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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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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: Brain_Tumor_Detector_swin |
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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: 0.9981308411214953 |
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- name: F1 |
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type: f1 |
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value: 0.9985111662531018 |
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- name: Recall |
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type: recall |
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value: 0.9990069513406157 |
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- name: Precision |
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type: precision |
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value: 0.998015873015873 |
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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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# Brain_Tumor_Detector_swin |
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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: 0.0054 |
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- Accuracy: 0.9981 |
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- F1: 0.9985 |
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- Recall: 0.9990 |
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- Precision: 0.9980 |
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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: 5e-05 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 0.079 | 1.0 | 113 | 0.0283 | 0.9882 | 0.9906 | 0.9930 | 0.9881 | |
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| 0.0575 | 2.0 | 226 | 0.0121 | 0.9956 | 0.9965 | 0.9950 | 0.9980 | |
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| 0.0312 | 3.0 | 339 | 0.0054 | 0.9981 | 0.9985 | 0.9990 | 0.9980 | |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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