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--- |
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license: apache-2.0 |
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datasets: |
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- marmal88/skin_cancer |
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base_model: |
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- google/vit-base-patch16-224-in21k |
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pipeline_tag: image-classification |
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tags: |
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- medical |
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--- |
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## Installation |
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First, clone the repository: |
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```bash |
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git clone https://github.com/ethicalabs-ai/SkinCancerViT.git |
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cd SkinCancerViT |
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``` |
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Then, install the package in editable mode using uv (or pip): |
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```bash |
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uv sync # Recommended if you use uv |
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# Or, if using pip: |
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# pip install -e . |
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``` |
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## Quick Start / Usage |
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This package allows you to load and use a pre-trained SkinCancerViT model for prediction. |
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```python |
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import torch |
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from skincancer_vit.model import SkinCancerViTModel |
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from PIL import Image |
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from datasets import load_dataset # To get a random sample |
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# Load the model from Hugging Face Hub |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = SkinCancerViTModel.from_pretrained("ethicalabs/SkinCancerViT") |
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model.to(device) # Move model to the desired device |
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model.eval() # Set model to evaluation mode |
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# Example Prediction from a Specific Image File |
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image_file_path = "images/patient-001.jpg" # Specify your image file path here |
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specific_image = Image.open(image_file_path).convert("RGB") |
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# Example tabular data for this prediction |
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specific_age = 42 |
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specific_localization = "face" # Ensure this matches one of your trained localization categories |
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predicted_dx, confidence = model.full_predict( |
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raw_image=specific_image, |
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raw_age=specific_age, |
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raw_localization=specific_localization, |
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device=device |
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) |
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print(f"Predicted Diagnosis: {predicted_dx}") |
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print(f"Confidence: {confidence:.4f}") |
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# Example Prediction from a Random Test Sample from the Dataset |
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dataset = load_dataset("marmal88/skin_cancer", split="test") |
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random_sample = dataset.shuffle(seed=42).select(range(1))[0] # Get the first shuffled sample |
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sample_image = random_sample["image"] |
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sample_age = random_sample["age"] |
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sample_localization = random_sample["localization"] |
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sample_true_dx = random_sample["dx"] |
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predicted_dx_sample, confidence_sample = model.full_predict( |
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raw_image=sample_image, |
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raw_age=sample_age, |
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raw_localization=sample_localization, |
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device=device |
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) |
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print(f"Predicted Diagnosis: {predicted_dx_sample}") |
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print(f"Confidence: {confidence_sample:.4f}") |
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print(f"Correct Prediction: {predicted_dx_sample == sample_true_dx}") |
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``` |
