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Interactive 3D/Computer Vision System Design
Final Throughput
296 img/s
Backbone Params
25M
FLOPs/image
4.1 GFLOPs
Precision
FP32
Batch Size
8
Use Case: Object Detection
COCO mAP target ≥50
CV Inference Pipeline - Throughput (img/sec)
Image Input
5,040 img/s
↓
Preprocessing (resize + normalize)
2,520 img/s
↓
Backbone: ResNet50
420 img/s
↓
Feature Pyramid Network (multi-scale)
378 img/s
↓
Detection Head (FPN + ATSS)
296 img/s
Task output: +NMS post-proc
Deployment Pipeline
Trained PyTorch Model
→
TensorRT FP16 Optimization
→
Batch Inference Server (bs=8)
→
Edge / Cloud Deployment
Feature Pyramid Network: extracts features at multiple scales (P3–P7) so the model can detect both small objects (pedestrians at distance) and large objects (vehicles up close) in the same forward pass.
CV System Controls
Backbone
Task
Use Case
Batch Size8
164
Bottleneck: The task head (not the backbone) is typically the throughput bottleneck in production CV systems.

FP16: NVIDIA Tensor Cores execute FP16 matrix ops at 2× the rate of FP32 with <1% accuracy loss.

Computer Vision System Design - Interactive Visualization

A production computer vision system is not just a model - it is a pipeline. Image data enters, gets preprocessed (resize, normalize, augment), passes through a backbone network (ResNet, EfficientNet, or ViT) to extract features, flows through a Feature Pyramid Network for multi-scale detection, and exits via a task-specific head for detection, classification, or segmentation. Deployment adds TensorRT optimization, batching, and edge serving. This interactive demo shows throughput (images/second) at every stage so you can see where the real bottlenecks are.

  • Switch backbone: ViT-B/16 has 3.5× fewer images/sec than ResNet50 at FP32 - transformer cost vs CNN cost
  • FP16 precision: NVIDIA Tensor Cores give ~1.85× speedup with negligible accuracy loss
  • Task head is often the bottleneck: segmentation head (0.35×) vs classification head (0.95×)
  • Use case selector: autonomous driving requires segmentation + EfficientNet; surveillance needs speed + ResNet

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.