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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-in22k-finetuned-brain-tumor-skullStrippded_03
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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: 0.9758007117437723
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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-in22k-finetuned-brain-tumor-skullStrippded_03
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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.
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It achieves the following results on the evaluation set:
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- Loss: 0.0706
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- Accuracy: 0.9758
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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: 6e-05
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- train_batch_size: 128
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- eval_batch_size: 128
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 512
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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: 10
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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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| 1.2809 | 1.0 | 11 | 0.7111 | 0.7153 |
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| 0.4554 | 2.0 | 22 | 0.2233 | 0.9139 |
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| 0.2382 | 3.0 | 33 | 0.1730 | 0.9388 |
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| 0.1453 | 4.0 | 44 | 0.1444 | 0.9509 |
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| 0.1064 | 5.0 | 55 | 0.0900 | 0.9665 |
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| 0.079 | 6.0 | 66 | 0.0866 | 0.9665 |
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| 0.0606 | 7.0 | 77 | 0.1744 | 0.9402 |
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| 0.0561 | 8.0 | 88 | 0.1116 | 0.9580 |
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| 0.0406 | 9.0 | 99 | 0.0726 | 0.9730 |
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| 0.0306 | 10.0 | 110 | 0.0706 | 0.9758 |
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### Framework versions
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- Transformers 4.27.4
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- Pytorch 2.0.0+cu117
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- Datasets 2.11.0
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- Tokenizers 0.13.3
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