|  | @ -18,20 +18,26 @@ who maintains SOTA deep-learning models and tools in computer vision. | 
		
	
		
			
				|  |  | Load an image from path './towhee.jpeg' |  |  | Load an image from path './towhee.jpeg' | 
		
	
		
			
				|  |  | and use the pre-trained ResNet50 model ('resnet50') to generate an image embedding. |  |  | and use the pre-trained ResNet50 model ('resnet50') to generate an image embedding. | 
		
	
		
			
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				|  |  |  *Write the pipeline in simplified style:* |  |  |  | 
		
	
		
			
				|  |  |  |  |  | *Write a same pipeline with explicit inputs/outputs name specifications:* | 
		
	
		
			
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				|  |  |  |  |  | - **option 1:** | 
		
	
		
			
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				|  |  | ```python |  |  | ```python | 
		
	
		
			
				|  |  | import towhee |  |  |  | 
		
	
		
			
				|  |  |  |  |  | from towhee.dc2 import pipe, ops, DataCollection | 
		
	
		
			
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				|  |  | towhee.glob('./towhee.jpeg') \ |  |  |  | 
		
	
		
			
				|  |  |       .image_decode() \ |  |  |  | 
		
	
		
			
				|  |  |       .image_embedding.timm(model_name='resnet50') \ |  |  |  | 
		
	
		
			
				|  |  |       .show() |  |  |  | 
		
	
		
			
				|  |  |  |  |  | p = ( | 
		
	
		
			
				|  |  |  |  |  |     pipe.input('path') | 
		
	
		
			
				|  |  |  |  |  |         .map('path', 'img', ops.image_decode()) | 
		
	
		
			
				|  |  |  |  |  |         .map('img', 'vec', ops.image_embedding.timm(model_name='resnet50')) | 
		
	
		
			
				|  |  |  |  |  |         .output('img', 'vec') | 
		
	
		
			
				|  |  |  |  |  | ) | 
		
	
		
			
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				|  |  |  |  |  | DataCollection(p('towhee.jpeg')).show() | 
		
	
		
			
				|  |  | ``` |  |  | ``` | 
		
	
		
			
				|  |  | <img src="./result1.png" height="50px"/> |  |  |  | 
		
	
		
			
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 |  |  | 
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				|  |  | *Write a same pipeline with explicit inputs/outputs name specifications:* |  |  |  | 
		
	
		
			
				|  |  |  |  |  | <img src="./result.png" height="150px"/> | 
		
	
		
			
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				|  |  |  |  |  | - **option 2:** | 
		
	
		
			
				|  |  | ```python |  |  | ```python | 
		
	
		
			
				|  |  | import towhee |  |  | import towhee | 
		
	
		
			
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				|  | @ -41,7 +47,6 @@ towhee.glob['path']('./towhee.jpeg') \ | 
		
	
		
			
				|  |  |       .select['img', 'vec']() \ |  |  |       .select['img', 'vec']() \ | 
		
	
		
			
				|  |  |       .show() |  |  |       .show() | 
		
	
		
			
				|  |  | ``` |  |  | ``` | 
		
	
		
			
				|  |  | <img src="./result2.png" height="150px"/> |  |  |  | 
		
	
		
			
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				|  |  | <br /> |  |  | <br /> | 
		
	
		
			
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				|  | @ -104,6 +109,13 @@ Save model to local with specified format. | 
		
	
		
			
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				|  |  | 	The path where model is saved to. By default, it will save model to the operator directory. |  |  | 	The path where model is saved to. By default, it will save model to the operator directory. | 
		
	
		
			
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				|  |  |  |  |  | ```python | 
		
	
		
			
				|  |  |  |  |  | from towhee import ops | 
		
	
		
			
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				|  |  |  |  |  | op = ops.image_embedding.timm(model_name='resnet50').get_op() | 
		
	
		
			
				|  |  |  |  |  | op.save_model('onnx', 'test.onnx') | 
		
	
		
			
				|  |  |  |  |  | ``` | 
		
	
		
			
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				|  |  | <br /> |  |  | <br /> | 
		
	
		
			
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				|  |  | ***supported_model_names(format=None)*** |  |  | ***supported_model_names(format=None)*** | 
		
	
	
		
			
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