# se_resnext50_32x4d_imagenet

|Module Name|se_resnext50_32x4d_imagenet|
| :--- | :---: |
|Category|image classification|
|Network|SE_ResNeXt|
|Dataset|ImageNet-2012|
|Fine-tuning supported or not|No|
|Module Size|107MB|
|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_ResNeXt50_32x4d, 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 se_resnext50_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

  - ```shell
    $ hub run se_resnext50_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](../../../../docs/docs_ch/tutorial/cmd_usage.rst)

- ### 2、预测Prediction Code Example

  - ```python
    import paddlehub as hub
    import cv2

    classifier = hub.Module(name="se_resnext50_32x4d_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 se_resnext50_32x4d_imagenet==1.0.0
    ```