Paddleclas supports Python WHL package for prediction. At present, WHL package only supports image classification, but does not support subject detection, feature extraction and vector retrieval.
PaddleClas supports Python wheel package for prediction. At present, PaddleClas wheel supports image classification including ImagetNet1k models and PULC models, but does not support mainbody detection, feature extraction and vector retrieval.
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-[1. Installation](#1)
-[2. Quick Start](#2)
-[3. Definition of Parameters](#3)
-[4. Usage](#4)
-[4. More usage](#4)
-[4.1 View help information](#4.1)
-[4.2 Prediction using inference model provide by PaddleClas](#4.2)
-[4.3 Prediction using local model files](#4.3)
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-[4.8 Specify the mapping between class id and label name](#4.8)
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## 1. Installation
* installing from pypi
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```
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## 2. Quick Start
* Using the `ResNet50` model provided by PaddleClas, the following image(`'docs/images/inference_deployment/whl_demo.jpg'`) as an example.
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### 2.1 ImageNet1k models
Using the `ResNet50` model provided by PaddleClas, the following image(`'docs/images/inference_deployment/whl_demo.jpg'`) as an example.
PULC integrates various state-of-the-art algorithms such as backbone network, data augmentation and distillation, etc., and finally can automatically obtain a lightweight and high-precision image classification model.
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.
Prection using the PULC "Human Exists Classification" model provided by PaddleClas:
`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.
**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 [Supported Model List](#PULC_Models) for the supported model list.
**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 [Supported Model List](#PULC_Models) for the supported model list.
| language_classification | Language Classification |
Please refer to [Human Exists Classification](../PULC/PULC_person_exists_en.md)、[Pedestrian Attribute Classification](../PULC/PULC_person_attribute_en.md)、[Classification of Wheather Wearing Safety Helmet](../PULC/PULC_safety_helmet_en.md)、[Traffic Sign Classification](../PULC/PULC_traffic_sign_en.md)、[Vehicle Attribute Classification](../PULC/PULC_vehicle_attribute_en.md)、[Car Exists Classification](../PULC/PULC_car_exists_en.md)、[Text Image Orientation Classification](../PULC/PULC_text_image_orientation_en.md)、[Text-line Orientation Classification](../PULC/PULC_textline_orientation_en.md)、[Language Classification](../PULC/PULC_language_classification_en.md) for more information about different scenarios.
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## 3. Definition of Parameters
The following parameters can be specified in Command Line or used as parameters of the constructor when instantiating the PaddleClas object in Python.
* model_name(str): If using inference model based on ImageNet1k provided by Paddle, please specify the model's name by the parameter.
* inference_model_dir(str): Local model files directory, which is valid when `model_name` is not specified. The directory should contain `inference.pdmodel` and `inference.pdiparams`.
* infer_imgs(str): The path of image to be predicted, or the directory containing the image files, or the URL of the image from Internet.
* use_gpu(bool): Whether to use GPU or not, default by `True`.
* gpu_mem(int): GPU memory usages，default by `8000`。
* use_tensorrt(bool): Whether to open TensorRT or not. Using it can greatly promote predict preformance, default by `False`.
* enable_mkldnn(bool): Whether enable MKLDNN or not, default `False`.
* cpu_num_threads(int): Assign number of cpu threads, valid when `--use_gpu` is `False` and `--enable_mkldnn` is `True`, default by `10`.
* batch_size(int): Batch size, default by `1`.
* resize_short(int): Resize the minima between height and width into `resize_short`, default by `256`.
* crop_size(int): Center crop image to `crop_size`, default by `224`.
* topk(int): Print (return) the `topk` prediction results, default by `5`.
*class_id_map_file(str): The mapping file between class ID and label, default by `ImageNet1K` dataset's mapping.
*pre_label_image(bool): whether prelabel or not, default=False.
* save_dir(str): The directory to save the prediction results that can be used as pre-label, default by `None`, that is, not to save.
* use_gpu(bool): Whether to use GPU or not.
* gpu_mem(int): GPU memory usages.
* use_tensorrt(bool): Whether to open TensorRT or not. Using it can greatly promote predict preformance.
* enable_mkldnn(bool): Whether enable MKLDNN or not.
* cpu_num_threads(int): Assign number of cpu threads, valid when `--use_gpu` is `False` and `--enable_mkldnn` is `True`.
* batch_size(int): Batch size.
* resize_short(int): Resize the minima between height and width into `resize_short`.
* crop_size(int): Center crop image to `crop_size`.
* topk(int): Print (return) the `topk` prediction results when Topk postprocess is used.
*threshold(float): The threshold of ThreshOutput when postprocess is used.
*class_id_map_file(str): The mapping file between class ID and label.
* save_dir(str): The directory to save the prediction results that can be used as pre-label.
**Note**: If you want to use `Transformer series models`, such as `DeiT_***_384`, `ViT_***_384`, etc., please pay attention to the input size of model, and need to set `resize_short=384`, `resize=384`. The following is a demo.
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2. Bash command line programming.
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### 4.1 View help information
* CLI
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```
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### 4.2 Prediction using inference model provide by PaddleClas
You can use the inference model provided by PaddleClas to predict, and only need to specify `model_name`. In this case, PaddleClas will automatically download files of specified model and save them in the directory `~/.paddleclas/`.
You can use the local model files trained by yourself to predict, and only need to specify `inference_model_dir`. Note that the directory must contain `inference.pdmodel` and `inference.pdiparams`.
You can predict the Internet image, only need to specify URL of Internet image by `infer_imgs`. In this case, the image file will be downloaded and saved in the directory `~/.paddleclas/images/`.
In Python code, you can predict the `NumPy.array` format image, only need to use the `infer_imgs` to transfer variable of image data. Note that the models in PaddleClas only support to predict 3 channels image data, and channels order is `RGB`.
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```
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### 4.7 Save the prediction result(s)
You can save the prediction result(s) as pre-label, only need to use `pre_label_out_dir` to specify the directory to save.
### 4.8 Specify the mapping between class id and label name
You can specify the mapping between class id and label name, only need to use `class_id_map_file` to specify the mapping file. PaddleClas uses ImageNet1K's mapping by default.
