What is accuracy in object detection?
Accuracy outputs IoU is the amount of overlap between the bounding box around a predicted object and the bounding box around the ground reference data. For example, if the model detected 100 trees, and 90 were correct, the precision is 90 percent.
How accurate is YOLOv4?
YOLOv4 is highly flexible that can be easily deployed in any production environment. YOLOv4 is found to be much efficient that it can achieve 43.5% Average Precision(AP)/ 65.7% AP50 accuracy in accordance with the Microsoft COCO test and is at the fastest speed of 62 Frames Per Second (FPS) TitanV or 34 FPS RTX 2070.
How to evaluate the performance of an object detection model?
if IoU ≥0.5, classify the object detection as True Positive (TP) if Iou <0.5, then it is a wrong detection and classify it as False Positive (FP) When a ground truth is present in the image and model failed to detect the object, classify it as False Negative (FN).
When to ignore TN in object detection model?
When a ground truth is present in the image and model failed to detect the object, classify it as False Negative (FN). True Negative (TN ): TN is every part of the image where we did not predict an object. This metrics is not useful for object detection, hence we ignore TN.
How are precision and recall calculated in object detection?
Precision and Recall are calculated using true positives (TP), false positives (FP) and false negatives (FN). Calculate precision and recall for all objects present in the image.
What does an IOU of 1 mean for object detection?
An IoU of 1 implies that predicted and the ground-truth bounding boxes perfectly overlap. You can set a threshold value for the IoU to determine if the object detection is valid or not not. When a ground truth is present in the image and model failed to detect the object, classify it as False Negative (FN).