mAP
A core evaluation metric in object detection, computed as the mean of per-class average precision (AP) values to provide a comprehensive measure of a detection model's accuracy. It serves as the standard benchmark metric for comparing object detection models.
Timeline (last 90 days)
mAP (mean average precision) is calculated as follows: plot precision-recall curves under different IoU criteria, compute the area under each curve (AP), and then average over all classes.
mAP is a metric averaged over multiple IoU thresholds or multiple categories, and better reflects a model's overall performance under different precision requirements.
Object detection tasks are commonly measured by mAP (mean Average Precision), while depth estimation uses metrics such as AbsRel (Absolute Relative Error) and δ1 accuracy; the two types of metrics are not interchangeable
When a model trained on European construction site data is deployed directly to Southeast Asian manufacturing workshops, a drop of 10–20 percentage points in mAP is a common phenomenon due to differences in lighting, worker body types, and equipment.
mAP and Top-1 Accuracy are fundamental evaluation metrics commonly tested in CV interviews.
Fine-tuning on a few hundred real data samples on top of synthetic pre-training can often improve mAP by 10 to 20 percentage points.
The effectiveness of object detection model improvements needs to be validated by comparing core metrics such as accuracy (mAP) and inference speed (FPS).
All Facts (8)
PR曲线下方的面积称为AP（Average Precision），mAP（mean Average Precision）是目标检测模型的标准评估指标
95%UnverifiedmAP (mean average precision) is calculated as follows: plot precision-recall curves under different IoU criteria, compute the area under each curve (AP), and then average over all classes.
50%UnverifiedmAP is a metric averaged over multiple IoU thresholds or multiple categories, and better reflects a model's overall performance under different precision requirements.
50%UnverifiedObject detection tasks are commonly measured by mAP (mean Average Precision), while depth estimation uses metrics such as AbsRel (Absolute Relative Error) and δ1 accuracy; the two types of metrics are not interchangeable
50%UnverifiedWhen a model trained on European construction site data is deployed directly to Southeast Asian manufacturing workshops, a drop of 10–20 percentage points in mAP is a common phenomenon due to differences in lighting, worker body types, and equipment.
50%UnverifiedmAP and Top-1 Accuracy are fundamental evaluation metrics commonly tested in CV interviews.
50%UnverifiedFine-tuning on a few hundred real data samples on top of synthetic pre-training can often improve mAP by 10 to 20 percentage points.
50%UnverifiedThe effectiveness of object detection model improvements needs to be validated by comparing core metrics such as accuracy (mAP) and inference speed (FPS).
50%