title: Non-squared image as input of YOLOv3
body: <p>I am trying to detect road objects (that are very small) using yolov3. For that, my dataset is composed of images of size 3840x400 px. The network implementation I am currently using (pytorch implementation by ultralytics <a href="https://github.com/ultralytics/yolov3" rel="nofollow noreferrer">1</a>) takes as input squared images. If, for instance, I pass through it a 3840x400 px image and I set the img_size to 1000 px, it resizes the image to 1000x400 px and then it squares it up to 1000x1000 px (letterboxing these 600 remaining pixels with black values in order to not change the height aspect). Most implementations of CNNs out there seems to work with squared images as the input of the network architecture. My question is: Will yolo still work if I manually change the network architecture to a non-squared size (e.g. 1000x400 px). Why most of the CNNs in the art work with squared-images?</p>