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This looks like it could be a homework problem, so consider updating it with the homework tag if so. The second model has the same precision, but worse recall, than model 1. Therefore we would rather have model 1 than model 2. The third model has worse recall than model 1, and worse precision than model 1, therefore we would rather have model 1 than model ...


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@Clement Hui Thanks for your answer, I ask AlexeyAB from Darknet the same question and he add now flag for Darknet for this type of model speed measurments: https://github.com/AlexeyAB/darknet/issues/4627 I added -benchmark flag for detector demo, now you can use command 2652263 ./darknet detector demo obj.data yolo.cfg yolo.weights test.mp4 -...


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You can use the dataset test set as "frames" of video. Test the images with your model and calculate the images per second of the result and that is the same as frames per second. However you should set the batch size to 1 as in the real world scenario. You should also display each image with teh corresponding boxes after inference and remove the accuracy ...


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You can use OpenCV's cv2.minAreaRect() to detect oriented/rotated rectangular bounding boxes. Below's an example result from OpenCV-Python-tutorials: Alternatively, you could train a supervised object detection model to output 8 co-ordinate values (x0,y0,x1,y1,x2,y2,x3,y3) of the quadrilateral by training with a labeled oriented-bounding-box dataset. You ...


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Precision is the number of true positives over the number of predicted positives(PP), and recall is the number of true positives(TP) over the number of actual positives(AP) you get. I used the initials just to make it easier ahead. A true positive is when you predict a car in a place and there is a car in that place. A predicted positive is every car you ...


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