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Pedestrian Attribute Recognition Pipeline Tutorial

1. Introduction to Pedestrian Attribute Recognition Pipeline

Pedestrian attribute recognition is a key function in computer vision systems, used to locate and label specific characteristics of pedestrians in images or videos, such as gender, age, clothing color, and style. This task not only requires accurately detecting pedestrians but also identifying detailed attribute information for each pedestrian. The pedestrian attribute recognition pipeline is an end-to-end serial system for locating and recognizing pedestrian attributes, widely used in smart cities, security surveillance, and other fields, significantly enhancing the system's intelligence level and management efficiency.

The pedestrian attribute recognition pipeline includes a pedestrian detection module and a pedestrian attribute recognition module, with several models in each module. Which models to use specifically can be selected based on the benchmark data below. If you prioritize model accuracy, choose models with higher accuracy; if you prioritize inference speed, choose models with faster inference; if you prioritize model storage size, choose models with smaller storage.

👉Model List Details

Pedestrian Detection Module:

Model mAP(0.5:0.95) mAP(0.5) GPU Inference Time (ms) CPU Inference Time (ms) Model Size (M) Description
PP-YOLOE-L_human 48.0 81.9 32.8 777.7 196.02 Pedestrian detection model based on PP-YOLOE
PP-YOLOE-S_human 42.5 77.9 15.0 179.3 28.79

Note: The above accuracy metrics are mAP(0.5:0.95) on the CrowdHuman dataset. All model GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.

Pedestrian Attribute Recognition Module:

Model mA (%) GPU Inference Time (ms) CPU Inference Time (ms) Model Size (M) Description
PP-LCNet_x1_0_pedestrian_attribute 92.2 3.84845 9.23735 6.7 M PP-LCNet_x1_0_pedestrian_attribute is a lightweight pedestrian attribute recognition model based on PP-LCNet, covering 26 categories.

Note: The above accuracy metrics are mA on PaddleX's internally built dataset. GPU inference times are based on an NVIDIA Tesla T4 machine with FP32 precision. CPU inference speeds are based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.

2. Quick Start

The pre-trained model pipelines provided by PaddleX can quickly demonstrate their effectiveness. You can experience the pedestrian attribute recognition pipeline online or locally using command line or Python.

2.1 Online Experience

Not supported yet.

2.2 Local Experience

Before using the pedestrian attribute recognition pipeline locally, ensure you have completed the installation of the PaddleX wheel package following the PaddleX Local Installation Tutorial.

2.2.1 Command Line Experience

You can quickly experience the pedestrian attribute recognition pipeline with a single command. Use the test file and replace --input with the local path for prediction.

paddlex --pipeline pedestrian_attribute_recognition --input pedestrian_attribute_002.jpg --device gpu:0

Parameter Description:

--pipeline: The name of the pipeline, here it is the pedestrian attribute recognition pipeline.
--input: The local path or URL of the input image to be processed.
--device: The GPU index to use (e.g., gpu:0 means using the first GPU, gpu:1,2 means using the second and third GPUs), or you can choose to use CPU (--device cpu).

When executing the above Python script, the default pedestrian attribute recognition pipeline configuration file is loaded. If you need a custom configuration file, you can run the following command to obtain it:

👉Click to Expand
paddlex --get_pipeline_config pedestrian_attribute_recognition

After execution, the pedestrian attribute recognition pipeline configuration file will be saved in the current path. If you wish to specify a custom save location, you can run the following command (assuming the custom save location is ./my_path):

paddlex --get_pipeline_config pedestrian_attribute_recognition --save_path ./my_path

After obtaining the pipeline configuration file, you can replace --pipeline with the saved path of the configuration file to make it effective. For example, if the configuration file is saved at ./pedestrian_attribute_recognition.yaml, simply execute:

paddlex --pipeline ./pedestrian_attribute_recognition.yaml --input pedestrian_attribute_002.jpg --device gpu:0

Among them, parameters such as --model and --device do not need to be specified, and the parameters in the configuration file will be used. If parameters are still specified, the specified parameters will take precedence.

2.2.2 Python Script Integration

A few lines of code are sufficient for quick inference of the pipeline. Taking the pedestrian attribute recognition pipeline as an example:

from paddlex import create_pipeline

pipeline = create_pipeline(pipeline="pedestrian_attribute_recognition")

output = pipeline.predict("pedestrian_attribute_002.jpg")
for res in output:
    res.print()  ## Print the structured output of the prediction
    res.save_to_img("./output/")  ## Save the visualized image of the result
    res.save_to_json("./output/")  ## Save the structured output of the prediction

The results obtained are the same as those from the command line approach.

In the above Python script, the following steps are executed:

(1) Instantiate the create_pipeline to create a pipeline object: Specific parameter descriptions are as follows:

Parameter Description Parameter Type Default Value
pipeline The name of the pipeline or the path to the pipeline configuration file. If it is the name of the pipeline, it must be a pipeline supported by PaddleX. str None
device The device for pipeline model inference. Supports: "gpu", "cpu". str "gpu"
use_hpip Whether to enable high-performance inference, only available when the pipeline supports high-performance inference. bool False
(2) Call the predict method of the pedestrian attribute recognition pipeline object for inference prediction: The predict method parameter is x, which is used to input data to be predicted, supporting multiple input methods. Specific examples are as follows:

Parameter Type Description
Python Var Supports directly passing in Python variables, such as image data represented by numpy.ndarray.
str Supports passing in the file path of the data to be predicted, such as the local path of an image file: /root/data/img.jpg.
str Supports passing in the URL of the data file to be predicted, such as the network URL of an image file: Example.
str Supports passing in a local directory, which should contain the data files to be predicted, such as the local path: /root/data/.
dict Supports passing in a dictionary type, where the key needs to correspond to the specific task, such as "img" for the pedestrian attribute recognition task, and the value of the dictionary supports the above data types, for example: {"img": "/root/data1"}.
list Supports passing in a list, where the elements of the list need to be the above data types, such as [numpy.ndarray, numpy.ndarray], ["/root/data/img1.jpg", "/root/data/img2.jpg"], ["/root/data1", "/root/data2"], [{"img": "/root/data1"}, {"img": "/root/data2/img.jpg"}].
(3) Obtain the prediction results by calling the predict method: The predict method is a generator, so prediction results need to be obtained through iteration. The predict method predicts data in batches, so the prediction results are in the form of a list.

(4) Process the prediction results: The prediction result for each sample is of type dict and supports printing or saving as a file. The supported save types are related to the specific pipeline, such as:

Method Description Method Parameters
print Print the results to the terminal - format_json: bool, whether to format the output content with json indentation, default is True;
- indent: int, json formatting setting, only effective when format_json is True, default is 4;
- ensure_ascii: bool, json formatting setting, only effective when format_json is True, default is False;
save_to_json Save the results as a json-formatted file - save_path: str, the path to save the file, when it is a directory, the saved file name is consistent with the input file name;
- indent: int, json formatting setting, default is 4;
- ensure_ascii: bool, json formatting setting, default is False;
save_to_img Save the results as an image file
If you have obtained the configuration file, you can customize various configurations for the pedestrian attribute recognition pipeline by simply modifying the pipeline parameter in the create_pipeline method to the path of your pipeline configuration file.

For example, if your configuration file is saved as ./my_path/pedestrian_attribute_recognition*.yaml, you only need to execute:

from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="./my_path/pedestrian_attribute_recognition.yaml")
output = pipeline.predict("pedestrian_attribute_002.jpg")
for res in output:
    res.print()  # Print the structured output of the prediction
    res.save_to_img("./output/")  # Save the visualized result image
    res.save_to_json("./output/")  # Save the structured output of the prediction

3. Development Integration/Deployment

If the face recognition pipeline meets your requirements for inference speed and accuracy, you can proceed directly with development integration/deployment.

If you need to directly apply the face recognition pipeline in your Python project, you can refer to the example code in 2.2.2 Python Script Integration.

Additionally, PaddleX provides three other deployment methods, detailed as follows:

🚀 High-Performance Inference: In actual production environments, many applications have stringent standards for the performance metrics of deployment strategies (especially response speed) to ensure efficient system operation and smooth user experience. To this end, PaddleX provides high-performance inference plugins aimed at deeply optimizing model inference and pre/post-processing to significantly speed up the end-to-end process. For detailed high-performance inference procedures, please refer to the PaddleX High-Performance Inference Guide.

☁️ Service-Oriented Deployment: Service-oriented deployment is a common deployment form in actual production environments. By encapsulating inference functionality as services, clients can access these services through network requests to obtain inference results. PaddleX supports users in achieving service-oriented deployment of pipelines at low cost. For detailed service-oriented deployment procedures, please refer to the PaddleX Service-Oriented Deployment Guide.

Below are the API reference and multi-language service invocation examples:

API Reference

For all operations provided by the service:

  • Both the response body and the request body for POST requests are JSON data (JSON objects).
  • When the request is processed successfully, the response status code is 200, and the response body properties are as follows:
Name Type Description
errorCode integer Error code. Fixed as 0.
errorMsg string Error description. Fixed as "Success".

The response body may also have a result property of type object, which stores the operation result information.

  • When the request is not processed successfully, the response body properties are as follows:
Name Type Description
errorCode integer Error code. Same as the response status code.
errorMsg string Error description.

Operations provided by the service are as follows:

  • infer

Get pedestrian attribute recognition results.

POST /pedestrian-attribute-recognition

  • The request body properties are as follows:
Name Type Description Required
image string The URL of an image file accessible by the service or the Base64 encoded result of the image file content. Yes
  • When the request is processed successfully, the result of the response body has the following properties:
Name Type Description
pedestrians array Information about the pedestrian's location and attributes.
image string The pedestrian attribute recognition result image. The image is in JPEG format and encoded using Base64.

Each element in pedestrians is an object with the following properties:

Name Type Description
bbox array The location of the pedestrian. The elements in the array are the x-coordinate of the top-left corner, the y-coordinate of the top-left corner, the x-coordinate of the bottom-right corner, and the y-coordinate of the bottom-right corner of the bounding box, respectively.
attributes array The pedestrian attributes.
score number The detection score.

Each element in attributes is an object with the following properties:

Name Type Description
label string The label of the attribute.
score number The classification score.

Multi-Language Service Invocation Examples

Python

import base64
import requests

API_URL = "http://localhost:8080/pedestrian-attribute-recognition" image_path = "./demo.jpg" output_image_path = "./out.jpg"

with open(image_path, "rb") as file:

image_bytes = file.read()
image_data = base64.b64encode(image_bytes).decode("ascii")

payload = {"image": image_data}

response = requests.post(API_URL, json=payload)

assert response.status_code == 200 result = response.json()["result"] with open(output_image_path, "wb") as file:

file.write(base64.b64decode(result["image"]))

print(f"Output image saved at {output_image_path}") print("\nDetected pedestrians:") print(result["pedestrians"])


📱 Edge Deployment: Edge deployment is a method where computing and data processing functions are placed on the user's device itself, allowing the device to process data directly without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. For detailed edge deployment procedures, please refer to the PaddleX Edge Deployment Guide. You can choose an appropriate method to deploy your model pipeline based on your needs, and proceed with subsequent AI application integration.

4. Custom Development

If the default model weights provided by the Face Recognition Pipeline do not meet your expectations in terms of accuracy or speed for your specific scenario, you can try to further fine-tune the existing models using your own domain-specific or application-specific data to enhance the recognition performance of the pipeline in your scenario.

4.1 Model Fine-tuning

Since the Face Recognition Pipeline consists of two modules (face detection and face recognition), the suboptimal performance of the pipeline may stem from either module.

You can analyze images with poor recognition results. If you find that many faces are not detected during the analysis, it may indicate deficiencies in the face detection model. In this case, you need to refer to the Custom Development section in the Face Detection Module Development Tutorial and use your private dataset to fine-tune the face detection model. If matching errors occur in detected faces, it suggests that the face feature model needs further improvement. You should refer to the Custom Development section in the Face Feature Module Development Tutorial to fine-tune the face feature model.

4.2 Model Application

After completing fine-tuning training with your private dataset, you will obtain local model weight files.

To use the fine-tuned model weights, you only need to modify the pipeline configuration file by replacing the local paths of the fine-tuned model weights with the corresponding paths in the pipeline configuration file:


......
Pipeline:
  device: "gpu:0"
  det_model: "BlazeFace"        # Can be modified to the local path of the fine-tuned face detection model
  rec_model: "MobileFaceNet"    # Can be modified to the local path of the fine-tuned face recognition model
  det_batch_size: 1
  rec_batch_size: 1
  device: gpu
......

Subsequently, refer to the command-line method or Python script method in 2.2 Local Experience to load the modified pipeline configuration file. Note: Currently, setting separate batch_size for face detection and face recognition models is not supported.

5. Multi-hardware Support

PaddleX supports various mainstream hardware devices such as NVIDIA GPUs, Kunlun XPU, Ascend NPU, and Cambricon MLU. Simply modifying the --device parameter allows seamless switching between different hardware.

For example, when running the face recognition pipeline using Python and changing the running device from an NVIDIA GPU to an Ascend NPU, you only need to modify the device in the script to npu:

from paddlex import create_pipeline

pipeline = create_pipeline(
    pipeline="face_recognition",
    device="npu:0" # gpu:0 --> npu:0
)

If you want to use the face recognition pipeline on more types of hardware, please refer to the PaddleX Multi-device Usage Guide.