Accepting a YOLO model: batch images and a live RTSP stream
Two checkpoints and mAP alone will not decide. Compare on a stratified batch, then watch an RTSP stream for instability a single score hides.
A training log tells you how a checkpoint performs on your validation split. What decides whether it ships is different: did it get worse in the real scene, and where exactly.
This is doable from the command line, but every variation means editing a script. Easy Infer Station turns the loop into clicks.
Start with a stratified batch
Do not sample randomly. Take a set that reflects deployment: poor lighting, crowded frames, heavy occlusion, each represented.
Run both checkpoints over the same batch and compare side by side. The value is the constant input — same images, same threshold, so any difference belongs to the model.
Then watch a live stream
Static images hide jitter. Point it at a camera URL or a local video and watch three things:
- Is the box stable while the object enters frame?
- Does it flicker or drop during fast motion?
- Do frame rate and memory stay flat over a long run?
Classification works the same way, with labels instead of boxes.
Turn the difference into a sentence
Usually you end up with a claim like “the new checkpoint recalls better at night, but merges overlapping objects.” That sentence is worth more than a 1.2 point mAP delta, because it tells you what to fix next.
Why “no code, no Docker” matters here
Acceptance is a high-frequency action: every retrain, every new device, every customer report. If it needs an environment and a script each time, it gets postponed.
A tool you can double-click turns acceptance into a habit instead of an event.
Download from the Easy Infer Station product page — free, Windows and Linux.