3750  | 0.6578          | 0.6972   |
| 0.6217        | 6.0   | 4500  | 0.6172          | 0.7388   |
| 0.6784        | 7.0   | 5250  | 0.6348          | 0.6905   |
| 0.6061        | 8.0   | 6000  | 0.5955          | 0.7255   |
| 0.5945        | 9.0   | 6750  | 0.5843          | 0.7271   |
| 0.6014        | 10.0  | 7500  | 0.5756          | 0.7454   |
| 0.617         | 11.0  | 8250  | 0.5718          | 0.7770   |
| 0.6461        | 12.0  | 9000  | 0.6316          | 0.7072   |
| 0.596         | 13.0  | 9750  | 0.6182          | 0.7354   |
| 0.5171        | 14.0  | 10500 | 0.5161          | 0.7637   |
| 0.4815        | 15.0  | 11250 | 0.5178          | 0.7737   |
| 0.5111        | 16.0  | 12000 | 0.5290          | 0.7554   |
| 0.5021        | 17.0  | 12750 | 0.5158          | 0.7903   |
| 0.4821        | 18.0  | 13500 | 0.5718          | 0.7554   |
| 0.4476        | 19.0  | 14250 | 0.4964          | 0.8153   |
| 0.4848        | 20.0  | 15000 | 0.4892          | 0.7953   |
| 0.4555        | 21.0  | 15750 | 0.5342          | 0.7671   |
| 0.4605        | 22.0  | 16500 | 0.4609          | 0.8020   |
| 0.4716        | 23.0  | 17250 | 0.5078          | 0.7787   |
| 0.3854        | 24.0  | 18000 | 0.4891          | 0.8053   |
| 0.4014        | 25.0  | 18750 | 0.6372          | 0.7238   |
| 0.3863        | 26.0  | 19500 | 0.5111          | 0.7920   |
| 0.4367        | 27.0  | 20250 | 0.4953          | 0.8153   |
| 0.4524        | 28.0  | 21000 | 0.5205          | 0.8120   |
| 0.2811        | 29.0  | 21750 | 0.5222          | 0.8186   |
| 0.3757        | 30.0  | 22500 | 0.5628          | 0.7953   |
| 0.3708        | 31.0  | 23250 | 0.5557          | 0.8087   |
| 0.32          | 32.0  | 24000 | 0.5500          | 0.8103   |
| 0.3085        | 33.0  | 24750 | 0.6631          | 0.8070   |
| 0.2871        | 34.0  | 25500 | 0.6566          | 0.8186   |
| 0.2897        | 35.0  | 26250 | 0.6191          | 0.8120   |
| 0.3033        | 36.0  | 27000 | 0.6703          | 0.8170   |
| 0.3563        | 37.0  | 27750 | 0.6477          | 0.8170   |
| 0.1988        | 38.0  | 28500 | 0.8282          | 0.7970   |
| 0.204         | 39.0  | 29250 | 0.7515          | 0.8103   |
| 0.2553        | 40.0  | 30000 | 0.7982          | 0.8037   |
| 0.214         | 41.0  | 30750 | 0.8153          | 0.7987   |
| 0.2121        | 42.0  | 31500 | 0.9997          | 0.7920   |
| 0.1814        | 43.0  | 32250 | 0.9138          | 0.8003   |
| 0.1454        | 44.0  | 33000 | 0.9691          | 0.8153   |
| 0.1611        | 45.0  | 33750 | 0.9812          | 0.8153   |
| 0.1644        | 46.0  | 34500 | 1.1917          | 0.8186   |
| 0.1618        | 47.0  | 35250 | 1.2292          | 0.8103   |
| 0.1142        | 48.0  | 36000 | 1.3349          | 0.7953   |
| 0.1428        | 49.0  | 36750 | 1.3718          | 0.8070   |
| 0.0645        | 50.0  | 37500 | 1.3855          | 0.8120   |


### 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_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8119800332778702, "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