Resnet Operator

Authors: derekdqc, shiyu22

Overview

This Operator generates feature vectors from the pytorch pretrained Resnet model[1], which is trained on imagenet dataset.

Resnet models were proposed in “Deep Residual Learning for Image Recognition”[2], this model was the winner of ImageNet challenge in 2015. "The fundamental breakthrough with ResNet was it allowed us to train extremely deep neural networks with 150+layers successfully. Prior to ResNet training very deep neural networks were difficult due to the problem of vanishing gradients"[3].

Interface

__init__(self, model_name: str, framework: str = 'pytorch')

Args:

• model_name:
  • the model name for embedding
  • supported types: str, for example 'resnet50' or 'resnet101'
• framework:
  • the framework of the model
  • supported types: str, default is 'pytorch'

__call__(self,  image: 'towhee.types.Image')

Args:

image:

• the input image
• supported types: towhee.types.Image

Returns:

The Operator returns a tuple Tuple[('feature_vector', numpy.ndarray)] containing following fields:

• feature_vector:
  • the embedding of the image
  • data type: numpy.ndarray
  • shape: (dim,)

Requirements

You can get the required python package by requirements.txt.

How it works

The towhee/resnet-image-embedding Operator implements the function of image embedding, which can add to the pipeline. For example, it's the key Operator named embedding_model within image-embedding-resnet50 pipeline and image-embedding-resnet101.

Reference

[1].https://pytorch.org/hub/pytorch_vision_resnet/

[2].https://arxiv.org/abs/1512.03385

[3].https://towardsdatascience.com/understanding-and-coding-a-resnet-in-keras-446d7ff84d33