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# SMIDS_10x_deit_base_rmsprop_lr0001_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: 1.1528
- Accuracy: 0.8918

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2399        | 1.0   | 750   | 0.3524          | 0.8552   |
| 0.0969        | 2.0   | 1500  | 0.4597          | 0.8869   |
| 0.0663        | 3.0   | 2250  | 0.4117          | 0.8769   |
| 0.0798        | 4.0   | 3000  | 0.5826          | 0.8702   |
| 0.0244        | 5.0   | 3750  | 0.5961          | 0.8852   |
| 0.0365        | 6.0   | 4500  | 0.5601          | 0.9035   |
| 0.0205        | 7.0   | 5250  | 0.6246          | 0.8918   |
| 0.0277        | 8.0   | 6000  | 0.4781          | 0.8952   |
| 0.0262        | 9.0   | 6750  | 0.5428          | 0.8968   |
| 0.0055        | 10.0  | 7500  | 0.5832          | 0.8952   |
| 0.0227        | 11.0  | 8250  | 0.7742          | 0.8835   |
| 0.0083        | 12.0  | 9000  | 0.6802          | 0.8835   |
| 0.0145        | 13.0  | 9750  | 0.6289          | 0.8935   |
| 0.0172        | 14.0  | 10500 | 0.4915          | 0.8952   |
| 0.0045        | 15.0  | 11250 | 0.5905          | 0.8935   |
| 0.0019        | 16.0  | 12000 | 0.8367          | 0.8885   |
| 0.0006        | 17.0  | 12750 | 0.8815          | 0.8869   |
| 0.0002        | 18.0  | 13500 | 0.7958          | 0.8902   |
| 0.0262        | 19.0  | 14250 | 0.7324          | 0.8835   |
| 0.0011        | 20.0  | 15000 | 0.6061          | 0.9018   |
| 0.0002        | 21.0  | 15750 | 0.7153          | 0.8918   |
| 0.0177        | 22.0  | 16500 | 0.7905          | 0.8902   |
| 0.0037        | 23.0  | 17250 | 0.7534          | 0.8985   |
| 0.007         | 24.0  | 18000 | 0.8498          | 0.8869   |
| 0.0028        | 25.0  | 18750 | 0.7244          | 0.9035   |
| 0.0271        | 26.0  | 19500 | 0.7994          | 0.8869   |
| 0.0043        | 27.0  | 20250 | 0.8474          | 0.8918   |
| 0.0           | 28.0  | 21000 | 0.8864          | 0.8835   |
| 0.0036        | 29.0  | 21750 | 0.8227          | 0.8769   |
| 0.0           | 30.0  | 22500 | 0.7795          | 0.8918   |
| 0.0           | 31.0  | 23250 | 0.9125          | 0.8952   |
| 0.0           | 32.0  | 24000 | 0.9374          | 0.8918   |
| 0.0001        | 33.0  | 24750 | 0.8336          | 0.8952   |
| 0.0           | 34.0  | 25500 | 0.8454          | 0.9052   |
| 0.0           | 35.0  | 26250 | 0.6633          | 0.9101   |
| 0.0           | 36.0  | 27000 | 0.9306          | 0.9018   |
| 0.0056        | 37.0  | 27750 | 0.9339          | 0.8968   |
| 0.0           | 38.0  | 28500 | 0.8568          | 0.9018   |
| 0.0           | 39.0  | 29250 | 1.0315          | 0.9002   |
| 0.0001        | 40.0  | 30000 | 0.9585          | 0.8952   |
| 0.0           | 41.0  | 30750 | 1.0605          | 0.8968   |
| 0.0           | 42.0  | 31500 | 1.0894          | 0.8935   |
| 0.0           | 43.0  | 32250 | 1.0885          | 0.8985   |
| 0.0           | 44.0  | 33000 | 1.1076          | 0.8935   |
| 0.0025        | 45.0  | 33750 | 1.1185          | 0.8935   |
| 0.0           | 46.0  | 34500 | 1.1305          | 0.8918   |
| 0.0           | 47.0  | 35250 | 1.1439          | 0.8918   |
| 0.0           | 48.0  | 36000 | 1.1491          | 0.8918   |
| 0.0           | 49.0  | 36750 | 1.1509          | 0.8918   |
| 0.0           | 50.0  | 37500 | 1.1528          | 0.8918   |


### 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_rmsprop_lr0001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.891846921797005, "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