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# PULC Quick Start
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------
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This document introduces the prediction using PULC series model based on PaddleClas wheel.
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## 目录
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- [1. Installation](#1)
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- [1.1 PaddlePaddle Installation](#11)
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- [1.2 PaddleClas Wheel Installation](#12)
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- [2. Quick Start](#2)
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- [2.1 Predicion with Command Line](#2.1)
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- [2.2 Predicion with Python](#2.2)
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- [2.3 Supported Model List](#2.3)
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- [3. Summary](#3)
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<a name="1"></a>
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## 1. Installation
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<a name="1.1"></a>
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### 1.1 PaddlePaddle Installation
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- Run the following command to install if CUDA9 or CUDA10 is available.
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```bash
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python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
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```
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- Run the following command to install if GPU device is unavailable.
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```bash
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python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
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```
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Please refer to [PaddlePaddle Installation](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/en/install/pip/linux-pip_en.html) for more information about installation, for examples other versions.
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<a name="1.2"></a>
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### 1.2 PaddleClas Wheel Installation
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```bash
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pip3 install paddleclas
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```
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<a name="2"></a>
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## 2. Quick Start
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PaddleClas provides a series of test cases, which contain demos of different scenes about people, cars, OCR, etc. Click [here](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip) to download the data.
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<a name="2.1"></a>
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### 2.1 Predicion with Command Line
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```
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cd /path/to/pulc_demo_imgs
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```
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The prediction command:
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```bash
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paddleclas --model_name=person_exists --infer_imgs=pulc_demo_imgs/person_exists/objects365_01780782.jpg
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```
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Result:
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```
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>>> result
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class_ids: [0], scores: [0.9955421453341842], label_names: ['nobody'], filename: pulc_demo_imgs/person_exists/objects365_01780782.jpg
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Predict complete!
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```
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`Nobody` means there is no one in the image, `someone` means there is someone in the image. Therefore, the prediction result indicates that there is no one in the figure.
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**Note**: The "--infer_imgs" argument specify the image(s) to be predict, and you can also specify a directoy contains images. If use other model, you can specify the `--model_name` argument. Please refer to [2.3 Supported Model List](#2.3) for the supported model list.
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<a name="2.2"></a>
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### 2.2 Predicion with Python
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You can also use in Python:
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```python
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import paddleclas
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model = paddleclas.PaddleClas(model_name="person_exists")
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result = model.predict(input_data="pulc_demo_imgs/person_exists/objects365_01780782.jpg")
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print(next(result))
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```
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The printed result information:
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```
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>>> result
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[{'class_ids': [0], 'scores': [0.9955421453341842], 'label_names': ['nobody'], 'filename': 'pulc_demo_imgs/person_exists/objects365_01780782.jpg'}]
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```
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**Note**: `model.predict()` is a generator, so `next()` or `for` is needed to call it. This would to predict by batch that length is `batch_size`, default by 1. You can specify the argument `batch_size` and `model_name` when instantiating PaddleClas object, for example: `model = paddleclas.PaddleClas(model_name="person_exists", batch_size=2)`. Please refer to [2.3 Supported Model List](#2.3) for the supported model list.
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<a name="2.3"></a>
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### 2.3 Supported Model List
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The name of PULC series models are as follows:
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| Name | Intro |
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| --- | --- |
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| person_exists | Human Exists Classification |
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| person_attribute | Pedestrian Attribute Classification |
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| safety_helmet | Classification of Wheather Wearing Safety Helmet |
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| traffic_sign | Traffic Sign Classification |
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| vehicle_attribute | Vehicle Attribute Classification |
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| car_exists | Car Exists Classification |
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| text_image_orientation | Text Image Orientation Classification |
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| textline_orientation | Text-line Orientation Classification |
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| language_classification | Language Classification |
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<a name="3"></a>
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## 3. Summary
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The PULC series models have been verified to be effective in different scenarios about people, vehicles, OCR, etc. The ultra lightweight model can achieve the accuracy close to SwinTransformer model, and the speed is increased by 40+ times. And PULC also provides the whole process of dataset getting, model training, model compression and deployment. Please refer to [Human Exists Classification](PULC_person_exists_en.md)、[Pedestrian Attribute Classification](PULC_person_attribute_en.md)、[Classification of Wheather Wearing Safety Helmet](PULC_safety_helmet_en.md)、[Traffic Sign Classification](PULC_traffic_sign_en.md)、[Vehicle Attribute Classification](PULC_vehicle_attribute_en.md)、[Car Exists Classification](PULC_car_exists_en.md)、[Text Image Orientation Classification](PULC_text_image_orientation_en.md)、[Text-line Orientation Classification](PULC_textline_orientation_en.md)、[Language Classification](PULC_language_classification_en.md) for more information about different scenarios.
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@ -2,7 +2,7 @@
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------
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本文主要介绍PaddleClas whl包对 PULC 系列模型的快速使用。
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本文主要介绍通过 PaddleClas whl 包，使用 PULC 系列模型进行预测。
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## 目录
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@ -49,9 +49,7 @@ pip3 install paddleclas
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## 2. 快速体验
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PaddleClas 提供了一系列测试图片，里边包含人、车、OCR等方向的多个场景大的demo数据。点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载并解压，然后在终端中切换到相应目录。
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PaddleClas 提供了一系列测试图片，里边包含人、车、OCR等方向的多个场景的demo数据。点击[这里](https://paddleclas.bj.bcebos.com/data/PULC/pulc_demo_imgs.zip)下载并解压，然后在终端中切换到相应目录。
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<a name="2.1"></a>
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@ -125,7 +123,3 @@ PULC 系列模型的名称和简介如下：
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通过本节内容，相信您已经熟练掌握 PaddleClas whl 包的 PULC 模型使用方法并获得了初步效果。
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PULC 方法产出的系列模型在人、车、OCR等方向的多个场景中均验证有效，用超轻量模型就可实现与 SwinTransformer 模型接近的精度，预测速度提高 40+ 倍。并且打通数据、模型训练、压缩和推理部署全流程，具体地，您可以参考[PULC有人/无人分类模型](PULC_person_exists.md)、[PULC人体属性识别模型](PULC_person_attribute.md)、[PULC佩戴安全帽分类模型](PULC_safety_helmet.md)、[PULC交通标志分类模型](PULC_traffic_sign.md)、[PULC车辆属性识别模型](PULC_vehicle_attribute.md)、[PULC有车/无车分类模型](PULC_car_exists.md)、[PULC含文字图像方向分类模型](PULC_text_image_orientation.md)、[PULC文本行方向分类模型](PULC_textline_orientation.md)、[PULC语种分类模型](PULC_language_classification.md)。
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