bharath5673/Deepstream

bharath5673/Deepstream has 122 GitHub stars and 27 forks. GitHot measured 0 stars in 24 hours. Last checked 2026-11-12 04:42 UTC. Last push 2025-12-07.

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yolov3, yolo12, dino, segmenations, face, pose, keypoints on deepstream

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README.md

Original

DeepStream 8.0 – Ultra-Optimized AI Video Analytics Stack

🔖 EXCLUSIVE Release – Fully Optimized • Low-Code • Docker-Ready

YOLO Detection • YOLO Pose • Tracking • ROI Analytics • Multi-Stream Pipelines • Python First
Fully Optimized · Low Code · Docker Ready · Production Tested

🖥 Recommended System Setup

Component	Recommended / Supported
OS	Ubuntu 24.04 LTS
NVIDIA Driver	570.133.20
CUDA Compatibility	Fully compatible with DeepStream 8.0
DeepStream Version	DeepStream 8.0 (Production Ready)
Docker Support	Yes – NVIDIA Container Runtime required
Bare Metal Support	Supported (Native DS 8.0 Install)

✔️ Fully Docker Compatible ✔️ Supports Bare-Metal ✔️ Works for Python & C++ pipelines ✔️ Optimized for YOLOv5/YOLOv8/YOLO-Pose/Custom CNNs

⚡ Quick Start (1 Step)

Setup your GPU + environment → Pull repo → Run QuickTest.sh

Install NVIDIA driver

Follow NVIDIA official quick install:

🔗 https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html

Clone this Repo and Run Quick Demo

git clone https://github.com/bharath5673/Deepstream.git

cd Deepstream
bash QuickDemo.sh

Runs instantly with DS8.0-ready configs:

YOLO Detection
YOLO Pose
Tracking
Multi-Model + Multi-Stream
ROI analytics

🎯 What This Repo Provides

✔️ Docker-Ready

Run your inference stack inside a fully isolated DeepStream 8.0 Docker environment. Just clone the prebuilt YOLO DS Docker image and start running demos instantly.

✔️ DeepStream 8.0 Templates (Production Ready)

Multi-model pipelines
YOLO detection & pose estimation
Trajectory tracking
ROI-based counting
Multi-stream tiled processing
Triton-ready configurations
Python & C++ implementations

✔️ Fully-Optimized & Low-Code

Minimal coding required — just edit config files and run. Get maximum performance with minimal effort.

🌟 Showcase Gallery

🔥 Multi-Model Pipeline

🔗 DeepStream-Configs/DeepStream-MultiModel

🟦 ROI Based Counting (Python)

🔗 DeepStream-Python/

🟧 Yolo POSE

🔗 DeepStream-Python/

⚙️ Custom CNN → DeepStream in 3 Steps

🔗 CNN-to-DeepStream/

⚡ Quick Demo

cd Deepstream
bash QuickDemo.sh

📂 Repo Structure

Deepstream/
│
├── DeepStream-Configs/
│   ├── DeepStream-MultiModel/
│   ├── test/ (multi-stream, tiling, custom pipelines)
│
├── DeepStream-Python/
│   ├── yolo
│   ├── yolo + pose
│   ├── ROI counting
│   ├── trajectory tracking
│
├── CNN-to-DeepStream/
│
└── QuickTest.sh

🙏 Acknowledgements

Massive respect to the open-source community powering the DeepStream 8.0 ecosystem.
Models, configs, tracking logic, pose models, and deployment workflows are built on top of these amazing projects.

🔰 Credits & Sources

🟩 YOLO Ecosystem

🟦 Core AI / CV Architectures

🟧 NVIDIA + DeepStream + Metropolis

NVIDIA DeepStream SDK
NVIDIA Metropolis documentation
NVIDIA TensorRT & ONNX conversion tools
NVIDIA samples & reference apps

🔵 Tracking, ROI, Multi-Model Inspirations

NvDCF + KLT Tracker designs
MOT community publications
ROI analytics from DS sample apps
Common open-source tracking repos

⭐ Special Thanks

Thank you to every researcher, engineer, and developer who has contributed to
YOLO, tracking algorithms, CNN architectures, and DeepStream integration guides.

This project stands on the shoulders of giants.