---
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-balanced
  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: 0.556
---

<!-- 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-balanced

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: 0.7145
- Accuracy: 0.556

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7016        | 0.98  | 47   | 0.7243          | 0.512    |
| 0.6676        | 1.99  | 95   | 0.7139          | 0.544    |
| 0.626         | 2.99  | 143  | 0.7145          | 0.556    |
| 0.6042        | 4.0   | 191  | 0.7342          | 0.556    |
| 0.5672        | 4.98  | 238  | 0.7481          | 0.548    |
| 0.5339        | 5.99  | 286  | 0.7458          | 0.532    |
| 0.5266        | 6.99  | 334  | 0.7662          | 0.536    |
| 0.5102        | 8.0   | 382  | 0.7832          | 0.544    |
| 0.4808        | 8.98  | 429  | 0.7898          | 0.53     |
| 0.4698        | 9.84  | 470  | 0.7844          | 0.534    |


### Framework versions

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