✏️ Web-based image segmentation tool for object detection, localization, and keypoints

Cityscapes to CoCo Format Conversion Tool for Mask-RCNN and Detectron

Utility scripts for COCO json annotation format

Fast alternative to FiftyOne for creating a subset of the COCO dataset.

Tool for working with images annotations. Allows you to convert, modify and analyze annotations to images of such formats as Yolo, COCO, LabelMe, etc.

Convert annotations from VIA to COCO.

Vehicle make and model recognition (VMMR) using BVMMR dataset v2

Dataset prep tool for object detection tasks

A dataset for semantic segmentation of Sosnowsky's hogweed in the ground-level view photos taken in St. Petersburg, Malaya Vishera, Pushkin, etc.

mask rcnn training with coco-like dataset. You can use for trainnig your own coco.json (polygon) dataset in Google Colab.

📦 HBB2OBB is a Python library for converting horizontal (axis-aligned) bounding boxes to oriented (rotated) bounding boxes using SAM-based segmentation. Includes tools for visualization, evaluation, format conversion, and hyperparameter tuning.

Object Detection Tools to convert between dataset formats, image splitting and stitching.

Deep Learning project for waste object detection and segmentation using YOLOv8 and U-Net on the TACO dataset. Implements object detection with YOLOv8 for bounding boxes and custom U-Net for pixel-wise segmentation. Includes EDA, data augmentation pipeline, and comparative analysis. Built with PyTorch for CS4045 course project.

Aerial Object Detection in Earth Vision: A model for monitoring harbour ports and airbases using satellite. 🛰️✈️

Pytorch implementation of homework 3 for VRDL course in 2021 Fall semester at NYCU.

Image Annotation Tool - A modern desktop application for creating bounding box annotations on images. Export to COCO, YOLO, and Pascal VOC formats with a single click. Built with Python and Tkinter.

Pose2COCO Converter is a tool designed to transform pose annotations generated by OpenPose into the COCO format. This conversion is crucial for using pose data in COCO-based machine learning models and frameworks.