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# SMIDS_10x_deit_base_adamax_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: 1.0018
- Accuracy: 0.9035

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2781        | 1.0   | 750   | 0.3810          | 0.8502   |
| 0.2261        | 2.0   | 1500  | 0.2730          | 0.9002   |
| 0.1706        | 3.0   | 2250  | 0.3047          | 0.8869   |
| 0.2169        | 4.0   | 3000  | 0.3862          | 0.8885   |
| 0.0837        | 5.0   | 3750  | 0.3990          | 0.8902   |
| 0.1236        | 6.0   | 4500  | 0.3231          | 0.8935   |
| 0.1244        | 7.0   | 5250  | 0.4820          | 0.8802   |
| 0.0826        | 8.0   | 6000  | 0.3985          | 0.8985   |
| 0.0398        | 9.0   | 6750  | 0.5231          | 0.8852   |
| 0.0134        | 10.0  | 7500  | 0.5170          | 0.8952   |
| 0.0835        | 11.0  | 8250  | 0.5577          | 0.8702   |
| 0.0493        | 12.0  | 9000  | 0.5183          | 0.8952   |
| 0.0579        | 13.0  | 9750  | 0.5385          | 0.8802   |
| 0.01          | 14.0  | 10500 | 0.6334          | 0.8902   |
| 0.0191        | 15.0  | 11250 | 0.5758          | 0.8952   |
| 0.0026        | 16.0  | 12000 | 0.5752          | 0.8985   |
| 0.002         | 17.0  | 12750 | 0.6911          | 0.8852   |
| 0.0173        | 18.0  | 13500 | 0.5655          | 0.9018   |
| 0.0084        | 19.0  | 14250 | 0.6050          | 0.9018   |
| 0.0015        | 20.0  | 15000 | 0.5623          | 0.8952   |
| 0.022         | 21.0  | 15750 | 0.6875          | 0.8852   |
| 0.0396        | 22.0  | 16500 | 0.6475          | 0.8902   |
| 0.0059        | 23.0  | 17250 | 0.5744          | 0.9085   |
| 0.0002        | 24.0  | 18000 | 0.7362          | 0.8985   |
| 0.0001        | 25.0  | 18750 | 0.6876          | 0.8902   |
| 0.0           | 26.0  | 19500 | 0.8483          | 0.8785   |
| 0.0103        | 27.0  | 20250 | 0.7748          | 0.8968   |
| 0.0           | 28.0  | 21000 | 0.8060          | 0.9002   |
| 0.0           | 29.0  | 21750 | 0.7462          | 0.8985   |
| 0.0           | 30.0  | 22500 | 0.8332          | 0.9052   |
| 0.0           | 31.0  | 23250 | 0.8156          | 0.9052   |
| 0.0           | 32.0  | 24000 | 0.8083          | 0.9002   |
| 0.0           | 33.0  | 24750 | 0.8197          | 0.9035   |
| 0.0           | 34.0  | 25500 | 0.8317          | 0.9002   |
| 0.0           | 35.0  | 26250 | 0.8446          | 0.9002   |
| 0.0           | 36.0  | 27000 | 0.8819          | 0.9018   |
| 0.0044        | 37.0  | 27750 | 0.8196          | 0.9035   |
| 0.0           | 38.0  | 28500 | 0.8857          | 0.8985   |
| 0.0           | 39.0  | 29250 | 0.8943          | 0.9035   |
| 0.0           | 40.0  | 30000 | 0.8618          | 0.9052   |
| 0.0           | 41.0  | 30750 | 0.9019          | 0.9035   |
| 0.0           | 42.0  | 31500 | 0.9306          | 0.9052   |
| 0.0           | 43.0  | 32250 | 0.9503          | 0.9002   |
| 0.0           | 44.0  | 33000 | 0.9323          | 0.9035   |
| 0.0029        | 45.0  | 33750 | 0.9597          | 0.9002   |
| 0.0           | 46.0  | 34500 | 0.9683          | 0.9018   |
| 0.0           | 47.0  | 35250 | 0.9824          | 0.9035   |
| 0.0           | 48.0  | 36000 | 0.9911          | 0.9035   |
| 0.0           | 49.0  | 36750 | 0.9984          | 0.9035   |
| 0.0           | 50.0  | 37500 | 1.0018          | 0.9035   |


### 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_adamax_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": 0.9034941763727121, "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