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# Train-Test-Augmentation-V3D-vit-base

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5845
- Accuracy: 0.8125

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.1982        | 0.9825 | 28   | 1.4670          | 0.6172   |
| 0.6796        | 2.0    | 57   | 0.6957          | 0.8052   |
| 0.2461        | 2.9825 | 85   | 0.5577          | 0.8255   |
| 0.0778        | 4.0    | 114  | 0.5563          | 0.8108   |
| 0.0243        | 4.9825 | 142  | 0.5745          | 0.8086   |
| 0.013         | 6.0    | 171  | 0.5634          | 0.8137   |
| 0.0096        | 6.9825 | 199  | 0.5765          | 0.8125   |
| 0.0077        | 8.0    | 228  | 0.5802          | 0.8114   |
| 0.0067        | 8.9825 | 256  | 0.5835          | 0.8114   |
| 0.0065        | 9.8246 | 280  | 0.5845          | 0.8125   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1

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