Vit B Coral Top Down

--- license: apache-2.0 base_model: - reefsupport/CoralSCOP pipeline_tag: image-segmentation tags: - coral - segmentation - vit - computer-vision - coral reef --- # Model Card for vit_b_coral-frontal-mask This model is an independent fine-tuned derivative of `reefsupport/CoralSCOP` (Apache-2.0) for frontal coral tile mask segmentation. The author of this repository is not affiliated with the CoralSCOP team or Reef Support. --- ## Model Details ### Model Description This model adapts the ViT-B backbone from CoralSCOP for binary coral mask segmentation in controlled, frontal tile imagery. The goal is to enable automated coral size measurement and growth monitoring workflows. - **Developed by:** lmagocs - **Model type:** Vision Transformer (ViT-B) segmentation model - **Language(s):** N/A (vision model) - **License:** Apache License 2.0 - **Finetuned from model:** reefsupport/CoralSCOP ### Model Sources - **Base model repository:** https://huggingface.co/reefsupport/CoralSCOP --- ## Uses ### Direct Use - Coral vs. non-coral mask segmentation - Tile-based coral growth measurement - Controlled imaging setups (frontal (top-down) coral glued to tile) ### Downstream Use - Automated measurement pipelines - Ecological monitoring workflows - Integration into research data processing systems ### Out-of-Scope Use - General underwater scene segmentation - Coral species classification - Real-time robotics deployment - Non-coral segmentation tasks Performance outside controlled frontal tile imagery is not validated. --- ## Bias, Risks, and Limitations - Trained on a small curated dataset. - Limited environmental diversity (lighting, turbidity, depth). - May not generalize to in-situ reef photography. - Sensitive to camera angle and framing assumptions. ### Recommendations - Validate on your own dataset before deployment. - Retrain or further fine-tune for different imaging conditions. - Avoid using for safety-critical or ecological policy decisions without verification. --- ## How to Get Started Example (PyTorch-style loading): ```python import torch model = torch.load("vit_b_coral-frontal-mask.pth") model.eval()