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Ultralytics Yolo Deepsort Bytetrack Pyqt Gui

Ultralytics Yolo Deepsort Bytetrack Pyqt Gui is a Python desktop application that runs Ultralytics YOLO models in ONNX for object detection, pose estimation, segmentation and oriented bounding boxes. It supports DeepSort and ByteTrack tracking, with local images and videos, camera input and RTSP streams as sources.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Ultralytics Yolo Deepsort Bytetrack Pyqt Gui — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want to run supported YOLO vision tasks and trackers through a desktop GUI against local media, camera input or an RTSP stream.

Who it is for

  • People reviewing object detections and tracks in local video footage.
  • Users applying pose estimation or segmentation to images and video.
  • Developers exploring a desktop interface for camera or RTSP computer-vision analysis.

Documented uses

  • Run object detection on images, video files or camera streams.
  • Track detected objects with DeepSort or ByteTrack.
  • Apply pose estimation, segmentation or oriented bounding-box tasks.
Before you choose

Requirements & limits

The repository is public and identifies the project as MIT-licensed.

  • The documented model families are YOLO26, YOLOv13 and YOLO11, with several listed sizes.
  • The application uses ONNX models and requires dependency installation and model-weight downloads.
  • Supported trackers are DeepSort and ByteTrack.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and launch the desktop GUI

Prepare the documented Python environment, download model weights and start the application.

Before you start

  • The project repository
  • Python and pip, or conda

Steps

  1. Install dependencies with the command below, or create the conda environment with conda env create -f environment.yml and activate it with conda activate yolo_gui.

    Documented command
    pip install -r requirements.txt

  2. Download model weights by running the command below; the README says the files are saved in the weights/ folder.

    Documented command
    python download_weights.py

  3. Launch the GUI with the command below.

    Documented command
    python main.py

Expected outcome

The documented setup and launch sequence for opening the application.

Guide limitations

  • The README does not specify hardware requirements or provide a separate workflow for configuring a camera or RTSP stream.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
jingh-ai/ultralytics-YOLO-DeepSort-ByteTrack-PyQt-GUI · Official homepage title · retained in its source language
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