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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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model-index: |
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- name: vit-base-oxford-brain-tumor |
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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: 0.6923076923076923 |
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pipeline_tag: image-classification |
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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 |
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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: 0.5719 |
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- Accuracy: 0.6923 |
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## Model description |
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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), which is a Vision Transformer (ViT) |
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ViT model is originaly a transformer encoder model pre-trained and fine-tuned on ImageNet 2012. |
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It was introduced in the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" by Dosovitskiy et al. |
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The model processes images as sequences of 16x16 patches, adding a [CLS] token for classification tasks, and uses absolute position embeddings. Pre-training enables the model to learn rich image representations, which can be leveraged for downstream tasks by adding a linear classifier on top of the [CLS] token. The weights were converted from the timm repository by Ross Wightman. |
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## Intended uses & limitations |
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This must be used for classification of x-ray images of the brain to diagnose of brain tumor. |
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## Training and evaluation data |
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The model was fine-tuned in the dataset [Mahadih534/brain-tumor-dataset](https://huggingface.co./datasets/Mahadih534/brain-tumor-dataset) that contains 253 brain images. This dataset was originally created by Yousef Ghanem. |
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The original dataset was splitted into training and evaluation subsets, 80% for training and 20% for evaluation. |
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For robust framework evaluation, the evaluation subset is further split into two equal parts for validation and testing. |
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This results in three distinct datasets: training, validation, and testing |
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### Training procedure/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: 7 |
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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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| No log | 1.0 | 11 | 0.5904 | 0.64 | |
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| No log | 2.0 | 22 | 0.5276 | 0.68 | |
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| No log | 3.0 | 33 | 0.4864 | 0.8 | |
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| No log | 4.0 | 44 | 0.4566 | 0.8 | |
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| No log | 5.0 | 55 | 0.4390 | 0.88 | |
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| No log | 6.0 | 66 | 0.4294 | 0.96 | |
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| No log | 7.0 | 77 | 0.4259 | 0.96 | |
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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 |
