Tutorial on Using the Rotated Object Detection Pipeline

1. Introduction to the Rotated Object Detection Pipeline

Rotated object detection is a variant of the object detection module, specifically designed for detecting rotated objects. Rotated bounding boxes are often used to detect rectangular boxes with angular information, where the width and height of the box are no longer parallel to the image coordinate axes. Compared to horizontal rectangular boxes, rotated rectangular boxes generally include less background information. Rotated object detection has important applications in remote sensing scenarios. This pipeline also provides flexible service deployment options, supporting multiple programming languages on various hardware. Moreover, this pipeline offers secondary development capabilities, allowing you to train and fine-tune models on your own dataset, with seamless integration of the trained models.

The rotated object detection pipeline includes a rotated object detection module, which contains multiple models. You can choose the model based on the benchmark data provided below.

If you prioritize model accuracy, choose a model with higher accuracy; if you care more about inference speed, choose a model with faster inference speed; if you are concerned about model storage size, choose a model with a smaller storage size.

Image Rotated Object Detection Module (Optional):

Model	Model Download Link	mAP(%)	GPU Inference Time (ms) [Normal Mode / High-Performance Mode]	CPU Inference Time (ms)	Model Storage Size (M)	Description
PP-YOLOE-R-L	Inference Model/Training Model	78.14	20.7039	157.942	211.0 M	PP-YOLOE-R is an efficient one-stage anchor-free rotated bounding box detection model. Based on PP-YOLOE, PP-YOLOE-R introduces several useful designs to improve detection accuracy with minimal additional parameters and computational cost.

Test Environment Description:

• Performance Test Environment
• Test Dataset: DOTA validation set.

• Hardware Configuration:

  • GPU: NVIDIA Tesla T4
  • CPU: Intel Xeon Gold 6271C @ 2.60GHz
  • Other Environments: Ubuntu 20.04 / cuDNN 8.6 / TensorRT 8.5.2.2

• Inference Mode Description

Mode	GPU Configuration	CPU Configuration	Acceleration Technology Combination
Normal Mode	FP32 Precision / No TRT Acceleration	FP32 Precision / 8 Threads	PaddleInference
High-Performance Mode	Optimal combination of pre-selected precision types and acceleration strategies	FP32 Precision / 8 Threads	Pre-selected optimal backend (Paddle/OpenVINO/TRT, etc.)

2. Quick Start

2.1 Local Experience

❗ Before using the rotated object detection pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the PaddleX Local Installation Guide.

2.1.1 Command Line Experience

• You can quickly experience the rotated object detection pipeline with a single command. Use the test image and replace --input with your local path for prediction.

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paddlex --pipeline rotated_object_detection \
        --input rotated_object_detection_001.png \
        --threshold 0.5 \
        --save_path ./output \
        --device gpu:0 \

The relevant parameter descriptions can be referred to in the parameter explanations of 2.2.2 Integration via Python Script.

After running, the results will be printed to the terminal, as follows:

{'res': {'input_path': 'rotated_object_detection_001.png', 'page_index': None, 'boxes': [{'cls_id': 4, 'label': 'small-vehicle', 'score': 0.7409099340438843, 'coordinate': [92.88687, 763.1569, 85.163124, 749.5868, 116.07975, 731.99414, 123.8035, 745.5643]}, {'cls_id': 4, 'label': 'small-vehicle', 'score': 0.7393015623092651, 'coordinate': [348.2332, 177.55974, 332.77704, 150.24973, 345.2183, 143.21028, 360.67444, 170.5203]}, {'cls_id': 11, 'label': 'roundabout', 'score': 0.8101699948310852, 'coordinate': [537.1732, 695.5475, 204.4297, 612.9735, 286.71338, 281.48022, 619.4569, 364.05426]}]}}

The explanation of the result parameters can be found in 2.2.2 Python Script Integration.

The visualized results are saved under save_path, and the visualized result of rotated object detection is as follows:

2.1.2 Python Script Integration

• The above command line is for quickly experiencing and viewing the effect. Generally, in a project, you often need to integrate through code. You can complete the quick inference of the pipeline with just a few lines of code. The inference code is as follows:

from paddlex import create_pipeline
pipeline = create_pipeline(pipeline_name="rotated_object_detection")
output = pipeline.predict(input="rotated_object_detection_001.png", threshold=0.5)
for res in output:
    res.print()
    res.save_to_img(save_path="./output/")
    res.save_to_json(save_path="./output/")

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

(1) The Rotated Object Detection pipeline object was instantiated via create_pipeline(), with the specific parameters described as follows:

Parameter	Description	Type	Default Value
pipeline_name	The name of the pipeline, which must be supported by PaddleX.	str	None
config	The path to the pipeline configuration file.	str	None
device	The device used for pipeline inference. It supports specifying the specific card number of the GPU, such as "gpu:0", other hardware card numbers, such as "npu:0", or CPU, such as "cpu".	str	None
use_hpip	Whether to enable high-performance inference, which is only available if the pipeline supports high-performance inference.	bool	False

(2) The predict() method of the Rotated Object Detection pipeline object was called for inference prediction. This method returns a generator. Below are the parameters of the predict() method and their descriptions:

Parameter	Description	Type	Options	Default Value
input	Data to be predicted, supporting multiple input types (required).	Python Var|str|list	Python Var: Image data represented by numpy.ndarray str: Local path of image or PDF file, e.g., /root/data/img.jpg; URL link, e.g., network URL of image or PDF file: Example; Local directory, the directory should contain images to be predicted, e.g., local path: /root/data/ (currently does not support prediction of PDF files in directories; PDF files must be specified with a specific file path) List: Elements of the list must be of the above types, e.g., [numpy.ndarray, numpy.ndarray], [\"/root/data/img1.jpg\", \"/root/data/img2.jpg\"], [\"/root/data1\", \"/root/data2\"]	None
device	The device used for pipeline inference	str|None	CPU: e.g., cpu indicates using CPU for inference; GPU: e.g., gpu:0 indicates using the 1st GPU for inference; NPU: e.g., npu:0 indicates using the 1st NPU for inference; XPU: e.g., xpu:0 indicates using the 1st XPU for inference; MLU: e.g., mlu:0 indicates using the 1st MLU for inference; DCU: e.g., dcu:0 indicates using the 1st DCU for inference; None: If set to None, the default value initialized by the pipeline will be used. During initialization, the local GPU 0 will be prioritized; if unavailable, the CPU will be used;	None
threshold	Filtering threshold for low-confidence object	None|float|dict[int, float]	None: If set to None, the default pipeline initialization parameter 0.5 will be used, i.e., 0.5 as the low-score object filtering threshold for all categories float: Any float number greater than 0 and less than 1 dict[int, float]: The key represents the category ID, and the value represents the threshold for that category, allowing different low-score filtering thresholds for different categories, e.g., {0:0.5, 1:0.35} indicates using 0.5 and 0.35 as the low-score filtering thresholds for categories 0 and 1, respectively	None

(3) Process the prediction results. The prediction result for each sample is of the dict type and supports operations such as printing, saving as an image, and saving as a json file:

Method	Description	Parameter	Parameter Type	Parameter Description	Default Value
print()	Print the result to the terminal	format_json	bool	Whether to format the output content using JSON indentation	True
		indent	int	Specify the indentation level to beautify the output JSON data, making it more readable. Effective only when format_json is True	4
		ensure_ascii	bool	Control whether to escape non-ASCII characters to Unicode. When set to True, all non-ASCII characters will be escaped; False will retain the original characters. Effective only when format_json is True	False
save_to_json()	Save the result as a JSON file	save_path	str	Path to save the file. When it is a directory, the saved file name is consistent with the input file type naming	None
		indent	int	Specify the indentation level to beautify the output JSON data, making it more readable. Effective only when format_json is True	4
		ensure_ascii	bool	Control whether to escape non-ASCII characters to Unicode. When set to True, all non-ASCII characters will be escaped; False will retain the original characters. Effective only when format_json is True	False
save_to_img()	Save the result as an image file	save_path	str	Path to save the file, supports directory or file path	None

• Calling the print() method will print the result to the terminal, with the printed content explained as follows:

  • input_path: (str) The input path of the image to be predicted

  • page_index: (Union[int, None]) If the input is a PDF file, it indicates which page of the PDF it is, otherwise it is None

  • boxes: (list) Detection box information, each element is a dictionary containing the following fields

  • cls_id: (int) Category ID
  • label: (str) Category name
  • score: (float) Confidence score
  • coordinates: (list) Detection box coordinates, in the format [x1, y1, x2, y2, x3, y3, x4, y4]

• Calling the save_to_json() method will save the above content to the specified save_path. If specified as a directory, the saved path will be save_path/{your_img_basename}_res.json; if specified as a file, it will be saved directly to that file. Since JSON files do not support saving numpy arrays, the numpy.array types will be converted to lists.

• Calling the save_to_img() method will save the visualization results to the specified save_path. If specified as a directory, the saved path will be save_path/{your_img_basename}_res.{your_img_extension}; if specified as a file, it will be saved directly to that file.

• Additionally, it also supports obtaining visualized images and prediction results through attributes, as follows:

Attribute	Attribute Description
json	Get the predicted json format result
img	Get the visualized image in dict format

• The prediction result obtained by the json attribute is a dict type of data, with content consistent with the content saved by calling the save_to_json() method.
• The prediction result returned by the img attribute is a dictionary type of data. The key is res, and the corresponding value is an Image.Image object: an image used to display the prediction result of rotated object detection.

In addition, you can obtain the rotated object detection pipeline configuration file and load the configuration file for prediction. You can execute the following command to save the result in my_path:

paddlex --get_pipeline_config rotated_object_detection --save_path ./my_path

If you have obtained the configuration file, you can customize the settings for the Rotated Object Detection Pipeline. Simply modify the pipeline parameter value in the create_pipeline method to the path of the pipeline configuration file. The example is as follows:

from paddlex import create_pipeline

pipeline = create_pipeline(pipeline="./my_path/rotated_object_detection.yaml")

output = pipeline.predict(
    input="./rotated_object_detection_001.png",
    threshold=0.5,
)
for res in output:
    res.print()
    res.save_to_img("./output/")
    res.save_to_json("./output/")

Note: The parameters in the configuration file are the initialization parameters for the pipeline. If you want to change the initialization parameters of the Rotated Object Detection Pipeline, you can directly modify the parameters in the configuration file and load the configuration file for prediction. Additionally, CLI prediction also supports passing in the configuration file by specifying the path with --pipeline.

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, you can refer to the example code in 2.2 Integration via Python Script.

In addition, PaddleX also provides three other deployment methods, detailed as follows:

🚀 High-Performance Inference: In actual production environments, many applications have stringent performance requirements (especially response speed) for deployment strategies to ensure efficient system operation and smooth user experience. Therefore, PaddleX provides a high-performance inference plugin designed to deeply optimize the performance of model inference and pre/post-processing, significantly speeding up the end-to-end process. For detailed high-performance inference procedures, please refer to the PaddleX High-Performance Inference Guide.

☁️ Service Deployment: Service deployment is a common form of deployment in actual production environments. By encapsulating inference functions as services, clients can access these services via network requests to obtain inference results. PaddleX supports multiple pipeline service deployment solutions. For detailed pipeline service deployment procedures, please refer to the PaddleX Service Deployment Guide.

Below are the API references and multi-language service call examples for basic service deployment:

API Reference

For the main operations provided by the service:

• The HTTP request method is POST.
• Both the request body and response body are JSON data (JSON objects).
• When the request is processed successfully, the response status code is 200, and the response body has the following properties:

Name	Type	Description
logId	string	The UUID of the request.
errorCode	integer	Error code. Fixed to 0.
errorMsg	string	Error message. Fixed to "Success".
result	object	Operation result.

• When the request is not processed successfully, the response body has the following properties:

Name	Type	Description
logId	string	The UUID of the request.
errorCode	integer	Error code. Same as the response status code.
errorMsg	string	Error message.

The main operations provided by the service are as follows:

• infer

Perform object detection on the image.

POST /rotated-object-detection

• The request body has the following properties:

Name	Type	Description	Required
image	string	The URL of an image file accessible to the server or the Base64 encoded result of the image file content.	Yes
detThreshold	number | null	Refer to the det_threshold parameter description in the pipeline predict method.	No
clsThreshold	number | array | object | null	Refer to the cls_threshold parameter description in the pipeline predict method.	No

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

Name	Type	Description
detectedObjects	array	Information about the position, category, etc., of the objects.
image	string	Object detection result image. The image is in JPEG format and encoded using Base64.

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

Name	Type	Description
bbox	array	Object position. The elements in the array are the x-coordinate of the top-left corner, y-coordinate of the top-left corner, x-coordinate of the bottom-right corner, and y-coordinate of the bottom-right corner of the bounding box.
categoryId	integer	Object category ID.
categoryName	string	The name of the target category.
score	number	Object score.

An example of the result is as follows:

{
"detectedObjects": [
{
"bbox": [
92.88687133789062,
763.1569213867188,
85.16312408447266,
749.5867919921875,
116.07975006103516,
731.994140625,
123.80349731445312,
745.5642700195312
],
"categoryId": 0,
"score": 0.7418514490127563
},
{
"bbox": [
348.2331848144531,
177.5597381591797,
332.77703857421875,
150.24972534179688,
345.2182922363281,
143.2102813720703,
360.6744384765625,
170.52029418945312
],
"categoryId": 1,
"score": 0.7328268885612488
}
],
"image": "xxxxxx"
}

Multi-language Service Invocation Example

Python

import base64
import requests

API_URL = "http://localhost:8080/rotated-object-detection"  # Service URL
image_path = "./demo.jpg"
output_image_path = "./out.jpg"

# Encode the local image with Base64
with open(image_path, "rb") as file:
    image_bytes = file.read()
    image_data = base64.b64encode(image_bytes).decode("ascii")

payload = {"image": image_data}  # Base64-encoded file content or image URL

# Call the API
response = requests.post(API_URL, json=payload)

# Process the returned data from the interface
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 objects:")
print(result["detectedObjects"])

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"}
    };

    // Encode the local image with Base64
    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();

    // Call the API
    auto response = client.Post("/small-object-detection", headers, body, "application/json");
    // Process the returned data from the interface
    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 detectedObjects = result["detectedObjects"];
        std::cout << "\nDetected objects:" << std::endl;
        for (const auto& category : detectedObjects) {
            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/small-object-detection"; // Service URL
        String imagePath = "./demo.jpg"; // Local image
        String outputImagePath = "./out.jpg"; // Output image

        // Encode the local image using Base64
        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); // Base64-encoded file content or image URL

        // Create an OkHttpClient instance
        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();

        // Call the API and process the response data
        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 detectedObjects = result.get("detectedObjects");

                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("\nDetected objects: " + detectedObjects.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/small-object-detection"
    imagePath := "./demo.jpg"
    outputImagePath := "./out.jpg"

    // Encode the local image using Base64
    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} // Base64-encoded file content or image URL
    payloadBytes, err := json.Marshal(payload)
    if err != nil {
        fmt.Println("Error marshaling payload:", err)
        return
    }

    // Call the API
    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()

    // Process the response data
    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"`
            DetectedObjects []map[string]interface{} `json:"detectedObjects"`
        } `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("\nDetected objects:")
    for _, category := range respData.Result.DetectedObjects {
        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/small-object-detection";
    static readonly string imagePath = "./demo.jpg";
    static readonly string outputImagePath = "./out.jpg";

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

        // Encode the local image in Base64
        byte[] imageBytes = File.ReadAllBytes(imagePath);
        string image_data = Convert.ToBase64String(imageBytes);

        var payload = new JObject{ { "image", image_data } }; // Base64-encoded file content or image URL
        var content = new StringContent(payload.ToString(), Encoding.UTF8, "application/json");

        // Call the API
        HttpResponseMessage response = await httpClient.PostAsync(API_URL, content);
        response.EnsureSuccessStatusCode();

        // Process the response data
        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("\nDetected objects:");
        Console.WriteLine(jsonResponse["result"]["detectedObjects"].ToString());
    }
}

Node.js

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

const API_URL = 'http://localhost:8080/small-object-detection';
const imagePath = './demo.jpg';
const outputImagePath = "./out.jpg";

let config = {
   method: 'POST',
   maxBodyLength: Infinity,
   url: API_URL,
   data: JSON.stringify({
    'image': encodeImageToBase64(imagePath)  // Base64-encoded file content or image URL
  })
};

// Encode the local image in Base64
function encodeImageToBase64(filePath) {
  const bitmap = fs.readFileSync(filePath);
  return Buffer.from(bitmap).toString('base64');
}

// Call the API
axios.request(config)
.then((response) => {
    // Process the response data
    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("\nDetected objects:");
    console.log(result["detectedObjects"]);
})
.catch((error) => {
  console.log(error);
});

PHP

<?php

$API_URL = "http://localhost:8080/small-object-detection"; // Service URL
$image_path = "./demo.jpg";
$output_image_path = "./out.jpg";

// Encode the local image in Base64
$image_data = base64_encode(file_get_contents($image_path));
$payload = array("image" => $image_data); // Base64-encoded file content or image URL

// Call the API
$ch = curl_init($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);

// Process the response data from the API
$result = json_decode($response, true)["result"];
file_put_contents($output_image_path, base64_decode($result["image"]));
echo "Output image saved at " . $output_image_path . "\n";
echo "\nDetected objects:\n";
print_r($result["detectedObjects"]);

?>

📱 Edge Deployment: Edge deployment is a method of placing computing and data processing capabilities directly on user devices, allowing them to process data locally without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. For detailed instructions, please refer to the PaddleX Edge Deployment Guide. You can choose the appropriate deployment method based on your needs to integrate the model pipeline into subsequent AI applications.

4. Secondary Development

If the default model weights provided by the Rotated Object Detection Pipeline do not meet your requirements in terms of accuracy or speed, you can attempt to fine-tune the existing models using your own domain-specific or application-specific data to improve the detection performance in your scenario.

4.1 Model Fine-Tuning

Since the Rotated Object Detection Pipeline includes a rotated object detection module, if the pipeline's performance is not satisfactory, you can analyze the poorly detected images and refer to the fine-tuning tutorial links in the table below for model fine-tuning.

Scenario	Fine-Tuning Module	Fine-Tuning Reference Link
Prediction results are not satisfactory	Rotated Object Detection Module	Link

4.2 Model Application

After fine-tuning with your private dataset, you will obtain the local model weight file.

To use the fine-tuned model weights, simply modify the pipeline configuration file by replacing the path of the fine-tuned model weights with the corresponding location in the pipeline configuration file:

PHP

<?php

$API_URL = "http://localhost:8080/small-object-detection"; // Service URL
$image_path = "./demo.jpg";
$output_image_path = "./out.jpg";

// Encode the local image in Base64
$image_data = base64_encode(file_get_contents($image_path));
$payload = array("image" => $image_data); // Base64-encoded file content or image URL

// Call the API
$ch = curl_init($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);

// Process the response data from the API
$result = json_decode($response, true)["result"];
file_put_contents($output_image_path, base64_decode($result["image"]));
echo "Output image saved at " . $output_image_path . "\n";
echo "\nDetected objects:\n";
print_r($result["detectedObjects"]);

?>

📱 Edge Deployment: Edge deployment is a method of placing computing and data processing capabilities directly on user devices, allowing them to process data locally without relying on remote servers. PaddleX supports deploying models on edge devices such as Android. For detailed instructions, please refer to the PaddleX Edge Deployment Guide. You can choose the appropriate deployment method based on your needs to integrate the model pipeline into subsequent AI applications.

4. Secondary Development

If the default model weights provided by the Rotated Object Detection Pipeline do not meet your requirements in terms of accuracy or speed, you can attempt to fine-tune the existing models using your own domain-specific or application-specific data to improve the detection performance in your scenario.

4.1 Model Fine-Tuning

Since the Rotated Object Detection Pipeline includes a rotated object detection module, if the pipeline's performance is not satisfactory, you can analyze the poorly detected images and refer to the fine-tuning tutorial links in the table below for model fine-tuning.

Scenario	Fine-Tuning Module	Fine-Tuning Reference Link
Prediction results are not satisfactory	Rotated Object Detection Module	Link

4.2 Model Application

After fine-tuning with your private dataset, you will obtain the local model weight file.

To use the fine-tuned model weights, simply modify the pipeline configuration file by replacing the path of the fine-tuned model weights with the corresponding location in the pipeline configuration file:

SubModules:
  RotatedObjectDetection:
    module_name: rotated_object_detection
    model_name: PP-YOLOE-R-L
    model_dir: null # Here replaced with the newly fine-tuned weight path.
    batch_size: 1
    threshold: 0.5

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

5. Multi-Hardware Support

PaddleX supports various mainstream hardware devices such as NVIDIA GPU, Kunlunxin XPU, Ascend NPU, and Cambricon MLU. Simply modify the --device parameter to achieve seamless switching between different hardware.

For example, if you use Ascend NPU for inference with the Rotated Object Detection pipeline, the Python command is:

paddlex --pipeline rotated_object_detection \
        --input rotated_object_detection_001.png \
        --threshold 0.5 \
        --save_path ./output \
        --device npu:0

If you want to use rotated object detection on more types of hardware, please refer to the PaddleX Multi-Hardware Usage Guide.

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