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.
Core Facts
Timeline (last 90 days)
A model that achieves high mAP on academic benchmarks may perform far worse in production if there is a distribution gap between its training data and real-world scenarios, or if evaluation fails to account for practical factors such as camera noise and occlusion
mAP is highly dependent on dataset distribution, annotation quality and evaluation protocols (such as IoU thresholds and class partitioning)
mAP is one of the most commonly used evaluation metrics in tasks such as object detection and image retrieval
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 (11)
The area under the PR curve is called AP (Average Precision), and mAP (mean Average Precision) is the standard evaluation metric for object detection models.
65%UnverifiedA model that achieves high mAP on academic benchmarks may perform far worse in production if there is a distribution gap between its training data and real-world scenarios, or if evaluation fails to account for practical factors such as camera noise and occlusion
50%UnverifiedmAP is one of the most commonly used evaluation metrics in tasks such as object detection and image retrieval
50%UnverifiedmAP is highly dependent on dataset distribution, annotation quality and evaluation protocols (such as IoU thresholds and class partitioning)
50%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%