README_en.md

# densenet169_imagenet

|Module Name|densenet169_imagenet|
| :--- | :---: |
|Category|image classification|
|Network|DenseNet|
|Dataset|ImageNet-2012|
|Fine-tuning supported or not|No|
|Module Size|59MB|
|Latest update date|-|
|Data indicators|-|


## I.Basic Information



- ### Module Introduction

  - DenseNet is the model in CVPR2017 best paper. Every layer outputs its result as input for the layer after it, and forms the dense connection topology. The dense connection ease the probblem of vanishing gradient and improve the information flow. This module is based on DenseNet169, 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 densenet169_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 densenet169_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="densenet169_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 densenet169_imagenet==1.0.0
    ```