---
license: other
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: mit-b2-fv-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: 0.8323029366306027
    - name: Precision
      type: precision
      value: 0.831217385971583
    - name: Recall
      type: recall
      value: 0.8323029366306027
    - name: F1
      type: f1
      value: 0.831492653119617
---

<!-- 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-fv-finetuned-memes

This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5984
- Accuracy: 0.8323
- Precision: 0.8312
- Recall: 0.8323
- F1: 0.8315

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3683        | 0.99  | 20   | 1.1798          | 0.5703   | 0.4914    | 0.5703 | 0.4915 |
| 1.0113        | 1.99  | 40   | 1.0384          | 0.6159   | 0.6813    | 0.6159 | 0.6274 |
| 0.7581        | 2.99  | 60   | 0.8348          | 0.6808   | 0.7377    | 0.6808 | 0.6840 |
| 0.6241        | 3.99  | 80   | 0.6034          | 0.7713   | 0.7864    | 0.7713 | 0.7735 |
| 0.4999        | 4.99  | 100  | 0.5481          | 0.7944   | 0.8000    | 0.7944 | 0.7909 |
| 0.3981        | 5.99  | 120  | 0.5253          | 0.8022   | 0.8091    | 0.8022 | 0.8000 |
| 0.3484        | 6.99  | 140  | 0.4688          | 0.8238   | 0.8147    | 0.8238 | 0.8146 |
| 0.3142        | 7.99  | 160  | 0.6245          | 0.7867   | 0.8209    | 0.7867 | 0.7920 |
| 0.2339        | 8.99  | 180  | 0.5053          | 0.8362   | 0.8426    | 0.8362 | 0.8355 |
| 0.2284        | 9.99  | 200  | 0.5070          | 0.8230   | 0.8220    | 0.8230 | 0.8187 |
| 0.1824        | 10.99 | 220  | 0.5780          | 0.8006   | 0.8138    | 0.8006 | 0.8035 |
| 0.1561        | 11.99 | 240  | 0.5429          | 0.8253   | 0.8197    | 0.8253 | 0.8218 |
| 0.1229        | 12.99 | 260  | 0.5325          | 0.8331   | 0.8296    | 0.8331 | 0.8303 |
| 0.1232        | 13.99 | 280  | 0.5595          | 0.8277   | 0.8290    | 0.8277 | 0.8273 |
| 0.118         | 14.99 | 300  | 0.5974          | 0.8292   | 0.8345    | 0.8292 | 0.8299 |
| 0.11          | 15.99 | 320  | 0.5796          | 0.8253   | 0.8228    | 0.8253 | 0.8231 |
| 0.0948        | 16.99 | 340  | 0.5581          | 0.8346   | 0.8358    | 0.8346 | 0.8349 |
| 0.0985        | 17.99 | 360  | 0.5700          | 0.8338   | 0.8301    | 0.8338 | 0.8318 |
| 0.0821        | 18.99 | 380  | 0.5756          | 0.8331   | 0.8343    | 0.8331 | 0.8335 |
| 0.0813        | 19.99 | 400  | 0.5984          | 0.8323   | 0.8312    | 0.8323 | 0.8315 |


### Framework versions

- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1.dev0
- Tokenizers 0.13.1