From real-time object detection to intelligent document processing — I build CV systems that see, understand, and act on visual data at scale.
End-to-end vision AI solutions — from data annotation to production deployment
Real-time detection using YOLO, Detectron2, and custom architectures. Multi-object tracking for surveillance, retail, and autonomous systems.
High-accuracy classifiers with transfer learning on ResNet, EfficientNet, and ViT. From medical imaging to industrial quality inspection.
Intelligent video processing with action recognition, anomaly detection, and frame-by-frame analysis for security and manufacturing.
Intelligent document processing with Google Document AI, Tesseract, and custom models. Extract structured data from invoices, forms, and receipts.
Secure face detection, verification, and recognition systems with anti-spoofing. GDPR-compliant identity verification pipelines.
Semantic and instance segmentation using Mask R-CNN and SAM. Pixel-level understanding for medical imaging and autonomous driving.
Battle-tested frameworks and tools for every vision challenge
PyTorch, TensorFlow, Keras, ONNX Runtime, TensorRT
Core FrameworksOpenCV, Detectron2, YOLOv8, Ultralytics, MediaPipe
Detection & TrackingGoogle Vision AI, AWS Rekognition, Azure Computer Vision
Managed APIsFastAPI, Docker, Kubernetes, Triton Inference Server
Production MLOpsTransparent pricing tailored to your computer vision needs
Single model, quick turnaround
1–2 week delivery
Multi-model pipeline
3–5 week delivery
Full vision platform
6–10 week delivery
Computer vision enables machines to interpret visual information — images, videos, and documents. It powers quality inspection in manufacturing, automated document processing, surveillance analytics, retail shelf monitoring, and much more. Any process involving human visual review is a candidate for CV automation.
With proper data and training, custom models regularly achieve 90–99% accuracy depending on the task. I use data augmentation, transfer learning, and extensive validation to maximize performance on your specific use case.
Yes. I optimize and deploy models on NVIDIA Jetson, Google Coral, Raspberry Pi, and mobile devices using TensorRT, ONNX Runtime, and TFLite. Edge deployment enables real-time inference without cloud dependency.
It varies by task. For classification, 500–1,000 images per class is a good start. For detection, 300+ annotated images can work with transfer learning. I can also help set up annotation workflows and use synthetic data generation to bootstrap smaller datasets.
Let's discuss your computer vision requirements and build a solution that delivers measurable results.