README_en.md

se_resnext101_32x4d_imagenet

Module Name	se_resnext101_32x4d_imagenet
Category	image classification
Network	SE_ResNeXt
Dataset	ImageNet-2012
Fine-tuning supported or not	No
Module Size	191MB
Latest update date	-
Data indicators	-

I.Basic Information

• Module Introduction

  • Squeeze-and-Excitation Network is proposed by Momenta in 2017. This model learns the weight to strengthen important channels of features and improves classification accuracy, which is the champion of ILSVR 2017. This module is based on se_resnext101_32x4d, trained on ImageNet-2012, and can predict an image of size 2242243.

II.Installation

• 1、Environmental Dependence

  • paddlepaddle >= 1.4.0

  • paddlehub >= 1.0.0 | How to install PaddleHub

• 2、Installation

  • $ hub install se_resnext101_32x4d_imagenet

  • In case of any problems during installation, please refer to: Windows_Quickstart | Linux_Quickstart | Mac_Quickstart

III.Module API Prediction

• 1、Command line Prediction

  • $ hub run se_resnext101_32x4d_imagenet --input_path "/PATH/TO/IMAGE"

  • If you want to call the Hub module through the command line, please refer to: PaddleHub Command Line Instruction

• 2、Prediction Code Example

  • import paddlehub as hub
    import cv2
    
    classifier = hub.Module(name="se_resnext101_32x4d_imagenet")
    test_img_path = "/PATH/TO/IMAGE"
    input_dict = {"image": [test_img_path]}
    result = classifier.classification(data=input_dict)

• 3、API

  • def classification(data)

    • classification API.

    • Parameters

      • data (dict): key is "image", value is a list of image paths

    • Return

      • result(list[dict]): classication results, each element in the list is dict, key is the label name, and value is the corresponding probability

IV.Release Note

• 1.0.0

  First release

  • $ hub install se_resnext101_32x4d_imagenet==1.0.0