# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("JuIm/ViT-Breast-Cancer")
model = AutoModelForImageClassification.from_pretrained("JuIm/ViT-Breast-Cancer")ViT-Breast-Cancer
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on a dataset of breast cancer microscope slides.
Model description
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'. I used the Transformers library and the out-of-the-box ViTForImageClassification configuration. Despite this being an incredibly barebones fine-tune, I hope you fine it useful! Any recommendations are welcome!
Intended uses & limitations
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.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
Training results
- training_loss = 0.007100
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for JuIm/ViT-Breast-Cancer
Base model
google/vit-base-patch16-224-in21k
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="JuIm/ViT-Breast-Cancer") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")