---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: swin-large-patch4-window7-224-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: 0.8601236476043277
    - name: Precision
      type: precision
      value: 0.8582306285016578
    - name: Recall
      type: recall
      value: 0.8601236476043277
    - name: F1
      type: f1
      value: 0.8582797853944862
---

<!-- 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. -->

# swin-large-patch4-window7-224-fv-finetuned-memes

This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224](https://huggingface.co/microsoft/swin-large-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6502
- Accuracy: 0.8601
- Precision: 0.8582
- Recall: 0.8601
- F1: 0.8583

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.2077        | 0.99  | 20   | 0.9499          | 0.6461   | 0.6764    | 0.6461 | 0.5863 |
| 0.5687        | 1.99  | 40   | 0.5365          | 0.7975   | 0.8018    | 0.7975 | 0.7924 |
| 0.3607        | 2.99  | 60   | 0.4007          | 0.8423   | 0.8419    | 0.8423 | 0.8398 |
| 0.203         | 3.99  | 80   | 0.3751          | 0.8509   | 0.8502    | 0.8509 | 0.8503 |
| 0.1728        | 4.99  | 100  | 0.4168          | 0.8509   | 0.8519    | 0.8509 | 0.8506 |
| 0.0963        | 5.99  | 120  | 0.4351          | 0.8586   | 0.8573    | 0.8586 | 0.8555 |
| 0.0956        | 6.99  | 140  | 0.4415          | 0.8547   | 0.8542    | 0.8547 | 0.8541 |
| 0.079         | 7.99  | 160  | 0.5312          | 0.8501   | 0.8475    | 0.8501 | 0.8459 |
| 0.0635        | 8.99  | 180  | 0.5376          | 0.8601   | 0.8578    | 0.8601 | 0.8577 |
| 0.0593        | 9.99  | 200  | 0.5060          | 0.8609   | 0.8615    | 0.8609 | 0.8604 |
| 0.0656        | 10.99 | 220  | 0.4997          | 0.8617   | 0.8573    | 0.8617 | 0.8587 |
| 0.0561        | 11.99 | 240  | 0.5430          | 0.8586   | 0.8604    | 0.8586 | 0.8589 |
| 0.0523        | 12.99 | 260  | 0.5354          | 0.8624   | 0.8643    | 0.8624 | 0.8626 |
| 0.0489        | 13.99 | 280  | 0.5539          | 0.8609   | 0.8572    | 0.8609 | 0.8577 |
| 0.0487        | 14.99 | 300  | 0.5785          | 0.8609   | 0.8591    | 0.8609 | 0.8591 |
| 0.0485        | 15.99 | 320  | 0.6186          | 0.8601   | 0.8578    | 0.8601 | 0.8573 |
| 0.0518        | 16.99 | 340  | 0.6342          | 0.8624   | 0.8612    | 0.8624 | 0.8606 |
| 0.0432        | 17.99 | 360  | 0.6302          | 0.8586   | 0.8598    | 0.8586 | 0.8580 |
| 0.0469        | 18.99 | 380  | 0.6323          | 0.8617   | 0.8606    | 0.8617 | 0.8604 |
| 0.0426        | 19.99 | 400  | 0.6502          | 0.8601   | 0.8582    | 0.8601 | 0.8583 |


### Framework versions

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