---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-hateful-meme-restructured
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.552
---

<!-- 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. -->

# vit-base-patch16-224-finetuned-hateful-meme-restructured

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: 0.7152
- Accuracy: 0.552

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6546        | 0.99  | 66   | 0.7185          | 0.52     |
| 0.6222        | 2.0   | 133  | 0.7152          | 0.552    |
| 0.5986        | 2.99  | 199  | 0.7344          | 0.542    |
| 0.5535        | 4.0   | 266  | 0.7782          | 0.514    |
| 0.5377        | 4.99  | 332  | 0.8329          | 0.514    |
| 0.5115        | 6.0   | 399  | 0.7596          | 0.528    |
| 0.5133        | 6.99  | 465  | 0.8151          | 0.512    |
| 0.511         | 8.0   | 532  | 0.7897          | 0.538    |
| 0.4712        | 8.99  | 598  | 0.8539          | 0.514    |
| 0.4626        | 9.92  | 660  | 0.8449          | 0.522    |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3