Code Documentation

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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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Star ⭐ this repo if you believe vision should be modular, fast, and open.
The more stars, the more components we add to the zoo.