README_en.md

vgg19_imagenet

Module Name	vgg19_imagenet
Category	image classification
Network	vgg19_imagenet
Dataset	ImageNet-2012
Fine-tuning supported or not	No
Module Size	549MB
Latest update date	-
Data indicators	-

I.Basic Information

• Module Introduction

  • VGG is a serial of models for image classification proposed by university of Oxford and DeepMind. The serial models demonstrate 'the deeper the network is, the better the performance is'. And VGG is used for feature extraction as the backbone by most image classification tasks. This module is based on VGG19, 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 vgg19_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 vgg19_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="vgg19_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 vgg19_imagenet==1.0.0