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# smids_3x_deit_base_adamax_0001_fold1

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: 0.7119
- Accuracy: 0.9098

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3221        | 1.0   | 226   | 0.2895          | 0.8915   |
| 0.1579        | 2.0   | 452   | 0.3905          | 0.8664   |
| 0.0813        | 3.0   | 678   | 0.6613          | 0.8397   |
| 0.039         | 4.0   | 904   | 0.5507          | 0.8915   |
| 0.0377        | 5.0   | 1130  | 0.4323          | 0.9015   |
| 0.0016        | 6.0   | 1356  | 0.4901          | 0.9032   |
| 0.0006        | 7.0   | 1582  | 0.4428          | 0.9182   |
| 0.0001        | 8.0   | 1808  | 0.4887          | 0.9165   |
| 0.0056        | 9.0   | 2034  | 0.5309          | 0.9132   |
| 0.0002        | 10.0  | 2260  | 0.5356          | 0.9115   |
| 0.0005        | 11.0  | 2486  | 0.6183          | 0.8998   |
| 0.0           | 12.0  | 2712  | 0.5569          | 0.9032   |
| 0.0218        | 13.0  | 2938  | 0.6429          | 0.9048   |
| 0.0047        | 14.0  | 3164  | 0.6294          | 0.9015   |
| 0.0001        | 15.0  | 3390  | 0.6584          | 0.8982   |
| 0.0           | 16.0  | 3616  | 0.7150          | 0.8915   |
| 0.003         | 17.0  | 3842  | 0.7055          | 0.9032   |
| 0.0           | 18.0  | 4068  | 0.7249          | 0.9032   |
| 0.0033        | 19.0  | 4294  | 0.7504          | 0.8998   |
| 0.0056        | 20.0  | 4520  | 0.7091          | 0.9032   |
| 0.0           | 21.0  | 4746  | 0.7020          | 0.9082   |
| 0.0027        | 22.0  | 4972  | 0.6539          | 0.9082   |
| 0.0024        | 23.0  | 5198  | 0.6895          | 0.9065   |
| 0.0           | 24.0  | 5424  | 0.7110          | 0.9048   |
| 0.0           | 25.0  | 5650  | 0.6678          | 0.9065   |
| 0.0           | 26.0  | 5876  | 0.6869          | 0.9048   |
| 0.0           | 27.0  | 6102  | 0.6975          | 0.9065   |
| 0.0           | 28.0  | 6328  | 0.7125          | 0.9082   |
| 0.0           | 29.0  | 6554  | 0.6594          | 0.9082   |
| 0.0           | 30.0  | 6780  | 0.6721          | 0.9115   |
| 0.0           | 31.0  | 7006  | 0.7233          | 0.9065   |
| 0.0           | 32.0  | 7232  | 0.7025          | 0.9082   |
| 0.0029        | 33.0  | 7458  | 0.7410          | 0.9032   |
| 0.0           | 34.0  | 7684  | 0.6915          | 0.9065   |
| 0.0           | 35.0  | 7910  | 0.6943          | 0.9032   |
| 0.0           | 36.0  | 8136  | 0.6848          | 0.9098   |
| 0.0           | 37.0  | 8362  | 0.6997          | 0.9082   |
| 0.0           | 38.0  | 8588  | 0.6917          | 0.9082   |
| 0.0           | 39.0  | 8814  | 0.7108          | 0.9065   |
| 0.0           | 40.0  | 9040  | 0.6898          | 0.9115   |
| 0.0026        | 41.0  | 9266  | 0.6995          | 0.9082   |
| 0.0027        | 42.0  | 9492  | 0.7042          | 0.9082   |
| 0.0           | 43.0  | 9718  | 0.7153          | 0.9082   |
| 0.0           | 44.0  | 9944  | 0.7075          | 0.9065   |
| 0.0           | 45.0  | 10170 | 0.7108          | 0.9065   |
| 0.0           | 46.0  | 10396 | 0.7067          | 0.9098   |
| 0.0           | 47.0  | 10622 | 0.7091          | 0.9098   |
| 0.0           | 48.0  | 10848 | 0.7104          | 0.9082   |
| 0.0           | 49.0  | 11074 | 0.7114          | 0.9098   |
| 0.0           | 50.0  | 11300 | 0.7119          | 0.9098   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+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-base-patch16-224", "model-index": [{"name": "smids_3x_deit_base_adamax_0001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9098497495826378, "name": "Accuracy"}]}]}]}
transformers pytorch 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