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# vit-base-patch16-224-FV2-finetuned-memes

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5458
- Accuracy: 0.8648
- Precision: 0.8651
- Recall: 0.8648
- F1: 0.8646

## 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: 0.00012
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.994         | 0.99  | 20   | 0.7937          | 0.7257   | 0.7148    | 0.7257 | 0.7025 |
| 0.509         | 1.99  | 40   | 0.4634          | 0.8346   | 0.8461    | 0.8346 | 0.8303 |
| 0.2698        | 2.99  | 60   | 0.3851          | 0.8594   | 0.8619    | 0.8594 | 0.8586 |
| 0.1381        | 3.99  | 80   | 0.4186          | 0.8624   | 0.8716    | 0.8624 | 0.8634 |
| 0.0899        | 4.99  | 100  | 0.4038          | 0.8586   | 0.8624    | 0.8586 | 0.8594 |
| 0.0708        | 5.99  | 120  | 0.4170          | 0.8563   | 0.8612    | 0.8563 | 0.8580 |
| 0.0629        | 6.99  | 140  | 0.4414          | 0.8594   | 0.8599    | 0.8594 | 0.8585 |
| 0.0554        | 7.99  | 160  | 0.4617          | 0.8539   | 0.8563    | 0.8539 | 0.8550 |
| 0.0582        | 8.99  | 180  | 0.4712          | 0.8648   | 0.8667    | 0.8648 | 0.8651 |
| 0.0582        | 9.99  | 200  | 0.4753          | 0.8632   | 0.8647    | 0.8632 | 0.8636 |
| 0.0535        | 10.99 | 220  | 0.4653          | 0.8694   | 0.8690    | 0.8694 | 0.8684 |
| 0.0516        | 11.99 | 240  | 0.4937          | 0.8679   | 0.8692    | 0.8679 | 0.8681 |
| 0.0478        | 12.99 | 260  | 0.5109          | 0.8725   | 0.8741    | 0.8725 | 0.8718 |
| 0.0484        | 13.99 | 280  | 0.5144          | 0.8640   | 0.8660    | 0.8640 | 0.8647 |
| 0.0472        | 14.99 | 300  | 0.5249          | 0.8679   | 0.8688    | 0.8679 | 0.8678 |
| 0.043         | 15.99 | 320  | 0.5324          | 0.8709   | 0.8711    | 0.8709 | 0.8704 |
| 0.0473        | 16.99 | 340  | 0.5352          | 0.8648   | 0.8660    | 0.8648 | 0.8647 |
| 0.0502        | 17.99 | 360  | 0.5389          | 0.8694   | 0.8692    | 0.8694 | 0.8687 |
| 0.0489        | 18.99 | 380  | 0.5564          | 0.8648   | 0.8666    | 0.8648 | 0.8651 |
| 0.04          | 19.99 | 400  | 0.5458          | 0.8648   | 0.8651    | 0.8648 | 0.8646 |


### Framework versions

- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1.dev0
- Tokenizers 0.13.1

bollywood memes industrialist political memes singer memes sports memes