Enterprise-grade computer vision platform for advanced image and video analysis
DeepScope is a powerful, production-ready computer vision platform that combines state-of-the-art machine learning models with a modern web interface. Built for scalability and performance, it provides comprehensive solutions for image classification, object detection, segmentation, and video analysis.
- 🚀 High Performance: Optimized inference pipeline with GPU/CPU support
- 🎯 Multiple Models: Support for various CV tasks (detection, classification, segmentation)
- 📊 Real-time Processing: WebSocket-based live video analysis
- 🔌 REST API: Well-documented RESTful API with OpenAPI/Swagger
- 🐳 Docker Ready: Complete containerization with Docker Compose
- 📈 Scalable: Microservices architecture with horizontal scaling
- 🔒 Secure: JWT authentication, rate limiting, input validation
- 📱 Modern UI: React-based responsive web interface
- 💾 Object Storage: S3-compatible storage with MinIO
- 📊 Monitoring: Prometheus metrics and health checks
Get up and running in 5 minutes:
# Clone repository
git clone https://github.com/your-org/deepscope.git
cd deepscope
# Initialize environment
make init
# Start development environment
make dev
# Access the application
open http://localhost:3000That's it! 🎉 Your DeepScope instance is running.
For detailed setup instructions, see SETUP.md.
This documentation is organized into the following sections:
- SETUP.md - Installation and configuration guide
- Quick start (5-minute setup)
- Detailed installation for Linux, macOS, Windows
- Docker setup
- Development environment
- Troubleshooting
- ARCHITECTURE.md - System architecture and design
- System overview
- Component architecture
- Data flow diagrams
- Technology stack
- Design decisions
- API.md - Complete API documentation
- Authentication
- Endpoints reference
- Request/response examples
- Error handling
- Rate limiting
- WebSocket protocol
- MODELS.md - Machine learning models documentation
- Available models
- Model selection guide
- Performance comparison
- Custom model integration
- Training and fine-tuning
- DEPLOYMENT.md - Production deployment guide
- Docker deployment
- Kubernetes deployment
- Cloud platforms (AWS, GCP, Azure)
- Scaling strategies
- Monitoring and logging
- Security best practices
- BENCHMARKS.md - Performance benchmarks and optimization
- Benchmark results
- Performance comparison
- Optimization tips
- Hardware recommendations
- CONTRIBUTING.md - Contribution guidelines
- Code of conduct
- Development workflow
- Coding standards
- Testing requirements
- Pull request process
- examples/ - Code examples and tutorials
- Python client examples
- JavaScript client examples
- cURL command examples
- Integration tutorials
| Component | Technology | Version |
|---|---|---|
| Framework | FastAPI | 0.115+ |
| Language | Python | 3.13 |
| ML Framework | PyTorch | 2.5+ |
| Computer Vision | OpenCV | 4.10+ |
| Database | PostgreSQL | 16+ |
| Cache | Redis | 7+ |
| Object Storage | MinIO | Latest |
| Web Server | Uvicorn | 0.32+ |
| Component | Technology | Version |
|---|---|---|
| Framework | Next.js | 20+ |
| Language | JavaScript/TypeScript | ES2023 |
| UI Library | React | 19+ |
| Styling | Tailwind CSS | 4+ |
| HTTP Client | Axios | Latest |
| Component | Technology | Purpose |
|---|---|---|
| Containerization | Docker | Application packaging |
| Orchestration | Docker Compose / Kubernetes | Container orchestration |
| Reverse Proxy | Nginx | Load balancing, SSL termination |
| Monitoring | Prometheus | Metrics collection |
| Logging | JSON Logger | Structured logging |
DeepScope is ideal for:
- Medical Imaging: Analyze X-rays, CT scans, MRIs
- Manufacturing: Quality control and defect detection
- Retail: Visual search and product recognition
- Security: Object detection and surveillance
- Agriculture: Crop disease detection
- Research: Academic and scientific image analysis
- E-commerce: Product categorization and tagging
┌─────────────────────────────────────────────────────────────┐
│ Client Applications │
│ (Web UI, Mobile Apps, API Clients) │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Nginx Reverse Proxy │
│ (Load Balancing, SSL, Rate Limiting) │
└─────────────────────────────────────────────────────────────┘
│
┌─────────────────┴─────────────────┐
▼ ▼
┌──────────────────┐ ┌──────────────────┐
│ Frontend (Next) │ │ Backend (FastAPI)│
│ - React UI │ │ - REST API │
│ - SSR │ │ - WebSocket │
│ - State Mgmt │ │ - ML Pipeline │
└──────────────────┘ └──────────────────┘
│
┌──────────────────────────────────┼─────────────┐
▼ ▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────┐
│ PostgreSQL │ │ Redis │ │ MinIO │ │ Models │
│ (Metadata) │ │ (Cache) │ │ (Storage) │ │ (Files)│
└─────────────┘ └─────────────┘ └─────────────┘ └─────────┘
For detailed architecture, see ARCHITECTURE.md.
# Get access token
curl -X POST http://localhost:8000/api/v1/auth/login \
-H "Content-Type: application/json" \
-d '{"username": "user", "password": "pass"}'# Analyze single image
curl -X POST http://localhost:8000/api/v1/analyze \
-H "Authorization: Bearer <token>" \
-F "file=@image.jpg" \
-F "model=detection"# Process multiple images
curl -X POST http://localhost:8000/api/v1/batch \
-H "Authorization: Bearer <token>" \
-F "files=@image1.jpg" \
-F "files=@image2.jpg"For complete API documentation, see API.md.
DeepScope delivers industry-leading performance:
| Metric | Value |
|---|---|
| Image Processing | 50-100 images/sec (CPU) |
| Video Processing | 30 FPS (1080p) |
| Latency | < 100ms (p95) |
| Throughput | 10,000 req/hour |
| Concurrent Users | 1,000+ |
For detailed benchmarks, see BENCHMARKS.md.
- 📖 Documentation: You're reading it!
- 💬 Discussions: GitHub Discussions
- 🐛 Issues: GitHub Issues
- 📧 Email: support@deepscope.io
Q: Is DeepScope free? A: Yes! DeepScope is open-source under the MIT License.
Q: Can I use it commercially? A: Yes, the MIT License allows commercial use.
Q: What hardware do I need? A: Minimum: 4GB RAM, 2 CPU cores. Recommended: 16GB RAM, 8 cores, GPU.
Q: Does it support GPU? A: Yes, CUDA-enabled GPUs are supported for faster inference.
Q: Can I add custom models? A: Yes! See MODELS.md for integration guide.
Q: Is it production-ready? A: Yes, with proper configuration. See DEPLOYMENT.md.
Q: Docker build fails A: Ensure Docker has enough memory (8GB+). See SETUP.md.
Q: Out of memory errors A: Reduce batch size or enable model quantization. See MODELS.md.
Q: Slow inference A: Use GPU, enable caching, or use lighter models. See BENCHMARKS.md.
- ✅ Image classification
- ✅ Object detection
- ✅ Image segmentation
- ✅ REST API
- ✅ Docker deployment
- 🔄 Video streaming analysis
- 🔄 Multi-model ensembling
- 🔄 Model training interface
- 🔄 Advanced analytics dashboard
- 📋 3D vision support
- 📋 Federated learning
- 📋 Edge deployment
- 📋 AutoML integration
DeepScope is licensed under the MIT License.
MIT License
Copyright (c) 2024 DeepScope Team
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
Built with amazing open-source technologies:
- FastAPI - Modern Python web framework
- PyTorch - Machine learning framework
- Next.js - React framework
- PostgreSQL - Database
- Redis - Cache
- MinIO - Object storage
- Website: https://deepscope.io
- Email: hello@deepscope.io
- Twitter: @deepscope
- GitHub: github.com/your-org/deepscope
Ready to get started? Head to SETUP.md for installation instructions!
Last updated: 2024-11-03