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Performance Guide

Fovux v1.0.0 focuses on predictable local performance for long-running YOLO workflows.

Metric streaming

The Studio dashboard now prefers metrics.jsonl written directly by the worker. The HTTP stream no longer re-parses the full history every polling tick. Instead:

  • a snapshot is sent on connect
  • file changes are watched
  • only appended rows are streamed after the snapshot

This keeps steady-state CPU usage low for active dashboards.

Dataset inspection

Large datasets benefit from stable mtimes and cached filesystem metadata. When repeatedly inspecting the same dataset:

  • keep labels under a consistent root
  • avoid rewriting annotation files unnecessarily
  • prefer SSD-backed FOVUX_HOME for cache-heavy workflows

Duplicate detection

Perceptual hashing is CPU-bound. For the fastest runs:

  • store input images on local disk instead of network storage
  • avoid background antivirus scans over the dataset root
  • run duplicate scans before opening multiple heavy dashboard sessions

RTSP inference

The RTSP path now uses reconnect backoff and a bounded capture queue. For stable streams:

  • keep CAP_PROP_BUFFERSIZE=1
  • prefer streams that report FPS correctly
  • write outputs to a fast local disk when recording

Benchmarking

Use benchmark_latency after export or quantization to compare checkpoints in the same environment. Small ONNX and INT8 wins are meaningful only when measured on the target hardware profile.