---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: swin-base-patch4-window7-224-in22k-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: 0.8562596599690881
    - name: Precision
      type: precision
      value: 0.8545652818321074
    - name: Recall
      type: recall
      value: 0.8562596599690881
    - name: F1
      type: f1
      value: 0.8552274649509984
---

<!-- 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-base-patch4-window7-224-in22k-finetuned-memes

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7094
- Accuracy: 0.8563
- Precision: 0.8546
- Recall: 0.8563
- F1: 0.8552

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.1655        | 0.99  | 20   | 0.8573          | 0.6955   | 0.6953    | 0.6955 | 0.6683 |
| 0.5506        | 1.99  | 40   | 0.5327          | 0.8083   | 0.8050    | 0.8083 | 0.7963 |
| 0.3573        | 2.99  | 60   | 0.4497          | 0.8338   | 0.8339    | 0.8338 | 0.8317 |
| 0.2083        | 3.99  | 80   | 0.4561          | 0.8354   | 0.8450    | 0.8354 | 0.8368 |
| 0.1545        | 4.99  | 100  | 0.4605          | 0.8423   | 0.8458    | 0.8423 | 0.8430 |
| 0.1014        | 5.99  | 120  | 0.4924          | 0.8524   | 0.8571    | 0.8524 | 0.8538 |
| 0.0854        | 6.99  | 140  | 0.5759          | 0.8393   | 0.8452    | 0.8393 | 0.8400 |
| 0.1012        | 7.99  | 160  | 0.5142          | 0.8362   | 0.8378    | 0.8362 | 0.8361 |
| 0.077         | 8.99  | 180  | 0.5647          | 0.8331   | 0.8538    | 0.8331 | 0.8407 |
| 0.0667        | 9.99  | 200  | 0.5294          | 0.8462   | 0.8509    | 0.8462 | 0.8483 |
| 0.0666        | 10.99 | 220  | 0.6038          | 0.8385   | 0.8415    | 0.8385 | 0.8396 |
| 0.0574        | 11.99 | 240  | 0.6384          | 0.8408   | 0.8431    | 0.8408 | 0.8411 |
| 0.0488        | 12.99 | 260  | 0.6305          | 0.8516   | 0.8561    | 0.8516 | 0.8532 |
| 0.0524        | 13.99 | 280  | 0.6411          | 0.8509   | 0.8526    | 0.8509 | 0.8510 |
| 0.0511        | 14.99 | 300  | 0.6462          | 0.8547   | 0.8542    | 0.8547 | 0.8543 |
| 0.0495        | 15.99 | 320  | 0.6869          | 0.8532   | 0.8534    | 0.8532 | 0.8527 |
| 0.0412        | 16.99 | 340  | 0.6643          | 0.8578   | 0.8554    | 0.8578 | 0.8564 |
| 0.0411        | 17.99 | 360  | 0.7214          | 0.8570   | 0.8539    | 0.8570 | 0.8552 |
| 0.0434        | 18.99 | 380  | 0.7037          | 0.8524   | 0.8507    | 0.8524 | 0.8514 |
| 0.0394        | 19.99 | 400  | 0.7094          | 0.8563   | 0.8546    | 0.8563 | 0.8552 |


### Framework versions

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