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# swin-tiny-patch4-window7-224-finetuned-eurosat

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3612
- Accuracy: 0.9177

## Model description
I have trained this model on different types fruits datasets.so this API only applicable for fruits classification.
This model can also be trainable on custom dataset for other purpose.

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.174         | 0.98  | 16   | 1.3004          | 0.7662   |
| 0.8074        | 1.97  | 32   | 0.5137          | 0.8745   |
| 0.5093        | 2.95  | 48   | 0.3612          | 0.9177   |


### Framework versions

- Transformers 4.30.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

apple banana avocado cherry kiwi mango orange pinenapple strawberries watermelon