Computer Vision Systems • Object Detection (R-CNN, YOLO, Single-stage vs Two-stage)Hard⏱️ ~2 min
Production Trade-Offs: When to Choose Two Stage vs Single Stage Detectors
Choosing between two stage and single stage detectors requires balancing speed, accuracy, deployment constraints, and business requirements. Two stage detectors win on mean Average Precision, especially for small objects, overlapping instances, and long tail classes. A Faster R CNN model might achieve 42 mAP on COCO at IoU 0.5 to 0.95, while a comparable single stage model reaches 38 to 40 mAP. For medical imaging review or content safety where recall on rare violations matters, that 2 to 4 mAP difference translates to catching 5 to 10 percent more true positives.
Single stage models dominate when latency or energy budgets are tight. A 33 millisecond per frame requirement for 30 frames per second video leaves only 15 to 20 milliseconds for detection after preprocessing and non maximum suppression overhead. Two stage models at 80 to 150 milliseconds cannot meet this. For mobile or edge deployments, single stage models consume less memory, compress better to INT8, and fit on devices with limited compute. A 200 megabyte Faster R CNN model will not run on a mobile Neural Processing Unit, but a quantized 20 megabyte YOLO model can.
Throughput versus latency creates another dimension. Offline batch jobs processing 100 million images per day can use static batching of 8 to 32 images and tolerate 200 to 400 millisecond per image latency. A cluster of data center GPUs each processing 200 to 400 images per second can finish in under three hours. Here, two stage models with multi scale test time augmentation maximize indexing quality. Real time applications use batch size 1 to 4 with micro batching to control p99 latency, favoring single stage simplicity.
Hybrid cascades offer a middle ground. A fast single stage filter runs on every frame, forwarding only low confidence or high risk detections to a slower two stage specialist. This cuts average compute by 50 to 80 percent while recovering 1 to 2 mAP points on hard cases. E commerce moderation stacks and social platforms commonly deploy such cascades. Google's content understanding pipelines have used similar triage patterns to balance scale and quality.
💡 Key Takeaways
•Two stage models deliver 2 to 4 mAP higher accuracy on COCO, critical when 5 to 10 percent more recall on rare classes matters for safety or compliance
•Single stage models meet 15 to 20 millisecond detection budgets for 30 frames per second real time video, while two stage 80 to 150 millisecond latency cannot
•Offline batch jobs tolerate 200 to 400 millisecond latency and use two stage models with static batching of 8 to 32 images for maximum mAP on 100 million plus images per day
•Hybrid cascades use fast single stage filters to triage, forwarding 20 to 50 percent of cases to two stage specialists, cutting compute by 50 to 80 percent with 1 to 2 mAP recovery
•Mobile deployment favors single stage: quantized 20 megabyte models fit on device with 2 percent accuracy drop, while 200 megabyte two stage models exceed memory limits
📌 Examples
Meta content safety using Faster R CNN offline for uploaded images at 300 millisecond latency, prioritizing recall on policy violations over speed
Tesla automotive perception using single stage detectors at 20 millisecond per camera cycle on 6 to 8 cameras with strict real time guarantees
Amazon product moderation cascade: YOLO filter at 10 milliseconds flags suspicious items, Faster R CNN specialist at 150 milliseconds reviews flagged cases, reducing average latency from 150 to 40 milliseconds