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# smids_5x_deit_tiny_sgd_0001_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: 0.4980
- Accuracy: 0.8003

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1772        | 1.0   | 375   | 1.1487          | 0.3760   |
| 1.0767        | 2.0   | 750   | 1.0817          | 0.4276   |
| 0.9966        | 3.0   | 1125  | 1.0309          | 0.4742   |
| 0.9479        | 4.0   | 1500  | 0.9881          | 0.5158   |
| 0.9443        | 5.0   | 1875  | 0.9501          | 0.5374   |
| 0.9172        | 6.0   | 2250  | 0.9138          | 0.5574   |
| 0.8419        | 7.0   | 2625  | 0.8792          | 0.5857   |
| 0.8745        | 8.0   | 3000  | 0.8452          | 0.6023   |
| 0.8037        | 9.0   | 3375  | 0.8135          | 0.6273   |
| 0.7725        | 10.0  | 3750  | 0.7849          | 0.6556   |
| 0.73          | 11.0  | 4125  | 0.7582          | 0.6705   |
| 0.7472        | 12.0  | 4500  | 0.7325          | 0.6822   |
| 0.6618        | 13.0  | 4875  | 0.7082          | 0.7105   |
| 0.6872        | 14.0  | 5250  | 0.6883          | 0.7255   |
| 0.6348        | 15.0  | 5625  | 0.6702          | 0.7304   |
| 0.5716        | 16.0  | 6000  | 0.6521          | 0.7388   |
| 0.6306        | 17.0  | 6375  | 0.6373          | 0.7454   |
| 0.604         | 18.0  | 6750  | 0.6231          | 0.7537   |
| 0.5325        | 19.0  | 7125  | 0.6109          | 0.7604   |
| 0.602         | 20.0  | 7500  | 0.6001          | 0.7604   |
| 0.5143        | 21.0  | 7875  | 0.5907          | 0.7604   |
| 0.5394        | 22.0  | 8250  | 0.5814          | 0.7671   |
| 0.5319        | 23.0  | 8625  | 0.5730          | 0.7671   |
| 0.5182        | 24.0  | 9000  | 0.5659          | 0.7704   |
| 0.5353        | 25.0  | 9375  | 0.5592          | 0.7720   |
| 0.5323        | 26.0  | 9750  | 0.5527          | 0.7787   |
| 0.5286        | 27.0  | 10125 | 0.5472          | 0.7787   |
| 0.4649        | 28.0  | 10500 | 0.5420          | 0.7820   |
| 0.5396        | 29.0  | 10875 | 0.5373          | 0.7837   |
| 0.4863        | 30.0  | 11250 | 0.5330          | 0.7837   |
| 0.4735        | 31.0  | 11625 | 0.5292          | 0.7837   |
| 0.5595        | 32.0  | 12000 | 0.5255          | 0.7887   |
| 0.4938        | 33.0  | 12375 | 0.5219          | 0.7920   |
| 0.468         | 34.0  | 12750 | 0.5190          | 0.7970   |
| 0.4879        | 35.0  | 13125 | 0.5162          | 0.7970   |
| 0.4592        | 36.0  | 13500 | 0.5136          | 0.7970   |
| 0.4625        | 37.0  | 13875 | 0.5113          | 0.7953   |
| 0.4476        | 38.0  | 14250 | 0.5091          | 0.7970   |
| 0.4681        | 39.0  | 14625 | 0.5073          | 0.8003   |
| 0.4644        | 40.0  | 15000 | 0.5056          | 0.8003   |
| 0.4624        | 41.0  | 15375 | 0.5040          | 0.8003   |
| 0.4244        | 42.0  | 15750 | 0.5026          | 0.8003   |
| 0.457         | 43.0  | 16125 | 0.5015          | 0.8003   |
| 0.4802        | 44.0  | 16500 | 0.5005          | 0.8003   |
| 0.4285        | 45.0  | 16875 | 0.4996          | 0.8003   |
| 0.4665        | 46.0  | 17250 | 0.4990          | 0.8003   |
| 0.4627        | 47.0  | 17625 | 0.4985          | 0.8003   |
| 0.4554        | 48.0  | 18000 | 0.4982          | 0.8003   |
| 0.4888        | 49.0  | 18375 | 0.4980          | 0.8003   |
| 0.4643        | 50.0  | 18750 | 0.4980          | 0.8003   |


### 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_sgd_0001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.800332778702163, "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