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---
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
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