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--- |
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metrics: |
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- accuracy |
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library_name: transformers |
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tags: |
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- vit |
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- image |
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- classification |
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- health |
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- cancer |
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datasets: |
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- emre570/breastcancer-ultrasound-images |
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language: |
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- en |
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--- |
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This Vision Transformer model is a fine-tuned version of Google's "vit-large-patch16-224" model. |
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This model has been fine-tuned with a custom dataset as a finishing project for an academic study. |
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The aim of the project is to develop a model that achieves high consistency with a limited amount of data. The study uses a dataset consisting of breast cancer images of varying resolutions. |
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The dataset contains 780 MRI images with a total of 3 classes (benign, malignant, normal), separated into train and test. |
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**Distributions of images:** |
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**train:** |
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- benign: 350 |
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- malignant: 168 |
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- normal: 106 |
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**test:** |
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- benign: 87 |
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- malignant: 42 |
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- normal: 27 |
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Since the size of the images varies, the images were scaled down to the resolution specified by Google for the model (224x224) and given to the model for fine-tuning. |
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**Arguments used in fine-tuning:** |
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```py |
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trainArgs = TrainingArguments( |
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save_strategy="epoch", |
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evaluation_strategy="epoch", |
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learning_rate=2e-5, |
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per_device_train_batch_size=10, |
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per_device_eval_batch_size=4, |
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num_train_epochs=40, |
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weight_decay=0.01, |
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load_best_model_at_end=True, |
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metric_for_best_model="accuracy", |
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logging_dir='logs', |
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remove_unused_columns=False, |
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) |
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``` |
