object-detection

SPARC is an intelligent ROS2 Humble-based pick-and-place robotic system integrating MoveIt, YOLOv8-OBB, and Isaac Sim. It enables real-time object detection, motion planning for Franka Panda and UR5 arms, and natural language control via an Ollama-powered chatbot—making robotic manipulation smart, interactive, and human-friendly.

Unofficial (Golang) Go bindings for the Hugging Face Inference API

This repository includes all computer vision, audio, document AI, and multimodal projects.

OpenCV Spatial AI Competition 3rd prize winner project "Automatic Lawn Mower Navigation"

TikTok video scraping and multimodal content analysis tool.

[ICCV 2023] Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment

Train YOLO + VLM with one command. Auto-generate vision-language training data from YOLO labels - no extra labeling needed.

This source code implements our ECCV paper "task-conditioned domain adaptation for pedestrian detection in thermal imagery".

High-accuracy Russian ANPR system built with YOLOv8 for detection and an optimized, custom-trained PyTorch CRNN for OCR.

The Hugging Face API wrapper for Delphi leverages cutting-edge models to deliver powerful features, including object detection, music generation, text classification, sentiment analysis, image segmentation, speech-to-text transcription, and text generation.

Fine-tuning Resnet-50 FPN based DNN for road crack detection on RDD2022

A step-by-step PyTorch guide for improved vehicle type 2D object detection based on Faster-RCNN using transfer learning and data augmentation.

The aim of this project is to develop an efficient computer vision model capable of real-time wildlife detection.

YOLOv26 and RF-DETR object detection for civilians, rescuers, and animals in disaster scenarios. Real-time support for emergency response in floods and structural collapses.

The License Plate Recognition (LPR) System is a computer vision project designed to detect and recognize license plates in images or video streams.

A deep learning project for detecting and classifying teeth as normal or cavity using YOLOv12m. The model is integrated into a web application for real-time dental screening. Designed to enhance early diagnosis accessibility, with future improvements planned for dataset expansion, annotation quality, and robustness to real-world conditions.

An end-to-end tennis video analysis system leveraging Ultralytics YOLOv8 for player and ball detection, multi-object tracking across frames, and a custom CNN for court keypoint extraction, providing data-driven insights into player movement, ball trajectories, and match dynamics using advanced computer vision and deep learning techniques.