---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: mit-b2-finetuned-memes
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8523956723338485
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      type: custom
      name: custom
      split: test
    metrics:
    - type: f1
      value: 0.8580847578266328
      name: F1
    - type: precision
      value: 0.8587893412503379
      name: Precision
    - type: recall
      value: 0.8593508500772797
      name: Recall
---

<!-- 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. -->

# mit-b2-finetuned-memes

This model is a fine-tuned version of [aaraki/vit-base-patch16-224-in21k-finetuned-cifar10](https://huggingface.co/aaraki/vit-base-patch16-224-in21k-finetuned-cifar10) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4137
- Accuracy: 0.8524

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9727        | 0.99  | 40   | 0.8400          | 0.7334   |
| 0.5305        | 1.99  | 80   | 0.5147          | 0.8284   |
| 0.3124        | 2.99  | 120  | 0.4698          | 0.8145   |
| 0.2263        | 3.99  | 160  | 0.3892          | 0.8563   |
| 0.1453        | 4.99  | 200  | 0.3874          | 0.8570   |
| 0.1255        | 5.99  | 240  | 0.4097          | 0.8470   |
| 0.0989        | 6.99  | 280  | 0.3860          | 0.8570   |
| 0.0755        | 7.99  | 320  | 0.4141          | 0.8539   |
| 0.08          | 8.99  | 360  | 0.4049          | 0.8594   |
| 0.0639        | 9.99  | 400  | 0.4137          | 0.8524   |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1