README_en.md

resnext152_64x4d_imagenet

Module Name	resnext152_64x4d_imagenet
Category	image classification
Network	ResNeXt
Dataset	ImageNet-2012
Fine-tuning supported or not	No
Module Size	444MB
Latest update date	-
Data indicators	-

I.Basic Information

• Module Introduction

  • ResNeXt is proposed by UC San Diego and Facebook AI Research in 2017. This module is based on resnext152_64x4d_imagenet, which denotes 152 layers ，64 branches，and the number of input and output branch channels is 4 in the network. It is weak-supervised trained on billions of socail images, finetuned on ImageNet-2012 dataset, 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 resnext152_64x4d_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 resnext152_64x4d_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="resnext152_64x4d_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 resnext152_64x4d_imagenet==1.0.0