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# Manipulating Your Training Image Size
This tutorial explains how to control your image size when training on your own data.
## 1. Introduction
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.
## 2. Multi Scale Training
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:
```python
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**
```python
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.**
## 3. Single Scale Training
If you want to train in a single scale. You need to specify the self.input_size and self.multiscale_range=0:
```python
self.input_size = (416, 416)
self.multiscale_range = 0
```
**DO NOT** set the self.random_size.
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