---
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-masked-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: 0.54
---

<!-- 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-masked-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: 0.7518
- Accuracy: 0.54

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6625        | 0.99  | 66   | 0.7385          | 0.518    |
| 0.6413        | 2.0   | 133  | 0.6980          | 0.538    |
| 0.6063        | 2.99  | 199  | 0.7422          | 0.53     |
| 0.5813        | 4.0   | 266  | 0.7794          | 0.52     |
| 0.5551        | 4.99  | 332  | 0.7975          | 0.52     |
| 0.5249        | 6.0   | 399  | 0.7518          | 0.54     |
| 0.5254        | 6.99  | 465  | 0.8074          | 0.53     |
| 0.5335        | 8.0   | 532  | 0.7907          | 0.52     |
| 0.4867        | 8.99  | 598  | 0.8286          | 0.524    |
| 0.4746        | 9.92  | 660  | 0.8262          | 0.522    |


### Framework versions

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