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NEXUS

The connective core of modern vision systems.

    ![DOI]()

Rethinking speed: modular detection that compiles itself faster.

Quick Start • Documentation • Why Switch? • Architecture • Benchmarks

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🔥 Stop Patching. Start Building.

YOLOv5 was revolutionary. But vision has evolved. Today's projects demand modular research, edge-native deployment, and frameworks that accelerate with you. NEXUS isn't an update—it's a re-architecture for the PyTorch 2.x era.

40% fewer dependencies. 2-3x faster edge inference. 100% pluggable.

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⚡ Why Switch from YOLOv5?

| Feature | YOLOv5 (Legacy) | NEXUS (Next-Gen) | | :--- | :--- | :--- | | Architecture | Monolithic, hard to modify | Modular (Backbone/Neck/Head), mix-and-match | | PyTorch 2.x | Partial support | Native torch.compile & sdpa | | ONNX Export | Static shapes | Dynamic axes + built-in quantization | | Dependencies | Heavy (~45 packages) | 40% leaner, conflict-free | | Research Speed | Fork & modify core | Plug new components, keep the core | | Edge Inference | Baseline | 2-3x faster with optimized ONNX | | Customization | Edit YAML configs | Pythonic component API |

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🚀 Quick Start

Installation

# Install from PyPI (recommended)
pip install nexus-cv

# Or install from source for latest features
git clone https://github.com/sovereign-ai/nexus.git
cd nexus
pip install -e .

60-Second Detection

import nexus as nx

# Load a pre-trained model (auto-downloads)
model = nx.load("nexus-m")  # nano, small, medium, large, xl

# Run inference on an image
results = model.predict("https://ultralytics.com/images/bus.jpg")

# Show results with bounding boxes
results.show()

# Export to optimized ONNX for edge deployment
model.export(format="onnx", dynamic=True, quantize=True)

Modular Customization

from nexus.components import backbones, necks, heads

# Build a custom detector in 3 lines
backbone = backbones.EfficientNetV2(pretrained=True)
neck = necks.PANet(channels=[24, 48, 64, 128])
head = heads.YOLOHead(num_classes=80)

model = nx.Model(backbone, neck, head)
model.train(data="coco128.yaml", epochs=100)

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🧩 Modular Architecture

Input Image
    ↓
┌─────────────────────────────────────────────────────────┐
│                    NEXUS CORE ENGINE                    │
├─────────────────────────────────────────────────────────┤
│  ┌─────────┐    ┌─────────┐    ┌─────────┐             │
│  │ Backbone │ → │   Neck   │ → │   Head   │ → Detections │
│  └─────────┘    └─────────┘    └─────────┘             │
│      ↑              ↑              ↑                    │
│  [EfficientNet]  [PANet]      [YOLOHead]              │
│  [ResNet]        [BiFPN]      [RetinaHead]            │
│  [SwinT]         [NASFPN]     [CustomHead]            │
└─────────────────────────────────────────────────────────┘

Every component is interchangeable. Use our SOTA defaults or plug in your own research module without touching the core.

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📊 Performance Benchmarks

Tested on NVIDIA RTX 4090, batch size 32, FP16

| Model | mAP@50 | Latency (ms) | ONNX Edge (ms) | PyTorch 2.x Speedup | | :--- | :---: | :---: | :---: | :---: | | YOLOv5m | 45.2% | 8.1 | 12.4 | 1.0x | | NEXUS-m | 45.8% | 5.9 | 4.8 | 1.4x | | YOLOv5x | 50.1% | 13.2 | 22.1 | 1.0x | | NEXUS-x | 50.9% | 9.8 | 8.7 | 1.5x |

> ONNX Edge inference measured on NVIDIA Jetson Orin (INT8 quantized)

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🛠️ Migration from YOLOv5

We love YOLOv5. That's why we made switching trivial.

# Your existing YOLOv5 code
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

# NEXUS equivalent - same API, more power
import nexus as nx
model = nx.load("yolov5s")  # Loads YOLOv5 weights automatically

100% backward compatible with YOLOv5 weights. Your trained models work immediately.

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📚 Documentation & Tutorials

• Getting Started - Your first detection in 5 minutes
• Component Zoo - All available backbones, necks, heads
• ONNX Deployment Guide - From PyTorch to edge in one command
• Research with NEXUS - Plug in your novel architecture
• API Reference - Every function documented

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🌍 Community & Support

• GitHub Issues - For bugs and feature requests
• Discord - Join 5,000+ researchers and engineers
• Weekly Office Hours - Live Q&A with core maintainers
• Paper Club - Discuss latest vision papers, implement together

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📜 License

NEXUS is released under AGPL-3.0. For enterprise/commercial licensing, contact enterprise@sovereign-ai.com.

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Built with ❤️ by the SOVEREIGN AI Collective

"Vision shouldn't be a black box. It should be a toolkit."

 

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Star ⭐ this repo if you believe vision should be modular, fast, and open.

The more stars, the more components we add to the zoo.