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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224 |
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tags: |
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- image-classification |
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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: vit-base-oxford-brain-tumor_x-ray |
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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: Mahadih534/brain-tumor-dataset |
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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: 0.7692307692307693 |
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- name: Precision |
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type: precision |
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value: 0.7692307692307693 |
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- name: Recall |
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type: recall |
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value: 0.7692307692307693 |
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- name: F1 |
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type: f1 |
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value: 0.7692307692307693 |
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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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# vit-base-oxford-brain-tumor_x-ray |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co./google/vit-base-patch16-224) on the Mahadih534/brain-tumor-dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5912 |
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- Accuracy: 0.7692 |
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- Precision: 0.7692 |
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- Recall: 0.7692 |
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- F1: 0.7692 |
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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: 0.0003 |
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- train_batch_size: 20 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 5 |
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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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| 0.6752 | 1.0 | 11 | 0.4894 | 0.76 | 0.7148 | 0.76 | 0.7114 | |
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| 0.5673 | 2.0 | 22 | 0.4630 | 0.72 | 0.57 | 0.72 | 0.6363 | |
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| 0.6173 | 3.0 | 33 | 0.4269 | 0.92 | 0.92 | 0.92 | 0.92 | |
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| 0.5562 | 4.0 | 44 | 0.5047 | 0.84 | 0.8653 | 0.84 | 0.8470 | |
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| 0.5285 | 5.0 | 55 | 0.4036 | 0.92 | 0.92 | 0.92 | 0.92 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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