---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: deit-base-patch16-224-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: 0.848531684698609
    - name: Precision
      type: precision
      value: 0.8458069264500935
    - name: Recall
      type: recall
      value: 0.848531684698609
    - name: F1
      type: f1
      value: 0.8463625265241504
---

<!-- 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. -->

# deit-base-patch16-224-FV-finetuned-memes

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6769
- Accuracy: 0.8485
- Precision: 0.8458
- Recall: 0.8485
- F1: 0.8464

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.2733        | 0.99  | 20   | 1.0893          | 0.5811   | 0.5790    | 0.5811 | 0.5293 |
| 0.7284        | 1.99  | 40   | 0.7351          | 0.7210   | 0.7642    | 0.7210 | 0.7271 |
| 0.4267        | 2.99  | 60   | 0.5202          | 0.7991   | 0.8104    | 0.7991 | 0.8033 |
| 0.2181        | 3.99  | 80   | 0.4605          | 0.8346   | 0.8351    | 0.8346 | 0.8334 |
| 0.1504        | 4.99  | 100  | 0.5281          | 0.8253   | 0.8281    | 0.8253 | 0.8266 |
| 0.1001        | 5.99  | 120  | 0.4945          | 0.8369   | 0.8336    | 0.8369 | 0.8347 |
| 0.0874        | 6.99  | 140  | 0.5902          | 0.8338   | 0.8370    | 0.8338 | 0.8348 |
| 0.0634        | 7.99  | 160  | 0.6088          | 0.8253   | 0.8221    | 0.8253 | 0.8234 |
| 0.0699        | 8.99  | 180  | 0.6210          | 0.8207   | 0.8202    | 0.8207 | 0.8186 |
| 0.0661        | 9.99  | 200  | 0.5675          | 0.8385   | 0.8417    | 0.8385 | 0.8393 |
| 0.0592        | 10.99 | 220  | 0.6550          | 0.8253   | 0.8324    | 0.8253 | 0.8275 |
| 0.0559        | 11.99 | 240  | 0.6400          | 0.8416   | 0.8370    | 0.8416 | 0.8387 |
| 0.0501        | 12.99 | 260  | 0.6726          | 0.8393   | 0.8353    | 0.8393 | 0.8350 |
| 0.0529        | 13.99 | 280  | 0.6285          | 0.8408   | 0.8399    | 0.8408 | 0.8401 |
| 0.0478        | 14.99 | 300  | 0.6423          | 0.8400   | 0.8380    | 0.8400 | 0.8384 |
| 0.0458        | 15.99 | 320  | 0.6632          | 0.8369   | 0.8337    | 0.8369 | 0.8348 |
| 0.048         | 16.99 | 340  | 0.6719          | 0.8423   | 0.8401    | 0.8423 | 0.8404 |
| 0.0417        | 17.99 | 360  | 0.6807          | 0.8423   | 0.8415    | 0.8423 | 0.8408 |
| 0.0461        | 18.99 | 380  | 0.6732          | 0.8454   | 0.8440    | 0.8454 | 0.8438 |
| 0.044         | 19.99 | 400  | 0.6769          | 0.8485   | 0.8458    | 0.8485 | 0.8464 |


### Framework versions

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