Computer Vision Systems
What we do
Computer vision at Algorithmic is led by founders with published research and patents in the field. We build production-grade vision systems that process millions of images and video frames with the reliability and latency that enterprise deployments demand. From real-time object detection on edge devices using optimized ONNX models to large-scale image classification pipelines running on GPU clusters, we bring deep domain experience to every engagement. Our work spans industrial quality inspection, medical imaging analysis, autonomous navigation, document processing, and retail analytics.
Object Detection & Tracking
Real-time detection and localization of objects in images and video streams using YOLO, DETR, and custom single-shot architectures. Multi-object tracking with DeepSORT or ByteTrack for video surveillance, traffic monitoring, and industrial automation. Optimized for throughput on edge devices and cloud GPUs alike.
Image Classification & Recognition
Large-scale classification systems for medical imaging, manufacturing quality inspection, content moderation, and visual search. We implement few-shot learning techniques for domains where labeled data is scarce, and active learning pipelines that continuously improve model accuracy with minimal human annotation effort.
Semantic & Instance Segmentation
Pixel-level scene understanding for autonomous systems, satellite and aerial imagery analysis, medical image segmentation, and industrial defect mapping. We work with U-Net, Mask R-CNN, and Segment Anything depending on accuracy requirements, inference speed constraints, and annotation budget.
OCR & Document Intelligence
Automated document extraction, form processing, receipt scanning, and handwriting recognition for enterprise workflows. We combine traditional OCR engines with transformer-based layout analysis models like LayoutLM to extract structured data from unstructured documents with high accuracy across languages and formats.
How we work together
Problem Definition & Data Strategy
We define the visual task precisely - detection, classification, segmentation, or tracking - and assess your available training data for volume, diversity, and label quality. Where annotation is needed, we design labeling pipelines using Label Studio or Scale AI with quality assurance protocols that prevent noisy labels from undermining model performance.
Model Development & Training
Architecture selection from state-of-the-art models including YOLOv8, DETR, Segment Anything, EfficientNet, and custom architectures where standard models fall short. We train with rigorous cross-validation, data augmentation pipelines using Albumentations, and hyperparameter optimization via Optuna. Every model is benchmarked against domain-specific metrics beyond simple accuracy.
Optimization & Edge Deployment
Model quantization, pruning, knowledge distillation, and TensorRT or Core ML conversion for target hardware - whether cloud GPUs, NVIDIA Jetson edge devices, Apple Neural Engine, or browser-based inference with ONNX.js. We profile inference latency, memory footprint, and power consumption to verify models meet real-world operational constraints.
Integration & Monitoring
REST or gRPC API deployment with batch and streaming inference pipelines, integrated with your existing data infrastructure. We implement prediction logging, confidence score monitoring, data drift detection on input distributions, and automated retraining triggers. Dashboard visualizations let your team monitor model health and annotation quality continuously.
