| --- |
| license: mit |
| language: |
| - en |
| base_model: |
| - facebook/dinov3-vits16-pretrain-lvd1689m |
| pipeline_tag: image-segmentation |
| tags: |
| - computer-vision |
| - image-segmentation |
| - vision-transformer |
| - marine-biology |
| - benthic-ecology |
| - pytorch |
| --- |
| |
| # 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](https://github.com/NeelJani1/Algae_webproject). |
|
|
| 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: |
| ```bash |
| 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 |
