This tutorial explains how to control your image size when training on your own data.

There are 3 hyperparamters control the training size:

• self.input_size = (640, 640)   #(height, width)
• self.multiscale_range = 5
• self.random_size = (14, 26)

There is 1 hyperparameter constrols the testing size:

• self.test_size = (640, 640)

The self.input_size is suggested to set to the same value as self.test_size. By default, it is set to (640, 640) for most models and (416, 416) for yolox-tiny and yolox-nano.

When training on your custom dataset, you can use multiscale training in 2 ways:

1. 【Default】Only specifying the self.input_size and leaving others unchanged.

   If so, the actual multiscale sizes range from:

   [self.input_size[0] - self.multiscale_range*32, self.input_size[0] + self.multiscale_range*32]

   For example, if you only set:

   self.input_size = (640, 640)

   the actual multiscale range is [640 - 5*32, 640 + 5*32], i.e., [480, 800].

   You can modify self.multiscale_range to change the multiscale range.

2. Simultaneously specifying the self.input_size and self.random_size

   self.input_size = (416, 416)
   self.random_size = (10, 20)

   In this case, the actual multiscale range is [self.random_size[0]*32, self.random_size[1]*32], i.e., [320, 640]

   Note: You must specify the self.input_size because it is used for initializing resize aug in dataset.

If you want to train in a single scale. You need to specify the self.input_size and self.multiscale_range=0:

self.input_size = (416, 416)
self.multiscale_range = 0

DO NOT set the self.random_size.