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update model card README.md
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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: swin-base-patch4-window7-224-20epochs-finetuned-memes
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.847758887171561
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# swin-base-patch4-window7-224-20epochs-finetuned-memes
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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.
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It achieves the following results on the evaluation set:
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- Loss: 0.7090
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- Accuracy: 0.8478
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.00012
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 20
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 1.0238 | 0.99 | 40 | 0.9636 | 0.6445 |
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| 0.777 | 1.99 | 80 | 0.6591 | 0.7666 |
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| 0.4763 | 2.99 | 120 | 0.5381 | 0.8130 |
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| 0.3215 | 3.99 | 160 | 0.5244 | 0.8253 |
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| 0.2179 | 4.99 | 200 | 0.5123 | 0.8238 |
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| 0.1868 | 5.99 | 240 | 0.5052 | 0.8308 |
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| 0.154 | 6.99 | 280 | 0.5444 | 0.8338 |
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| 0.1166 | 7.99 | 320 | 0.6318 | 0.8238 |
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| 0.1099 | 8.99 | 360 | 0.5656 | 0.8338 |
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| 0.0925 | 9.99 | 400 | 0.6057 | 0.8338 |
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| 0.0779 | 10.99 | 440 | 0.5942 | 0.8393 |
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| 0.0629 | 11.99 | 480 | 0.6112 | 0.8400 |
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| 0.0742 | 12.99 | 520 | 0.6588 | 0.8331 |
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| 0.0752 | 13.99 | 560 | 0.6143 | 0.8408 |
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| 0.0577 | 14.99 | 600 | 0.6450 | 0.8516 |
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| 0.0589 | 15.99 | 640 | 0.6787 | 0.8400 |
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| 0.0555 | 16.99 | 680 | 0.6641 | 0.8454 |
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| 0.052 | 17.99 | 720 | 0.7213 | 0.8524 |
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| 0.0589 | 18.99 | 760 | 0.6917 | 0.8470 |
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| 0.0506 | 19.99 | 800 | 0.7090 | 0.8478 |
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### Framework versions
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- Transformers 4.22.1
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- Pytorch 1.12.1+cu113
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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