I am running the TensorFlow SSD-Inception V2 COCO object detection model on images captured from a 4K camera. After cropping the image vertically, my input image size is 3840x1400. I have found that when using the model in OpenCV, I still get successful/valid detections when passing in the entire 3840x1400 input into the CNN without resizing it and specifying (3840, 1400) as the size in my cv2.dnn.blobFromImages call. However, when I run the same model in TensorFlow, it first resizes the input to 300x300 and therefore I miss a lot of detections in my huge image.
My question is: how is OpenCV handling this large input size successfully? Is it tiling the image first, or does it modify the network at all to handle any size input image?
Thanks!