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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-base-patch4-window7-224-20epochs-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: 0.847758887171561
- task:
type: image-classification
name: Image Classification
dataset:
type: custom
name: custom
split: test
metrics:
- type: f1
value: 0.8504084378729573
name: F1
- type: precision
value: 0.8519647060733512
name: Precision
- type: recall
value: 0.8523956723338485
name: Recall
---
<!-- 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-window7-224-20epochs-finetuned-memes
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7090
- Accuracy: 0.8478
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0238 | 0.99 | 40 | 0.9636 | 0.6445 |
| 0.777 | 1.99 | 80 | 0.6591 | 0.7666 |
| 0.4763 | 2.99 | 120 | 0.5381 | 0.8130 |
| 0.3215 | 3.99 | 160 | 0.5244 | 0.8253 |
| 0.2179 | 4.99 | 200 | 0.5123 | 0.8238 |
| 0.1868 | 5.99 | 240 | 0.5052 | 0.8308 |
| 0.154 | 6.99 | 280 | 0.5444 | 0.8338 |
| 0.1166 | 7.99 | 320 | 0.6318 | 0.8238 |
| 0.1099 | 8.99 | 360 | 0.5656 | 0.8338 |
| 0.0925 | 9.99 | 400 | 0.6057 | 0.8338 |
| 0.0779 | 10.99 | 440 | 0.5942 | 0.8393 |
| 0.0629 | 11.99 | 480 | 0.6112 | 0.8400 |
| 0.0742 | 12.99 | 520 | 0.6588 | 0.8331 |
| 0.0752 | 13.99 | 560 | 0.6143 | 0.8408 |
| 0.0577 | 14.99 | 600 | 0.6450 | 0.8516 |
| 0.0589 | 15.99 | 640 | 0.6787 | 0.8400 |
| 0.0555 | 16.99 | 680 | 0.6641 | 0.8454 |
| 0.052 | 17.99 | 720 | 0.7213 | 0.8524 |
| 0.0589 | 18.99 | 760 | 0.6917 | 0.8470 |
| 0.0506 | 19.99 | 800 | 0.7090 | 0.8478 |
### Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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