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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: vit-base-patch16-224-in21k_brain_tumor_diagnosis |
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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: 0.9215686274509803 |
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- name: F1 |
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type: f1 |
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value: 0.9375 |
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- name: Recall |
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type: recall |
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value: 1 |
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- name: Precision |
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type: precision |
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value: 0.8823529411764706 |
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language: |
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- en |
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pipeline_tag: image-classification |
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--- |
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# vit-base-patch16-224-in21k_brain_tumor_diagnosis |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2591 |
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- Accuracy: 0.9216 |
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- F1: 0.9375 |
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- Recall: 1.0 |
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- Precision: 0.8824 |
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## Model description |
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This is a binary classification model to distinguish between if the MRI images detect a brain tumor or not. |
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Binary%20Classification/Brain%20Tumor%20MRI%20Images/brain_tumor_MRI_Images_ViT.ipynb |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection |
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_Sample Images From Dataset:_ |
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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: 0.0002 |
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- train_batch_size: 16 |
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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 | F1 | Recall | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 0.7101 | 1.0 | 13 | 0.3351 | 0.9412 | 0.9474 | 0.9 | 1.0 | |
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| 0.7101 | 2.0 | 26 | 0.3078 | 0.9020 | 0.9231 | 1.0 | 0.8571 | |
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| 0.7101 | 3.0 | 39 | 0.2591 | 0.9216 | 0.9375 | 1.0 | 0.8824 | |
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| 0.7101 | 4.0 | 52 | 0.2702 | 0.9020 | 0.9123 | 0.8667 | 0.9630 | |
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| 0.7101 | 5.0 | 65 | 0.2855 | 0.9020 | 0.9123 | 0.8667 | 0.9630 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.8.0 |
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- Tokenizers 0.12.1 |
