---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: cvt-13-384-in22k-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: 0.8346213292117465
    - name: Precision
      type: precision
      value: 0.8326806465391725
    - name: Recall
      type: recall
      value: 0.8346213292117465
    - name: F1
      type: f1
      value: 0.8322067261008879
---

<!-- 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. -->

# cvt-13-384-in22k-FV-finetuned-memes

This model is a fine-tuned version of [microsoft/cvt-13-384-22k](https://huggingface.co/microsoft/cvt-13-384-22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5595
- Accuracy: 0.8346
- Precision: 0.8327
- Recall: 0.8346
- F1: 0.8322

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.4066        | 0.99  | 20   | 1.2430          | 0.5124   | 0.5141    | 0.5124 | 0.4371 |
| 1.0813        | 1.99  | 40   | 0.8244          | 0.6893   | 0.6834    | 0.6893 | 0.6616 |
| 0.8392        | 2.99  | 60   | 0.6334          | 0.7612   | 0.7670    | 0.7612 | 0.7570 |
| 0.7065        | 3.99  | 80   | 0.5819          | 0.7767   | 0.7799    | 0.7767 | 0.7672 |
| 0.5751        | 4.99  | 100  | 0.5365          | 0.8176   | 0.8216    | 0.8176 | 0.8130 |
| 0.4896        | 5.99  | 120  | 0.4943          | 0.8308   | 0.8257    | 0.8308 | 0.8265 |
| 0.4487        | 6.99  | 140  | 0.5399          | 0.8107   | 0.8069    | 0.8107 | 0.8054 |
| 0.4349        | 7.99  | 160  | 0.4892          | 0.8300   | 0.8285    | 0.8300 | 0.8273 |
| 0.43          | 8.99  | 180  | 0.4984          | 0.8454   | 0.8465    | 0.8454 | 0.8426 |
| 0.4372        | 9.99  | 200  | 0.5573          | 0.8192   | 0.8221    | 0.8192 | 0.8157 |
| 0.3994        | 10.99 | 220  | 0.5158          | 0.8300   | 0.8284    | 0.8300 | 0.8281 |
| 0.3883        | 11.99 | 240  | 0.5495          | 0.8354   | 0.8317    | 0.8354 | 0.8314 |
| 0.406         | 12.99 | 260  | 0.5298          | 0.8284   | 0.8285    | 0.8284 | 0.8246 |
| 0.3355        | 13.99 | 280  | 0.5401          | 0.8393   | 0.8346    | 0.8393 | 0.8357 |
| 0.395         | 14.99 | 300  | 0.5915          | 0.8308   | 0.8278    | 0.8308 | 0.8261 |
| 0.3612        | 15.99 | 320  | 0.5852          | 0.8408   | 0.8378    | 0.8408 | 0.8368 |
| 0.3765        | 16.99 | 340  | 0.5509          | 0.8385   | 0.8351    | 0.8385 | 0.8356 |
| 0.3688        | 17.99 | 360  | 0.5668          | 0.8416   | 0.8398    | 0.8416 | 0.8387 |
| 0.3503        | 18.99 | 380  | 0.5626          | 0.8393   | 0.8371    | 0.8393 | 0.8365 |
| 0.3611        | 19.99 | 400  | 0.5595          | 0.8346   | 0.8327    | 0.8346 | 0.8322 |


### Framework versions

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