I am looking at training the Scaled YOLOv4 on TensorFlow 2.x, as can be found at this link. I plan to collect the imagery, annotate the objects within the image in VOC format, and then use these images/annotations to train the large-scale model. If you look at the multi-scale training commands, they are as follows:
python train.py --use-pretrain True --model-type p5 --dataset-type voc --dataset dataset/pothole_voc --num-classes 1 --class-names pothole.names --voc-train-set dataset_1,train --voc-val-set dataset_1,val --epochs 200 --batch-size 4 --multi-scale 320,352,384,416,448,480,512 --augment ssd_random_crop
As we know that Scaled YOLOv4 (and any YOLO algorithm at that) likes image dimensions divisible by 32, I have plans to use larger images of 1024x1024. Is it possible to modify the
--multi-scale commands to include larger dimensions such as 1024, and have the algorithm run successfully?
Here is what it would look like when modified: