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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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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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model-index: |
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- name: ViT-Breast-Cancer |
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results: [] |
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widget: |
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- src: https://pathology.jhu.edu/build/assets/breast/_gallery/invasive-lobular-carcinoma.jpg |
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example_title: Invasive Lobular Carcinoma |
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pipeline_tag: image-classification |
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--- |
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# ViT-Breast-Cancer |
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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 a dataset of breast cancer microscope slides. |
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## Model description |
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This is a fine-tuned ViT (Google) that serves more as an exploration of vision transformers in medicine for my learning than as anything specific. I fine-tuned this model on a dataset of ~7000 images of breast cancer slides labelled as 'benign' or 'cancerous'. |
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I used the Transformers library and the out-of-the-box ViTForImageClassification configuration. |
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Despite this being an incredibly barebones fine-tune, I hope you fine it useful! Any recommendations are welcome! |
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## Intended uses & limitations |
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This is a super basic fine tuned model. Please evaluate its performance for yourself do determine whether it can be useful for you. In a big picture sense, this model can tell apart benign and cancerous breast tissue samples. |
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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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### Training results |
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- training_loss = 0.007100 |
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### Framework versions |
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- Transformers 4.42.0.dev0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
