preferred than rotated images. That is, images with 0, 90, 180, and 270 degrees’ rotations are preferred than images with arbitrary rotations, and images with less than 10 degrees’ rotation are preferred than images with other arbitrary rotations. Specifically, for each training image, we have 25% chance to select non-rotated image; 25% chance to rotate images by $90$x degrees; 25% chance to rotate images by less than $\pm 10$ degrees plus a random $90$x rotation; and the last 25% chance to rotate images for any angle.

Another important parameter is that we use a large input resolution for both training and testing due to large number of objects and small object sizes in each image. During training, we resize the image so that the longer side randomly ranges from $832$ to $1664$, and then crop a subregion of $416\times 416$ as the network input. During testing, we resize the input image so that the whole area is close to $1248\times 1248$ while its aspect ratio is kept. Non-Maximum Compression (NMS) is used to filter the bounding box and the IoU threshold is 0.2.

The network backbone is Darknet19, which is the same as in YoloV2 [@RedmonF17]. We use 9 stages 