
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# swin-tiny-patch4-window7-224-large-dataset-varicropped

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: 1.3554
- Accuracy: 0.6571

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8523        | 0.99  | 88   | 1.8136          | 0.3771   |
| 0.0725        | 1.99  | 176  | 1.2359          | 0.6006   |
| 0.0397        | 2.99  | 264  | 1.1716          | 0.6014   |
| 0.0179        | 3.99  | 352  | 1.5688          | 0.5704   |
| 0.0173        | 4.99  | 440  | 1.3718          | 0.6237   |
| 0.0097        | 5.99  | 528  | 1.3841          | 0.5927   |
| 0.0109        | 6.99  | 616  | 1.4044          | 0.5895   |
| 0.0019        | 7.99  | 704  | 1.2936          | 0.6150   |
| 0.002         | 8.99  | 792  | 1.4264          | 0.5760   |
| 0.0035        | 9.99  | 880  | 1.2226          | 0.6396   |
| 0.0025        | 10.99 | 968  | 1.1553          | 0.6635   |
| 0.0009        | 11.99 | 1056 | 1.1727          | 0.6643   |
| 0.0037        | 12.99 | 1144 | 1.1182          | 0.6714   |
| 0.0017        | 13.99 | 1232 | 1.4015          | 0.6364   |
| 0.0009        | 14.99 | 1320 | 1.2955          | 0.6683   |
| 0.0002        | 15.99 | 1408 | 1.2310          | 0.6555   |
| 0.0007        | 16.99 | 1496 | 1.3849          | 0.6325   |
| 0.001         | 17.99 | 1584 | 1.4312          | 0.6102   |
| 0.0001        | 18.99 | 1672 | 1.5087          | 0.6181   |
| 0.0002        | 19.99 | 1760 | 1.7247          | 0.6062   |
| 0.0016        | 20.99 | 1848 | 1.5534          | 0.6237   |
| 0.0004        | 21.99 | 1936 | 1.5382          | 0.6333   |
| 0.0008        | 22.99 | 2024 | 1.4910          | 0.6484   |
| 0.0008        | 23.99 | 2112 | 1.5020          | 0.6380   |
| 0.0005        | 24.99 | 2200 | 1.4788          | 0.6468   |
| 0.001         | 25.99 | 2288 | 1.3416          | 0.6770   |
| 0.003         | 26.99 | 2376 | 1.2643          | 0.6738   |
| 0.0001        | 27.99 | 2464 | 1.3582          | 0.6595   |
| 0.0           | 28.99 | 2552 | 1.3767          | 0.6523   |
| 0.0           | 29.99 | 2640 | 1.3554          | 0.6571   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1

ardei castravete ceapa dovlecel gulie loboda mărar ridiche salata spanac tomate usturoi varza vinete țelină