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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: swin-large-patch4-window7-224-fv-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: 0.8601236476043277 |
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- name: Precision |
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type: precision |
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value: 0.8582306285016578 |
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- name: Recall |
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type: recall |
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value: 0.8601236476043277 |
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- name: F1 |
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type: f1 |
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value: 0.8582797853944862 |
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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-large-patch4-window7-224-fv-finetuned-memes |
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This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224](https://huggingface.co/microsoft/swin-large-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: 0.6502 |
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- Accuracy: 0.8601 |
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- Precision: 0.8582 |
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- Recall: 0.8601 |
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- F1: 0.8583 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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 | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.2077 | 0.99 | 20 | 0.9499 | 0.6461 | 0.6764 | 0.6461 | 0.5863 | |
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| 0.5687 | 1.99 | 40 | 0.5365 | 0.7975 | 0.8018 | 0.7975 | 0.7924 | |
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| 0.3607 | 2.99 | 60 | 0.4007 | 0.8423 | 0.8419 | 0.8423 | 0.8398 | |
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| 0.203 | 3.99 | 80 | 0.3751 | 0.8509 | 0.8502 | 0.8509 | 0.8503 | |
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| 0.1728 | 4.99 | 100 | 0.4168 | 0.8509 | 0.8519 | 0.8509 | 0.8506 | |
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| 0.0963 | 5.99 | 120 | 0.4351 | 0.8586 | 0.8573 | 0.8586 | 0.8555 | |
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| 0.0956 | 6.99 | 140 | 0.4415 | 0.8547 | 0.8542 | 0.8547 | 0.8541 | |
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| 0.079 | 7.99 | 160 | 0.5312 | 0.8501 | 0.8475 | 0.8501 | 0.8459 | |
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| 0.0635 | 8.99 | 180 | 0.5376 | 0.8601 | 0.8578 | 0.8601 | 0.8577 | |
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| 0.0593 | 9.99 | 200 | 0.5060 | 0.8609 | 0.8615 | 0.8609 | 0.8604 | |
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| 0.0656 | 10.99 | 220 | 0.4997 | 0.8617 | 0.8573 | 0.8617 | 0.8587 | |
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| 0.0561 | 11.99 | 240 | 0.5430 | 0.8586 | 0.8604 | 0.8586 | 0.8589 | |
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| 0.0523 | 12.99 | 260 | 0.5354 | 0.8624 | 0.8643 | 0.8624 | 0.8626 | |
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| 0.0489 | 13.99 | 280 | 0.5539 | 0.8609 | 0.8572 | 0.8609 | 0.8577 | |
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| 0.0487 | 14.99 | 300 | 0.5785 | 0.8609 | 0.8591 | 0.8609 | 0.8591 | |
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| 0.0485 | 15.99 | 320 | 0.6186 | 0.8601 | 0.8578 | 0.8601 | 0.8573 | |
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| 0.0518 | 16.99 | 340 | 0.6342 | 0.8624 | 0.8612 | 0.8624 | 0.8606 | |
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| 0.0432 | 17.99 | 360 | 0.6302 | 0.8586 | 0.8598 | 0.8586 | 0.8580 | |
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| 0.0469 | 18.99 | 380 | 0.6323 | 0.8617 | 0.8606 | 0.8617 | 0.8604 | |
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| 0.0426 | 19.99 | 400 | 0.6502 | 0.8601 | 0.8582 | 0.8601 | 0.8583 | |
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### Framework versions |
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- Transformers 4.24.0.dev0 |
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- Pytorch 1.11.0+cu102 |
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- Datasets 2.6.1.dev0 |
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- Tokenizers 0.13.1 |
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