🪸 Andromeida Reef Vision - ONNX Model Suite

This repository hosts optimized ONNX (Open Neural Network Exchange) weights for Andromeida Reef Vision Studio, enabling high-speed, ultra-lightweight desktop and edge inference via ONNX Runtime without requiring PyTorch or heavy CUDA drivers.

📦 Models Included

File Name	Architecture	Task	Source Checkpoint	Size
`sam_image_encoder.onnx`	Vision Transformer (ViT-B)	Deep Image Embedding extraction	`reefsupport/CoralSCOP`	342 MB
`sam_mask_decoder.onnx`	SAM Lightweight Mask Decoder	Instance Mask Generation & IoU Prediction	`reefsupport/CoralSCOP`	16 MB
`bioclip_visual.onnx`	ViT-B/16 Vision Transformer	Zero-Shot Coral Taxonomy Features	`ReefNet/finetuned-bioclip`	329 MB
`coral_taxonomy_embeddings.npy`	Pre-computed cosine text matrix	18 Benthic Coral Taxa embeddings	Pre-encoded with BioCLIP	36 KB
`bleaching_yolo11n.onnx`	YOLO11n Classification	Coral Bleaching Condition Assessment	`NMFS-OSI/yolo11n-cls-noaa-esd-coral-bleaching`	5.9 MB

🙏 Credits & Author Attributions

We give full credit to the original research teams and institutions whose open-source foundation models power this suite:

1. CoralSCOP SAM ViT-B

Model Card / Repo: reefsupport/CoralSCOP
Developed by: Reef Support B.V. & EPFL Environmental Computational Science and Earth Observation Laboratory (ECEO).
Base Architecture: Meta AI Segment Anything (segment-anything by Kirillov et al., ICCV 2023).
License: Apache 2.0 / Meta SAM Research License.

2. ReefNet Fine-Tuned BioCLIP

Model Card / Repo: ReefNet/finetuned-bioclip
Developed by: ReefNet Marine Vision Team.
Base Architecture: BioCLIP ("BioCLIP: A Vision Foundation Model for the Tree of Life", Stevens et al., CVPR 2024 / Imageomics Institute).
License: MIT License.

3. NOAA ESD Coral Bleaching Classifier

Model Card / Repo: NMFS-OSI/yolo11n-cls-noaa-esd-coral-bleaching
Developed by: National Oceanic and Atmospheric Administration (NOAA) Fisheries, Ecosystem Sciences Division (ESD).
Base Architecture: Ultralytics YOLO11n Classifier.
License: AGPL-3.0 / Open Government Work.

🚀 Quickstart with ONNX Runtime

import onnxruntime as ort
import numpy as np
from huggingface_hub import hf_hub_download

# Download and run Bleaching Classifier
model_path = hf_hub_download(
    repo_id="harsh-awasthi/Andromeida-ReefVision-ONNX",
    filename="bleaching_yolo11n.onnx"
)
session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
dummy_input = np.random.randn(1, 3, 224, 224).astype(np.float32)
output = session.run(None, {"images": dummy_input})
print("Bleaching probabilities (Healthy vs Bleached):", output[0])

🛡️ License

The ONNX weights provided here inherit the respective open-source licenses from their original creators. Please refer to the upstream repositories for commercial use and scientific citation guidelines.

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