KD OCT EfficientNet

--- license: mit library_name: pytorch pipeline_tag: image-classification tags: - medical-imaging - oct - retinal-imaging - knowledge-distillation - computer-vision --- # KD-OCT: Knowledge Distillation for OCT Image Classification This model is part of the **KD-OCT** project, introduced in the paper [KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification](https://huggingface.co/papers/2512.09069). ## Model Description KD-OCT is a knowledge distillation framework designed to compress a high-performance **ConvNeXtV2-Large** teacher model into a lightweight **EfficientNet-B2** student model. This approach is optimized for the clinical-grade classification of retinal Optical Coherence Tomography (OCT) images, specifically targeting conditions like age-related macular degeneration (AMD) and choroidal neovascularization (CNV). - **Task**: Multi-class classification (Normal, Drusen, CNV, and DME) - **Student Architecture**: EfficientNet-B2 - **Teacher Architecture**: ConvNeXtV2-Large - **Goal**: Enabling real-time deployment on edge devices for clinical screening. ## Training Details - **Framework**: PyTorch - **Method**: Real-time knowledge distillation using a combined loss (soft teacher knowledge transfer + hard ground-truth supervision). - **Optimization**: Focal loss for class imbalance and Stochastic Weight Averaging (SWA). - **Datasets**: Evaluated on Noor Eye Hospital (NEH) and UCSD datasets. ## Usage The following code snippet demonstrates how to load the model and perform inference using PyTorch: ```python import torch from torchvision import transforms # Load model model = torch.load("model.pth") model.eval() # Prepare image transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) # Inference (example for a single image tensor 'input_tensor') with torch.no_grad(): # input_tensor = transform(image).unsqueeze(0) output = model(input_tensor) prediction = torch.argmax(output, dim=1) ``` ## Citation If you use this model in your research, please cite: ```bibtex @article{nourbakhsh2025kd, title={KD-OCT: Efficient Knowledge Distillation for Clinical-Grade Retinal OCT Classification}, author={Nourbakhsh, Erfan and Sanjari, Nasrin and Nourbakhsh, Ali}, journal={arXiv preprint arXiv:2512.09069},...

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