General Image Multi-Label Classification Pipeline Tutorial¶

1. Introduction to the General Image Multi-Label Classification Pipeline¶

Image multi-label classification is a technique that assigns multiple relevant categories to a single image simultaneously, widely used in image annotation, content recommendation, and social media analysis. It can identify multiple objects or features present in an image, for example, an image containing both "dog" and "outdoor" labels. By leveraging deep learning models, image multi-label classification automatically extracts image features and performs accurate classification, providing users with more comprehensive information. This technology is of great significance in applications such as intelligent search engines and automatic content generation.

The General Image Multi-Label Classification Pipeline includes a module for image multi-label classification. If you prioritize model accuracy, choose a model with higher accuracy. If you prioritize inference speed, choose a model with faster inference. If you prioritize model storage size, choose a model with a smaller storage size.

Model Name	Model Download Link	mAP (%)	Model Storage Size (M)
CLIP_vit_base_patch16_448_ML	Inference Model/Trained Model	89.15	-
PP-HGNetV2-B0_ML	Inference Model/Trained Model	80.98	39.6
PP-HGNetV2-B4_ML	Inference Model/Trained Model	87.96	88.5
PP-HGNetV2-B6_ML	Inference Model/Trained Model	91.25	286.5
PP-LCNet_x1_0_ML	Inference Model/Trained Model	77.96	29.4
ResNet50_ML	Inference Model/Trained Model	83.50	108.9

Note: The above accuracy metrics are mAP for the multi-label classification task on COCO2017. The GPU inference time for all models is based on an NVIDIA Tesla T4 machine with FP32 precision. The CPU inference speed is based on an Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz with 8 threads and FP32 precision.

2. Quick Start¶

PaddleX supports experiencing the effects of the General Image Multi-Label Classification Pipeline locally using command line or Python.

Before using the General Image Multi-Label Classification Pipeline locally, please ensure you have installed the PaddleX wheel package following the PaddleX Local Installation Tutorial.

2.1 Experience via Command Line¶

Experience the effects of the image multi-label classification pipeline with a single command:

paddlex --pipeline multi_label_image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0

Parameter Explanation:

--pipeline: The name of the pipeline, here it is the image multi-label classification pipeline.
--input: The local path or URL of the input image to be processed.
--device: The GPU index to use (e.g., gpu:0 for the first GPU, gpu:1,2 for the second and third GPUs). You can also choose to use CPU (--device cpu).

When executing the above command, the default configuration file for the image multi-label classification pipeline is loaded. If you need to customize the configuration file, you can execute the following command to obtain it:

👉Click to Expand

paddlex --get_pipeline_config multi_label_image_classification

After execution, the configuration file for the image multi-label classification pipeline will be saved in the current path. If you wish to customize the save location, you can execute the following command (assuming the custom save location is ./my_path):

paddlex --get_pipeline_config multi_label_image_classification --save_path ./my_path

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

paddlex --pipeline ./multi_label_image_classification.yaml --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0

Where --model, --device, and other parameters are not specified, the parameters in the configuration file will be used. If parameters are specified, the specified parameters will take precedence.

After running, the result obtained is:

{'input_path': 'general_image_classification_001.jpg', 'class_ids': [21, 0, 30, 24], 'scores': [0.99257, 0.70596, 0.63001, 0.57852], 'label_names': ['bear', 'person', 'skis', 'backpack']}

The visualized image not saved by default. You can customize the save path through --save_path, and then all results will be saved in the specified path.

2.2 Integration via Python Script¶

A few lines of code can complete the rapid inference of the pipeline. Taking the general image multi-label classification pipeline as an example:

from paddlex import create_pipeline

pipeline = create_pipeline(pipeline="multi_label_image_classification")

output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg")
for res in output:
    res.print()  # Print the structured output of the prediction
    res.save_to_img("./output/")  # Save the result visualization image
    res.save_to_json("./output/")  # Save the structured output of the prediction

The result obtained is the same as that of the command line method.

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	Type	Default Value
`pipeline`	The name of the pipeline or the path of 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, which is only available when the pipeline supports it.	`bool`	`False`

(2) Call the predict method of the multi-label classification pipeline object for inference prediction: The predict method parameter is x, which is used to input data to be predicted, supporting multiple input methods, as shown in the following examples:

Parameter Type	Description
Python Var	Supports directly passing in Python variables, such as numpy.ndarray representing image data.
str	Supports passing in the file path of the data file 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 of the dictionary needs to correspond to the specific task, such as "img" for image classification tasks, and the value of the dictionary supports the above data types, for example: `{"img": "/root/data1"}`.
list	Supports passing in a list, where the list elements 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 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 dict type and supports printing or saving to files, with the supported file types depending on the specific pipeline. For example:

Method	Description	Method Parameters
print	Prints 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 valid when format_json is True, default is 4; `- ensure_ascii`: bool, json formatting setting, only valid when format_json is True, default is False;
save_to_json	Saves results as a json file	`- save_path`: str, the path to save the file, when it's a directory, the saved file name is consistent with the input file type; `- indent`: int, json formatting setting, default is 4; `- ensure_ascii`: bool, json formatting setting, default is False;
save_to_img	Saves results as an image file	`- save_path`: str, the path to save the file, when it's a directory, the saved file name is consistent with the input file type;

If you have a configuration file, you can customize the configurations of the image anomaly detection pipeline by simply modifying the pipeline parameter in the create_pipeline method to the path of the pipeline configuration file.

For example, if your configuration file is saved at ./my_path/multi_label_image_classification.yaml, you only need to execute:

from paddlex import create_pipeline
pipeline = create_pipeline(pipeline="./my_path/multi_label_image_classification.yaml")
output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg")
for res in output:
    res.print()  # Print the structured output of prediction
    res.save_to_img("./output/")  # Save the visualization image of the result
    res.save_to_json("./output/")  # Save the structured output of prediction

3. Development Integration/Deployment¶

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

If you need to directly apply the pipeline in your Python project, refer to the example code in 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 strict 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 that aim to deeply optimize model inference and pre/post-processing to significantly speed up the end-to-end process. For detailed high-performance inference procedures, 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 functions as services, clients can access these services through network requests to obtain inference results. PaddleX supports users in achieving low-cost service-oriented deployment of pipelines. For detailed service-oriented deployment procedures, refer to the PaddleX Service-Oriented Deployment Guide.

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

API Reference

For main operations provided by the service:

The HTTP request method is POST.
The request body and the response body are both 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 to `0`.
`errorMsg`	`string`	Error message. Fixed to `"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 message.

Main operations provided by the service are as follows:

infer

Classify images.

POST /multilabel-image-classification

The request body properties are as follows:

Name	Type	Description	Required
`image`	`string`	The URL of the image file accessible by the service or the Base64 encoded result of the image file content.	Yes
`inferenceParams`	`object`	Inference parameters.	No

The properties of inferenceParams are as follows:

Name	Type	Description	Required
`topK`	`integer`	Only the top `topK` categories with the highest scores will be retained in the result.	No

When the request is processed successfully, the result of the response body has the following properties:

Name	Type	Description
`categories`	`array`	Image category information.
`image`	`string`	Image classification result image. The image is in JPEG format and encoded in Base64.

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

Name	Type	Description
`id`	`integer`	Category ID.
`name`	`string`	Category name.
`score`	`number`	Category score.

An example of result is as follows:

{
"categories": [
{
"id": 5,
"name": "Rabbit",
"score": 0.93
}
],
"image": "xxxxxx"
}

Multi-Language Service Invocation Examples

Python

import base64
import requests

API_URL = "http://localhost:8080/multilabel-image-classification"
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("\nCategories:")
print(result["categories"])

C++

#include <iostream>
#include "cpp-httplib/httplib.h" // https://github.com/Huiyicc/cpp-httplib
#include "nlohmann/json.hpp" // https://github.com/nlohmann/json
#include "base64.hpp" // https://github.com/tobiaslocker/base64

int main() {
    httplib::Client client("localhost:8080");
    const std::string imagePath = "./demo.jpg";
    const std::string outputImagePath = "./out.jpg";

    httplib::Headers headers = {
        {"Content-Type", "application/json"}
    };

    std::ifstream file(imagePath, std::ios::binary | std::ios::ate);
    std::streamsize size = file.tellg();
    file.seekg(0, std::ios::beg);

    std::vector<char> buffer(size);
    if (!file.read(buffer.data(), size)) {
        std::cerr << "Error reading file." << std::endl;
        return 1;
    }
    std::string bufferStr(reinterpret_cast<const char*>(buffer.data()), buffer.size());
    std::string encodedImage = base64::to_base64(bufferStr);

    nlohmann::json jsonObj;
    jsonObj["image"] = encodedImage;
    std::string body = jsonObj.dump();

    auto response = client.Post("/multilabel-image-classification", headers, body, "application/json");
    if (response && response->status == 200) {
        nlohmann::json jsonResponse = nlohmann::json::parse(response->body);
        auto result = jsonResponse["result"];

        encodedImage = result["image"];
        std::string decodedString = base64::from_base64(encodedImage);
        std::vector<unsigned char> decodedImage(decodedString.begin(), decodedString.end());
        std::ofstream outputImage(outPutImagePath, std::ios::binary | std::ios::out);
        if (outputImage.is_open()) {
            outputImage.write(reinterpret_cast<char*>(decodedImage.data()), decodedImage.size());
            outputImage.close();
            std::cout << "Output image saved at " << outPutImagePath << std::endl;
        } else {
            std::cerr << "Unable to open file for writing: " << outPutImagePath << std::endl;
        }

        auto categories = result["categories"];
        std::cout << "\nCategories:" << std::endl;
        for (const auto& category : categories) {
            std::cout << category << std::endl;
        }
    } else {
        std::cout << "Failed to send HTTP request." << std::endl;
        return 1;
    }

    return 0;
}

Java

import okhttp3.*;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.node.ObjectNode;

import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;
import java.util.Base64;

public class Main {
    public static void main(String[] args) throws IOException {
        String API_URL = "http://localhost:8080/multilabel-image-classification";
        String imagePath = "./demo.jpg";
        String outputImagePath = "./out.jpg";

        File file = new File(imagePath);
        byte[] fileContent = java.nio.file.Files.readAllBytes(file.toPath());
        String imageData = Base64.getEncoder().encodeToString(fileContent);

        ObjectMapper objectMapper = new ObjectMapper();
        ObjectNode params = objectMapper.createObjectNode();
        params.put("image", imageData);

        OkHttpClient client = new OkHttpClient();
        MediaType JSON = MediaType.Companion.get("application/json; charset=utf-8");
        RequestBody body = RequestBody.Companion.create(params.toString(), JSON);
        Request request = new Request.Builder()
                .url(API_URL)
                .post(body)
                .build();

        try (Response response = client.newCall(request).execute()) {
            if (response.isSuccessful()) {
                String responseBody = response.body().string();
                JsonNode resultNode = objectMapper.readTree(responseBody);
                JsonNode result = resultNode.get("result");
                String base64Image = result.get("image").asText();
                JsonNode categories = result.get("categories");

                byte[] imageBytes = Base64.getDecoder().decode(base64Image);
                try (FileOutputStream fos = new FileOutputStream(outputImagePath)) {
                    fos.write(imageBytes);
                }
                System.out.println("Output image saved at " + outputImagePath);
                System.out.println("\nCategories: " + categories.toString());
            } else {
                System.err.println("Request failed with code: " + response.code());
            }
        }
    }
}

Go

package main

import (
    "bytes"
    "encoding/base64"
    "encoding/json"
    "fmt"
    "io/ioutil"
    "net/http"
)

func main() {
    API_URL := "http://localhost:8080/multilabel-image-classification"
    imagePath := "./demo.jpg"
    outputImagePath := "./out.jpg"

    imageBytes, err := ioutil.ReadFile(imagePath)
    if err != nil {
        fmt.Println("Error reading image file:", err)
        return
    }
    imageData := base64.StdEncoding.EncodeToString(imageBytes)

    payload := map[string]string{"image": imageData}
    payloadBytes, err := json.Marshal(payload)
    if err != nil {
        fmt.Println("Error marshaling payload:", err)
        return
    }

    client := &http.Client{}
    req, err := http.NewRequest("POST", API_URL, bytes.NewBuffer(payloadBytes))
    if err != nil {
        fmt.Println("Error creating request:", err)
        return
    }

    res, err := client.Do(req)
    if err != nil {
        fmt.Println("Error sending request:", err)
        return
    }
    defer res.Body.Close()

    body, err := ioutil.ReadAll(res.Body)
    if err != nil {
        fmt.Println("Error reading response body:", err)
        return
    }
    type Response struct {
        Result struct {
            Image      string   `json:"image"`
            Categories []map[string]interface{} `json:"categories"`
        } `json:"result"`
    }
    var respData Response
    err = json.Unmarshal([]byte(string(body)), &respData)
    if err != nil {
        fmt.Println("Error unmarshaling response body:", err)
        return
    }

    outputImageData, err := base64.StdEncoding.DecodeString(respData.Result.Image)
    if err != nil {
        fmt.Println("Error decoding base64 image data:", err)
        return
    }
    err = ioutil.WriteFile(outputImagePath, outputImageData, 0644)
    if err != nil {
        fmt.Println("Error writing image to file:", err)
        return
    }
    fmt.Printf("Image saved at %s.jpg\n", outputImagePath)
    fmt.Println("\nCategories:")
    for _, category := range respData.Result.Categories {
        fmt.Println(category)
    }
}

C#

using System;
using System.IO;
using System.Net.Http;
using System.Net.Http.Headers;
using System.Text;
using System.Threading.Tasks;
using Newtonsoft.Json.Linq;

class Program
{
    static readonly string API_URL = "http://localhost:8080/multilabel-image-classification";
    static readonly string imagePath = "./demo.jpg";
    static readonly string outputImagePath = "./out.jpg";

    static async Task Main(string[] args)
    {
        var httpClient = new HttpClient();

        byte[] imageBytes = File.ReadAllBytes(imagePath);
        string image_data = Convert.ToBase64String(imageBytes);

        var payload = new JObject{ { "image", image_data } };
        var content = new StringContent(payload.ToString(), Encoding.UTF8, "application/json");

        HttpResponseMessage response = await httpClient.PostAsync(API_URL, content);
        response.EnsureSuccessStatusCode();

        string responseBody = await response.Content.ReadAsStringAsync();
        JObject jsonResponse = JObject.Parse(responseBody);

        string base64Image = jsonResponse["result"]["image"].ToString();
        byte[] outputImageBytes = Convert.FromBase64String(base64Image);

        File.WriteAllBytes(outputImagePath, outputImageBytes);
        Console.WriteLine($"Output image saved at {outputImagePath}");
        Console.WriteLine("\nCategories:");
        Console.WriteLine(jsonResponse["result"]["categories"].ToString());
    }
}

Node.js

const axios = require('axios');
const fs = require('fs');

const API_URL = 'http://localhost:8080/multilabel-image-classification'
const imagePath = './demo.jpg'
const outputImagePath = "./out.jpg";

let config = {
   method: 'POST',
   maxBodyLength: Infinity,
   url: API_URL,
   data: JSON.stringify({
    'image': encodeImageToBase64(imagePath)
  })
};

function encodeImageToBase64(filePath) {
  const bitmap = fs.readFileSync(filePath);
  return Buffer.from(bitmap).toString('base64');
}

axios.request(config)
.then((response) => {
    const result = response.data["result"];
    const imageBuffer = Buffer.from(result["image"], 'base64');
    fs.writeFile(outputImagePath, imageBuffer, (err) => {
      if (err) throw err;
      console.log(`Output image saved at ${outputImagePath}`);
    });
    console.log("\nCategories:");
    console.log(result["categories"]);
})
.catch((error) => {
  console.log(error);
});

PHP

<?php

$API_URL = "http://localhost:8080/multilabel-image-classification";
$image_path = "./demo.jpg";
$output_image_path = "./out.jpg";

$image_data = base64_encode(file_get_contents($image_path));
$payload = array("image" => $image_data);

$ch = curl_init($API_URL);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload));
curl_setopt($ch, CURLOPT_HTTPHEADER, array('Content-Type: application/json'));
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$response = curl_exec($ch);
curl_close($ch);

$result = json_decode($response, true)["result"];
file_put_contents($output_image_path, base64_decode($result["image"]));
echo "Output image saved at " . $output_image_path . "\n";
echo "\nCategories:\n";
print_r($result["categories"]);
?>

📱 Edge Deployment: Edge deployment is a way to place computing and data processing functions on user devices themselves, allowing devices to process data directly without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. For detailed edge deployment procedures, refer to the PaddleX Edge Deployment Guide. You can choose the appropriate deployment method for 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 general image multi-label classification pipeline do not meet your requirements in terms of accuracy or speed in your specific scenario, you can try to further fine-tune the existing model using your own domain-specific or application-specific data to improve the recognition performance of the general image multi-label classification pipeline in your scenario.

4.1 Model Fine-tuning¶

Since the general image multi-label classification pipeline includes an image multi-label classification module, if the performance of the pipeline does not meet expectations, you need to refer to the Customization section in the Image Multi-Label Classification Module Development Tutorial to fine-tune the image multi-label classification model using your private dataset.

4.2 Model Application¶

After you have completed fine-tuning training using your private dataset, you will obtain local model weights files.

If you need to use the fine-tuned model weights, simply modify the pipeline configuration file by replacing the local path of the fine-tuned model weights to the corresponding location in the pipeline configuration file:

......
Pipeline:
  model: PP-LCNet_x1_0_ML   # Can be modified to the local path of the fine-tuned model
  batch_size: 1
  device: "gpu:0"
......

Then, refer to the command line method or Python script method in the local experience section to load the modified pipeline configuration file.

5. Multi-hardware Support¶

PaddleX supports various mainstream hardware devices such as NVIDIA GPUs, Kunlun XPU, Ascend NPU, and Cambricon MLU. Simply modify the --device parameter to seamlessly switch between different hardware.

For example, if you use an NVIDIA GPU for inference of the image multi-label classification pipeline, the Python command is:

paddlex --pipeline multi_label_image_classification --input https://paddle-model-ecology.bj.bcebos.com/padd

At this point, if you wish to switch the hardware to Ascend NPU, simply modify the --device in the Python command to npu:0:

paddlex --pipeline multi_label_image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device npu:0

If you want to use the General Image Multi-label Classification Pipeline on more diverse hardware, please refer to the PaddleX Multi-device Usage Guide.