README_en.md

food_classification

Module Name	food_classification
Category	image classification
Network	ResNet50_vd_ssld
Dataset	Food Dataset
Fine-tuning supported or not	No
Module Size	91MB
Latest update date	-
Data indicators	-

I.Basic Information

• Module Introduction

  • This module can be used for food classification.

II.Installation

• 1、Environmental Dependence

  • paddlehub >= 2.0.0 | How to install PaddleHub

  • paddlex >= 1.3.7

• 2、Installation

  • $ hub install food_classification

  • 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 food_classification --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="food_classification")
    images = [cv2.imread('/PATH/TO/IMAGE')]
    results = classifier.predict(images=images)
    for result in results:
        print(result)

• 3、API

  • def predict(images)

    • classification API.

    • Parameters

      • images (list[numpy.ndarray]): image data, ndarray.shape is in the format [H, W, C], BGR;

    • Return

      • result(list[dict]): classication results, each element in the list is dict, key is the label name, and value is the corresponding probability
        • category_id (int): category id；
        • category（str）: category name;
        • score（float）: probability

IV.Release Note

• 1.0.0

  First release

  • $ hub install food_classification==1.0.0