VERIDIS Dataset

Overview

This repository contains the VERIDIS dataset, a collection of annotated agricultural images for crop detection and identification. The dataset comprises field images of beet and corn crops captured by a ground-level robotic platform, organized in YOLO format for object detection tasks.

The dataset includes 10,077 total files distributed across training (9,100 files), validation (488 files), and test (489 files) sets, with images of two crop types: beet (Class 0) and corn (Class 1).

Repository Contents

Main Dataset

• veridis.zip
  • Main dataset containing the complete collection of data for identity verification research
  • Recently updated with the latest version of the dataset
  • Format: Compressed ZIP archive

Processing Scripts

1. anonymize_persons.py

Script designed for anonymizing personal data within the dataset. It implements privacy-preserving techniques to protect personally identifiable information while maintaining data utility for research purposes.

2. data_augmentation.py

Data augmentation script that expands the dataset through systematic transformations and variations generation.

3. extract_frames.py

Tool for extracting individual frames from video sequences or temporal data.

4. train.py

Main training script for machine learning and deep learning models on the VERIDIS dataset.

License

This project is licensed under CC-BY-SA-4.0 (Creative Commons Attribution-ShareAlike 4.0 International).