---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: cvt-13-384-22k-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: 0.8315301391035549
    - name: Precision
      type: precision
      value: 0.8302128280229624
    - name: Recall
      type: recall
      value: 0.8315301391035549
    - name: F1
      type: f1
      value: 0.8292026505769348
---

<!-- 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-13-384-22k-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: 0.5761
- Accuracy: 0.8315
- Precision: 0.8302
- Recall: 0.8315
- F1: 0.8292

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3821        | 0.99  | 20   | 1.2780          | 0.4969   | 0.5083    | 0.4969 | 0.4458 |
| 1.0785        | 1.99  | 40   | 0.8633          | 0.6669   | 0.6658    | 0.6669 | 0.6500 |
| 0.8862        | 2.99  | 60   | 0.7110          | 0.7218   | 0.7258    | 0.7218 | 0.7013 |
| 0.665         | 3.99  | 80   | 0.5515          | 0.8045   | 0.8137    | 0.8045 | 0.8050 |
| 0.6056        | 4.99  | 100  | 0.5956          | 0.7960   | 0.8041    | 0.7960 | 0.7846 |
| 0.4779        | 5.99  | 120  | 0.6229          | 0.7937   | 0.7945    | 0.7937 | 0.7857 |
| 0.4554        | 6.99  | 140  | 0.5355          | 0.8099   | 0.8126    | 0.8099 | 0.8086 |
| 0.4249        | 7.99  | 160  | 0.5447          | 0.8269   | 0.8275    | 0.8269 | 0.8236 |
| 0.4313        | 8.99  | 180  | 0.5530          | 0.8153   | 0.8140    | 0.8153 | 0.8132 |
| 0.423         | 9.99  | 200  | 0.5346          | 0.8238   | 0.8230    | 0.8238 | 0.8223 |
| 0.3997        | 10.99 | 220  | 0.5413          | 0.8338   | 0.8347    | 0.8338 | 0.8338 |
| 0.4095        | 11.99 | 240  | 0.5999          | 0.8207   | 0.8231    | 0.8207 | 0.8177 |
| 0.3979        | 12.99 | 260  | 0.5632          | 0.8284   | 0.8255    | 0.8284 | 0.8250 |
| 0.3408        | 13.99 | 280  | 0.5725          | 0.8207   | 0.8198    | 0.8207 | 0.8196 |
| 0.3828        | 14.99 | 300  | 0.5631          | 0.8277   | 0.8258    | 0.8277 | 0.8260 |
| 0.3595        | 15.99 | 320  | 0.6005          | 0.8308   | 0.8297    | 0.8308 | 0.8275 |
| 0.3789        | 16.99 | 340  | 0.5840          | 0.8300   | 0.8271    | 0.8300 | 0.8273 |
| 0.3545        | 17.99 | 360  | 0.5983          | 0.8246   | 0.8226    | 0.8246 | 0.8222 |
| 0.3472        | 18.99 | 380  | 0.5795          | 0.8416   | 0.8382    | 0.8416 | 0.8390 |
| 0.355         | 19.99 | 400  | 0.5761          | 0.8315   | 0.8302    | 0.8315 | 0.8292 |


### Framework versions

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