Computer Vision Systems

It varies by task complexity. Simple binary classifiers can work with 500–1,000 labeled images per class. Object detection typically needs 1,000–5,000 annotated images. Complex segmentation may require 10,000+. We always start with a data audit and often use transfer learning from foundation models to dramatically reduce data requirements — sometimes 100–200 examples are enough with the right approach.

Real-time processing (under 100ms latency) is essential for live video surveillance, autonomous systems, and production line inspection. Batch processing is more cost-effective for document analysis, post-event video review, and large-scale image datasets. We match the architecture to your latency and throughput requirements — and design systems that can scale from batch to real-time as needs evolve.

Edge inference (on-device) wins when you need ultra-low latency, have bandwidth constraints, or handle sensitive data that can't leave the facility. Cloud inference is better for high-complexity models, variable workloads, or when edge hardware costs are prohibitive. Many production systems use both — edge for fast initial detection, cloud for detailed analysis of flagged events.

In most cases, we fine-tune pre-trained models (CLIP, SAM, YOLO) on your data — this gives better accuracy with far less data and training time than building from scratch. Custom architectures from scratch are only justified for highly specialized domains where no suitable foundation model exists. We always benchmark both approaches before committing.

We apply preprocessing pipelines specifically designed for your imaging conditions: adaptive histogram equalization for low light, deblurring filters, and noise reduction. During training, we augment data with simulated degradation so the model learns robustness. For critical applications, we also recommend hardware improvements (lighting, optics) alongside software solutions.