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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- precision
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model-index:
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- name: swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final
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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: 0.9219817767653758
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- name: Precision
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type: precision
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value: 0.9235132299409764
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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-final
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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: 0.2021
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- Accuracy: 0.9220
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- F1 Score: 0.9207
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- Precision: 0.9235
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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: 1e-05
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
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| 1.2212 | 0.96 | 20 | 1.1407 | 0.6429 | 0.6225 | 0.6601 |
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| 0.565 | 1.98 | 41 | 0.5162 | 0.8326 | 0.8311 | 0.8428 |
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| 0.3245 | 2.99 | 62 | 0.3265 | 0.8804 | 0.8784 | 0.8843 |
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| 0.2618 | 4.0 | 83 | 0.2713 | 0.9066 | 0.9054 | 0.9105 |
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| 0.2164 | 4.96 | 103 | 0.2812 | 0.8946 | 0.8929 | 0.8994 |
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| 0.1814 | 5.98 | 124 | 0.2411 | 0.9060 | 0.9043 | 0.9091 |
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| 0.1481 | 6.99 | 145 | 0.2345 | 0.9100 | 0.9084 | 0.9130 |
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| 0.1468 | 8.0 | 166 | 0.2340 | 0.9072 | 0.9055 | 0.9108 |
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| 0.1336 | 8.96 | 186 | 0.1925 | 0.9265 | 0.9252 | 0.9270 |
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| 0.133 | 9.64 | 200 | 0.2021 | 0.9220 | 0.9207 | 0.9235 |
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### Framework versions
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- Transformers 4.29.2
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- Pytorch 2.0.1+cu117
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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