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# vit-base-patch16-224-FV-20epochs-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: 0.6532
- Accuracy: 0.8632
- Precision: 0.8617
- Recall: 0.8632
- F1: 0.8621

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.1709        | 0.99  | 20   | 0.9393          | 0.6971   | 0.6896    | 0.6971 | 0.6890 |
| 0.5295        | 1.99  | 40   | 0.5024          | 0.8091   | 0.8210    | 0.8091 | 0.8133 |
| 0.2909        | 2.99  | 60   | 0.4070          | 0.8539   | 0.8529    | 0.8539 | 0.8529 |
| 0.1435        | 3.99  | 80   | 0.4136          | 0.8539   | 0.8522    | 0.8539 | 0.8522 |
| 0.0928        | 4.99  | 100  | 0.4495          | 0.8478   | 0.8548    | 0.8478 | 0.8507 |
| 0.0643        | 5.99  | 120  | 0.4897          | 0.8594   | 0.8572    | 0.8594 | 0.8573 |
| 0.061         | 6.99  | 140  | 0.5040          | 0.8423   | 0.8490    | 0.8423 | 0.8453 |
| 0.0519        | 7.99  | 160  | 0.5266          | 0.8524   | 0.8502    | 0.8524 | 0.8510 |
| 0.0546        | 8.99  | 180  | 0.5200          | 0.8586   | 0.8632    | 0.8586 | 0.8605 |
| 0.0478        | 9.99  | 200  | 0.5654          | 0.8555   | 0.8548    | 0.8555 | 0.8548 |
| 0.0509        | 10.99 | 220  | 0.5774          | 0.8609   | 0.8626    | 0.8609 | 0.8616 |
| 0.0467        | 11.99 | 240  | 0.5847          | 0.8594   | 0.8602    | 0.8594 | 0.8594 |
| 0.0468        | 12.99 | 260  | 0.5909          | 0.8601   | 0.8597    | 0.8601 | 0.8596 |
| 0.0469        | 13.99 | 280  | 0.5970          | 0.8563   | 0.8560    | 0.8563 | 0.8561 |
| 0.0438        | 14.99 | 300  | 0.6234          | 0.8594   | 0.8583    | 0.8594 | 0.8586 |
| 0.0441        | 15.99 | 320  | 0.6190          | 0.8563   | 0.8582    | 0.8563 | 0.8570 |
| 0.0431        | 16.99 | 340  | 0.6419          | 0.8570   | 0.8584    | 0.8570 | 0.8574 |
| 0.0454        | 17.99 | 360  | 0.6528          | 0.8563   | 0.8556    | 0.8563 | 0.8558 |
| 0.0417        | 18.99 | 380  | 0.6688          | 0.8578   | 0.8575    | 0.8578 | 0.8574 |
| 0.0432        | 19.99 | 400  | 0.6532          | 0.8632   | 0.8617    | 0.8632 | 0.8621 |


### 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