Neel536/Algea_Segmentation_Model

SeaDino-Seg-1: Benthic Algae Segmentation Model

SeaDino-Seg-1 is a specialized machine learning pipeline designed to automate benthic reef mapping and marine ecological survey analysis. It provides dense, pixel-level semantic segmentation of key ecological substrates and marine flora on rocky reefs, transforming raw underwater footage into structured ecological data.

• Repository Type: Model Card / Weight Hub
• Target Domain: Marine Benthic Ecology
• Base Architecture: Vision Transformer (DINOv3 ViT-S/16)

---

📊 Model Variants

This repository hosts two distinct trained model configurations that can be evaluated independently or compared side-by-side:

1. SeaDino-Seg-1-Org (Model Org): A standard baseline configuration optimized for rapid, general benthic feature extraction.
2. SeaDino-Seg-1-Fg (Model Fg): A highly specialized configuration featuring a custom backbone optimized for low-contrast boundaries and high-density marine life identification.

Both models support multiple, interchangeable decoder head sizes:

• Tiny (1D Linear Head)
• Small (2D Spatial Conv Head)
• Medium (3D Spatial Conv Head)
• Big (4D Spatial Conv Head)

---

📁 Repository Files

This repository contains the following deployment-ready weights and configuration files:

• SeaDino-Seg-1-Fg-Backbone.ckpt (The custom, specialized backbone weights)
• SeaDino-Seg-1-Fg-Small.pth (The spatial decoder head weights for Model Fg)
• SeaDino-Seg-1-Org-Small.pth (The spatial decoder head weights for Model Org)
• class_map.json (The configuration file defining the benthic classes)

---

📈 Target Benthic Classes

The system is trained to identify and segment the following 6 ecologically vital classes:

Class ID	Class Name	Color Code (RGB)	Description
0	Background	[0, 0, 0]	Barren sand, open water column, or unlabeled substrates
1	Rock	[204, 51, 51]	Exposed, barren rocky reef substrate
2	Carpophyllum	[51, 204, 51]	Canopy-forming brown algae (Carpophyllum maschalocarpum)
3	Ecklonia	[204, 204, 51]	Common kelp forest canopy (Ecklonia radiata)
4	Amphiroa	[51, 51, 204]	Articulated coralline algae (Amphiroa anceps)
5	Anthothoe	[204, 51, 204]	Encrusting white-striped anemone (Anthothoe albocincta)

---

⚙️ How to Load and Use

To run predictions using these cloud weights, use our official SeaDino-Seg-1 Github Pipeline.

The pipeline automatically fetches these weights, manages memory safety on GPU/CPU, and outputs both visual maps (confidence heatmaps & comparative grids) and structured JSON percent-cover data.

Example CLI Command:

python evaluate.py \
    --run_base \
    --run_ft \
    --sizes small \
    --mode all \
    --hf_repo "Neel536/Algea_Segmentation_Model"

---

📝 Citation & License

• License: MIT
• Backbone Reference: Self-Supervised ViT-S/16 (DINOv3)
• Project Lead: Neel / SeaDino-seg-1