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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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- task: |
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type: image-classification |
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name: Image Classification |
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dataset: |
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type: custom |
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name: custom |
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split: test |
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metrics: |
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- type: f1 |
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value: 0.8504084378729573 |
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name: F1 |
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- type: precision |
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value: 0.8519647060733512 |
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name: Precision |
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- type: recall |
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value: 0.8523956723338485 |
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name: Recall |
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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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