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# smids_3x_deit_tiny_adamax_001_fold1

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: 1.1177
- Accuracy: 0.8681

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5567        | 1.0   | 226   | 0.5713          | 0.7462   |
| 0.3828        | 2.0   | 452   | 0.3656          | 0.8564   |
| 0.3502        | 3.0   | 678   | 0.3812          | 0.8381   |
| 0.276         | 4.0   | 904   | 0.4744          | 0.8280   |
| 0.2886        | 5.0   | 1130  | 0.4090          | 0.8447   |
| 0.2624        | 6.0   | 1356  | 0.3974          | 0.8598   |
| 0.1626        | 7.0   | 1582  | 0.4030          | 0.8781   |
| 0.1074        | 8.0   | 1808  | 0.4449          | 0.8631   |
| 0.184         | 9.0   | 2034  | 0.3919          | 0.8831   |
| 0.0836        | 10.0  | 2260  | 0.5392          | 0.8564   |
| 0.0539        | 11.0  | 2486  | 0.6608          | 0.8381   |
| 0.0511        | 12.0  | 2712  | 0.6349          | 0.8564   |
| 0.046         | 13.0  | 2938  | 0.6675          | 0.8848   |
| 0.054         | 14.0  | 3164  | 0.6371          | 0.8664   |
| 0.0362        | 15.0  | 3390  | 0.8363          | 0.8414   |
| 0.0153        | 16.0  | 3616  | 0.7151          | 0.8664   |
| 0.0583        | 17.0  | 3842  | 0.7946          | 0.8514   |
| 0.0315        | 18.0  | 4068  | 0.8456          | 0.8614   |
| 0.0078        | 19.0  | 4294  | 0.7428          | 0.8431   |
| 0.0034        | 20.0  | 4520  | 0.8571          | 0.8614   |
| 0.0384        | 21.0  | 4746  | 0.7674          | 0.8731   |
| 0.0465        | 22.0  | 4972  | 0.6983          | 0.8715   |
| 0.0099        | 23.0  | 5198  | 1.0003          | 0.8581   |
| 0.0034        | 24.0  | 5424  | 0.9037          | 0.8631   |
| 0.0001        | 25.0  | 5650  | 0.9614          | 0.8548   |
| 0.0004        | 26.0  | 5876  | 1.0237          | 0.8548   |
| 0.0094        | 27.0  | 6102  | 0.8996          | 0.8698   |
| 0.0001        | 28.0  | 6328  | 0.9361          | 0.8765   |
| 0.0           | 29.0  | 6554  | 1.0528          | 0.8614   |
| 0.0001        | 30.0  | 6780  | 0.9933          | 0.8614   |
| 0.0001        | 31.0  | 7006  | 1.0600          | 0.8614   |
| 0.0           | 32.0  | 7232  | 1.0559          | 0.8664   |
| 0.0031        | 33.0  | 7458  | 1.0415          | 0.8598   |
| 0.0           | 34.0  | 7684  | 1.1002          | 0.8648   |
| 0.0           | 35.0  | 7910  | 1.0102          | 0.8731   |
| 0.0           | 36.0  | 8136  | 1.0422          | 0.8731   |
| 0.0           | 37.0  | 8362  | 1.0448          | 0.8681   |
| 0.0           | 38.0  | 8588  | 1.0235          | 0.8698   |
| 0.0           | 39.0  | 8814  | 1.0543          | 0.8648   |
| 0.0           | 40.0  | 9040  | 1.0808          | 0.8681   |
| 0.0029        | 41.0  | 9266  | 1.0840          | 0.8698   |
| 0.0032        | 42.0  | 9492  | 1.0669          | 0.8698   |
| 0.0           | 43.0  | 9718  | 1.1155          | 0.8631   |
| 0.0           | 44.0  | 9944  | 1.0986          | 0.8698   |
| 0.0           | 45.0  | 10170 | 1.1093          | 0.8664   |
| 0.0           | 46.0  | 10396 | 1.1032          | 0.8664   |
| 0.0           | 47.0  | 10622 | 1.1140          | 0.8664   |
| 0.0           | 48.0  | 10848 | 1.1176          | 0.8681   |
| 0.0           | 49.0  | 11074 | 1.1190          | 0.8681   |
| 0.0           | 50.0  | 11300 | 1.1177          | 0.8681   |


### 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_3x_deit_tiny_adamax_001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8681135225375626, "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