# 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 是 CVPR 2017 最佳论文的模型，DenseNet 以前馈方式将每一层与其他层连接，从而 L 层网络就有 L(L+1)/2 个直接连接.对于每一层，其输入是之前的所有层的特征图，而自己的特征图作为之后所有层的输入.DenseNet 缓解了梯度消失问题，加强特征传播，促进了特征重用，并大幅减少了Parameters量.该PaddleHub Module结构为 DenseNet169，基于ImageNet-2012数据集训练，接受输入图片大小为 224 x 224 x 3，支持直接通过命令行或者Python接口进行预测.

## 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
    ```