Spaces:
				
			
			
	
			
			
					
		Running
		
	
	
	
			
			
	
	
	
	
		
		ο»ΏMany cutting-edge computer vision models consist of multiple stages:
β° backbone extracts the features,
β° neck refines the features,
β° head makes the detection for the task.
Implementing this is cumbersome, so π€ transformers has an API for this: Backbone!  
Let's see an example of such model. Assuming we would like to initialize a multi-stage instance segmentation model with ResNet backbone and MaskFormer neck and a head, you can use the backbone API like following (left comments for clarity) π
One can also use a backbone just to get features from any stage. You can initialize any backbone with AutoBackbone class. 
See below how to initialize a backbone and getting the feature maps at any stage π 
Backbone API also supports any timm backbone of your choice! Check out a variation of timm backbones here.
Leaving some links π:
π I've created a notebook for you to play with it
π Backbone API docs
π AutoBackbone docs π
(all written with love by me!)  
Orignial tweet (January 23, 2024)

 
			


