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# smids_5x_deit_tiny_adamax_0001_fold2

This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2333
- Accuracy: 0.8752

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2658        | 1.0   | 375   | 0.3487          | 0.8686   |
| 0.2056        | 2.0   | 750   | 0.3572          | 0.8802   |
| 0.1091        | 3.0   | 1125  | 0.4053          | 0.8785   |
| 0.0803        | 4.0   | 1500  | 0.6864          | 0.8636   |
| 0.0671        | 5.0   | 1875  | 0.6967          | 0.8702   |
| 0.0046        | 6.0   | 2250  | 0.8951          | 0.8636   |
| 0.0027        | 7.0   | 2625  | 0.7926          | 0.8852   |
| 0.0005        | 8.0   | 3000  | 0.7839          | 0.8769   |
| 0.0002        | 9.0   | 3375  | 0.8871          | 0.8869   |
| 0.0002        | 10.0  | 3750  | 0.8009          | 0.8968   |
| 0.0097        | 11.0  | 4125  | 0.9981          | 0.8669   |
| 0.0006        | 12.0  | 4500  | 1.0041          | 0.8719   |
| 0.01          | 13.0  | 4875  | 0.9204          | 0.8735   |
| 0.0001        | 14.0  | 5250  | 0.9628          | 0.8785   |
| 0.036         | 15.0  | 5625  | 0.9459          | 0.8752   |
| 0.0001        | 16.0  | 6000  | 0.9812          | 0.8819   |
| 0.0022        | 17.0  | 6375  | 0.9724          | 0.8819   |
| 0.004         | 18.0  | 6750  | 1.0660          | 0.8769   |
| 0.0           | 19.0  | 7125  | 0.9857          | 0.8719   |
| 0.0           | 20.0  | 7500  | 1.0524          | 0.8752   |
| 0.0           | 21.0  | 7875  | 1.0663          | 0.8686   |
| 0.0           | 22.0  | 8250  | 1.1115          | 0.8686   |
| 0.0           | 23.0  | 8625  | 1.0536          | 0.8752   |
| 0.0           | 24.0  | 9000  | 1.0776          | 0.8719   |
| 0.0           | 25.0  | 9375  | 1.0945          | 0.8686   |
| 0.0           | 26.0  | 9750  | 1.1377          | 0.8802   |
| 0.0           | 27.0  | 10125 | 1.1130          | 0.8735   |
| 0.0           | 28.0  | 10500 | 1.1384          | 0.8735   |
| 0.0           | 29.0  | 10875 | 1.1315          | 0.8769   |
| 0.0           | 30.0  | 11250 | 1.1249          | 0.8752   |
| 0.0037        | 31.0  | 11625 | 1.1445          | 0.8752   |
| 0.0039        | 32.0  | 12000 | 1.1633          | 0.8719   |
| 0.0041        | 33.0  | 12375 | 1.1577          | 0.8785   |
| 0.0           | 34.0  | 12750 | 1.1917          | 0.8686   |
| 0.0           | 35.0  | 13125 | 1.2054          | 0.8702   |
| 0.0           | 36.0  | 13500 | 1.1857          | 0.8735   |
| 0.0           | 37.0  | 13875 | 1.1926          | 0.8719   |
| 0.0           | 38.0  | 14250 | 1.2094          | 0.8719   |
| 0.0           | 39.0  | 14625 | 1.2019          | 0.8769   |
| 0.003         | 40.0  | 15000 | 1.2092          | 0.8752   |
| 0.0           | 41.0  | 15375 | 1.2117          | 0.8769   |
| 0.0026        | 42.0  | 15750 | 1.2195          | 0.8769   |
| 0.0026        | 43.0  | 16125 | 1.2211          | 0.8752   |
| 0.0026        | 44.0  | 16500 | 1.2245          | 0.8769   |
| 0.0024        | 45.0  | 16875 | 1.2259          | 0.8769   |
| 0.0           | 46.0  | 17250 | 1.2293          | 0.8752   |
| 0.0049        | 47.0  | 17625 | 1.2287          | 0.8752   |
| 0.0           | 48.0  | 18000 | 1.2326          | 0.8752   |
| 0.0023        | 49.0  | 18375 | 1.2328          | 0.8752   |
| 0.0024        | 50.0  | 18750 | 1.2333          | 0.8752   |


### 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-small-patch16-224", "model-index": [{"name": "smids_5x_deit_tiny_adamax_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": 0.8752079866888519, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-small-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us