Iwari nkan

O yatọ si tito lẹtọ aworan, eyiti o le fi aami kan laisi wiwa nkan naa, ati pipin, eyiti o ṣe apejuwe awọn agbegbe ipele piksẹli.

Awọn gbigba bọtini

• Define consistent instance annotations.
• Ijabọ tuntun ati awọn eto ẹnu-ọna.
• Ṣe idanwo awọn ohun kekere, ti o farapamọ, ati ti o pọju.

A erin dataset nilo ibamu akole ati ipo annotations. Define how to handle partial hidden objects, very small instances, and ambiguous categories. Inconsistent boxes or omitted objects can confuse both training and evaluation. The model's output usually includes a Dimegilio and a location for each candidate. Postprocessing may remove overlapping duplicate predictions or apply a threshold. Those settings affect the balance between missing objects and false detections and should be recorded with the result. Evaluate localization and category correctness together. Intersection over union measures the overlap between a predicted region and a reference region. Precision and recall also depend on matching rules, Dimegilio thresholds, and which object sizes are included. Test real Capture conditions, including blur, lighting changes, occlusion, and crowded scenes. A detector can appear strong on large isolated objects while missing the small or partial hidden objects that matter in deployment. Define how uncertain detections are reviewed before they trigger consequential actions.

1. Lo awọn apoti 10-nipasẹ-10 meji. Ekeji ti wa ni gbigbe awọn ẹya 5 ni petele, nitorinaa wọn kọja agbegbe 5-nipasẹ-10.
2. Agbegbe ikorita jẹ 50 ati iṣọkan jẹ 100 + 100−50 = 150. Ikorita lori iṣọkan jẹ 50/150, nipa 0.33.
3. Labẹ ẹnu-ọna ti o baamu 0.5, awọn apoti kii yoo ka bi ibaramu agbegbe ti o to laibikita idaran ti o han gbangba.

Geometry ti a ṣe ṣalaye paati igbelewọn kan; kii ṣe itọkasi oluwari.