Data, Dataset and Code Repository for AI-based shrimp fingerprinting study

Abstract

This repository contains python code that benchmarks local feature–based matching models against a classic computer vision baseline for shrimp re-identification across moulting events. The objective is to evaluate whether modern feature extractors and matchers can reliably re-identify the same shrimp before and after a moult.

Each method is evaluated using two main criteria: • Re-identification accuracy • Percentage of shrimps correctly matched between moults. • Computational efficiency • Runtime per image pair and GPU usage.

***Repository structure**

The repository is organized by method, with each folder containing a standalone fingerprinting pipeline: • d2-net/ – D2-Net feature extraction and shrimp fingerprinting pipeline • LoFTR/ – LoFTR-based matching pipeline • LightGlue/ – LightGlue + SuperPoint pipeline • SuperGluePretrainedNetwork/ – SuperGlue + SuperPoint pipeline • EDM/ – EDM model code and fingerprinting pipeline • xfeat/ – XFeat pipeline and training utilities • SIFT_OCV/ – SIFT baseline implemented using OpenCV

Additional folders: • database/ – ROI cropped imaged dataset , organized by moult stage and shrimp ID • results/ – Output folders for each matching method • Stats/ – Jupyter notebooks, plots, and summary tables for analysis

**Dataset layout**

The dataset structure is: • database/moult1/<shrimp_id>/*.jpg • database/moult2/<shrimp_id>/*.jpg

Notes: • Each <shrimp_id> directory may contain multiple images. • Each shrimp ID is treated as a class. • The scripts perform class-to-class matching, linking shrimp from moult 1 to moult 2.

**Setup**

1. Create a Python 3.10 environment. 2. Install dependencies: 3. pip install -r requirements.txt 4. (Optional) Install a CUDA-enabled PyTorch build for GPU acceleration. The original setup used PyTorch 2.0.1 with CUDA 11.7. 5. Activate the virtual environment (venv).

**Running the experiments**

Each method has its own standalone script, with a configuration block at the top. Paths can be updated directly in the script.

Example commands: SIFT • python SIFT_OCV/SIFT_OCV_fingerpritning.py LightGlue • python LightGlue/lightGlue_fingerprint.py LoFTR • python LoFTR/LoFTR_fingerprinting.py SuperGlue • python SuperGluePretrainedNetwork/superglue_fingerprinting.py D2-Net • python d2-net/D2-net_fingerprint.py EDM • python EDM/EDM_fingerprint.py XFeat • python xfeat/Xfeat.py

**Outputs**

Each method writes its results to a dedicated output folder, including: • score_matrix.csv – Class-to-class similarity matrix • mapping.csv – Hungarian assignment results with inlier counts and match rates • matched_keypoints.csv – Inlier correspondences for the best image pair per assigned class • vis/ – Visualizations of inlier matches after RANSAC filtering • run_resource_report.txt – Runtime statistics and GPU usage

**Analysis and visualization**

The Stats/ folder contains Jupyter notebooks that: • Aggregates results across all methods • Computes re-identification accuracy and efficiency metrics • Generates plots and CSV summary tables used in the final analysis