lmagocs/vit-b-coral-top-down

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.

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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

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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.

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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.

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How to Get Started

Example (PyTorch-style loading):

import torch

model = torch.load("vit_b_coral-frontal-mask.pth")
model.eval()