# vgg11_imagenet

|Module Name|vgg11_imagenet|
| :--- | :---: |
|Category|image classification|
|Network|VGG|
|Dataset|ImageNet-2012|
|Fine-tuning supported or not|No|
|Module Size|507MB|
|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 VGG11, trained on ImageNet-2012, and can predict an image of size 224*224*3.

## II.Installation

- ### 1、Environmental Dependence  

  - paddlepaddle >= 1.4.0  

  - paddlehub >= 1.0.0  | [How to install PaddleHub]()


- ### 2、Installation

  - ```shell
    $ hub install vgg11_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

  - ```shell
    $ hub run vgg11_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](../../../../docs/docs_ch/tutorial/cmd_usage.rst)

- ### 2、预测Prediction Code Example

  - ```python
    import paddlehub as hub
    import cv2

    classifier = hub.Module(name="vgg11_imagenet")
    test_img_path = "/PATH/TO/IMAGE"
    input_dict = {"image": [test_img_path]}
    result = classifier.classification(data=input_dict)
    ```

- ### 3、API

  - ```python
    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

  - ```shell
    $ hub install vgg11_imagenet==1.0.0
    ```