The vehicle attribute recognition pipeline includes a vehicle detection module and a vehicle attribute recognition module, with several models in each module. Which models to use 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.
> The inference time only includes the model inference time and does not include the time for pre- or post-processing.
Vehicle Detection Module:
| Model | Model Download Link | mAP 0.5:0.95 | GPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
CPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
Model Storage Size (MB) | Description |
|---|---|---|---|---|---|---|
| PP-YOLOE-S_vehicle | Inference Model/Training Model | 61.3 | 10.54 / 6.69 | 52.73 / 23.58 | 28.79 | Vehicle detection model based on PP-YOLOE |
| PP-YOLOE-L_vehicle | Inference Model/Training Model | 63.9 | 30.30 / 26.27 | 169.28 / 111.88 | 196.02 |
Vehicle Attribute Recognition Module:
| Model | Model Download Link | mAP (%) | GPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
CPU Inference Time (ms) [Normal Mode / High-Performance Mode] |
Model Storage Size (MB) | Description |
|---|---|---|---|---|---|---|
| PP-LCNet_x1_0_vehicle_attribute | Inference Model/Training Model | 91.7 | 2.53 / 0.67 | 2.73 / 1.10 | 6.7 | PP-LCNet_x1_0_vehicle_attribute is a lightweight vehicle attribute recognition model based on PP-LCNet. |
| 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.) |
If you are satisfied with the performance of the pipeline, you can directly integrate and deploy it. You can choose to download the deployment package from the cloud, or refer to the methods in [Section 2.2 Local Experience](#22-local-experience) for local deployment. If you are not satisfied with the effect, you can fine-tune the models in the pipeline using your private data. If you have local hardware resources for training, you can start training directly on your local machine; if not, the Star River Zero-Code platform provides a one-click training service. You don't need to write any code—just upload your data and start the training task with one click.
### 2.2 Local Experience
Before using the vehicle attribute recognition pipeline locally, please ensure that you have completed the installation of the PaddleX wheel package according to the [PaddleX Local Installation Guide](../../../installation/installation.en.md). If you wish to selectively install dependencies, please refer to the relevant instructions in the installation guide. The dependency group corresponding to this pipeline is `cv`.
#### 2.2.1 Command Line Experience
You can quickly experience the vehicle attribute recognition pipeline with a single command. Use the [test file](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_attribute_002.jpg) and replace `--input` with the local path for prediction.
```bash
paddlex --pipeline vehicle_attribute_recognition --input vehicle_attribute_002.jpg --device gpu:0 --save_path ./output/
```
Parameter Description:
```bash
{'res': {'input_path': 'vehicle_attribute_002.jpg', 'boxes': [{'labels': ['red(红色)', 'sedan(轿车)'], 'cls_scores': array([0.96375, 0.94025]), 'det_score': 0.9774094820022583, 'coordinate': [196.32553, 302.3847, 639.3131, 655.57904]}, {'labels': ['suv(SUV)', 'brown(棕色)'], 'cls_scores': array([0.99968, 0.99317]), 'det_score': 0.9705657958984375, 'coordinate': [769.4419, 278.8417, 1401.0217, 641.3569]}]}}
```
For the explanation of the running result parameters, you can refer to the result interpretation in [Section 2.2.2 Integration via Python Script](#222-integration-via-python-script). Supports specifying multiple devices simultaneously for parallel inference. For details, please refer to the documentation on pipeline parallel inference.
The visualization results are saved under `save_path`, and the visualization result is as follows:
#### 2.2.2 Integration via Python Script
* The above command line is for quick experience and viewing of results. Generally, in projects, integration through code is often required. You can complete the pipeline's fast inference with just a few lines of code. The inference code is as follows:
The results obtained are the same as those from the command line method.
In the above Python script, the following steps are executed:
(1) The vehicle attribute recognition pipeline object is instantiated via `create_pipeline()`. The specific parameter descriptions are as follows:
| Parameter | 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 a pipeline, it must be supported by PaddleX. | str |
None | |
config |
Specific configuration information for the pipeline (if set simultaneously with pipeline, it has higher priority than pipeline, and the pipeline name must be consistent with pipeline). |
dict[str, Any] |
None |
|
device |
The device used for pipeline inference. It supports specifying the specific card number of GPUs, such as "gpu:0", other hardware card numbers, such as "npu:0", and CPUs, such as "cpu". Supports specifying multiple devices simultaneously for parallel inference. For details, please refer to Pipeline Parallel Inference. | str |
gpu:0 |
|
use_hpip |
Whether to enable the high-performance inference plugin. If set to None, the setting from the configuration file or config will be used. |
bool |
None | None |
hpi_config |
High-performance inference configuration | dict | None |
None | None |
| Parameter | Parameter Description | Parameter Type | Options | Default Value |
|---|---|---|---|---|
input |
The data to be predicted. It supports multiple input types and is required. | Python Var|str|list |
|
None |
det_threshold |
Threshold for vehicle detection visualization. | float | None |
|
0.5 |
cls_threshold |
Threshold for vehicle attribute prediction. | float | dict | list | None |
|
0.7 |
| Method | Description | Parameter | Type | 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. This is only effective when format_json is True. |
4 | ||
ensure_ascii |
bool |
Control whether to escape non-ASCII characters to Unicode. If set to True, all non-ASCII characters will be escaped; False will retain the original characters. This is only effective when format_json is True. |
False |
||
save_to_json() |
Save the result as a JSON file. | save_path |
str |
The file path to save the result. When specified as a directory, the saved file will have the same name as the input file. | None |
indent |
int |
Specify the indentation level to beautify the output JSON data, making it more readable. This is only effective when format_json is True. |
4 | ||
ensure_ascii |
bool |
Control whether to escape non-ASCII characters to Unicode. If set to True, all non-ASCII characters will be escaped; False will retain the original characters. This is only effective when format_json is True. |
False |
||
save_to_img() |
Save the result as an image file. | save_path |
str |
The file path to save the result, supporting both directory and file paths. | None |
print() method, the result will be printed to the terminal. The printed content is 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 the current page number of the PDF; otherwise, it is `None`.
- `boxes`: `(List[Dict])` The category IDs of the prediction results.
- `labels`: `(List[str])` The category names of the prediction results.
- `cls_scores`: `(List[numpy.ndarray])` The confidence scores of the attribute prediction results.
- `det_scores`: `(float)` The confidence scores of the vehicle detection boxes.
- Calling the `save_to_json()` method will save the above content to the specified `save_path`. If a directory is specified, the saved path will be `save_path/{your_img_basename}_res.json`. If a file is specified, it will be saved directly to that file. Since JSON files do not support saving numpy arrays, the `numpy.array` type will be converted to a list format.
- Calling the `save_to_img()` method will save the visualization result to the specified `save_path`. If a directory is specified, the saved path will be `save_path/{your_img_basename}_res.{your_img_extension}`. If a file is specified, it will be saved directly to that file. (The pipeline usually contains many result images, so it is not recommended to specify a specific file path directly, otherwise multiple images will be overwritten, and only the last image will be retained.)
* Additionally, it also supports obtaining visualized images with results and prediction results through attributes, as follows:
| Attribute | Description |
|---|---|
json |
Get the prediction result in json format |
img |
Get the visualized image in dict format |
For the main operations provided by the service:
200, and the attributes of the response body are as follows:| Name | Type | Description |
|---|---|---|
logId |
string |
The UUID of the request. |
errorCode |
integer |
Error code. Fixed as 0. |
errorMsg |
string |
Error message. Fixed as "Success". |
result |
object |
The result of the operation. |
| 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:
inferGet the vehicle attribute recognition results.
POST /vehicle-attribute-recognition
| Name | Type | Description | Required |
|---|---|---|---|
image |
string |
The URL of the image file accessible by the server or the Base64-encoded content of the image file. | Yes |
detThreshold |
number | null |
Please refer to the description of the det_threshold parameter of the pipeline object's predict method. |
No |
clsThreshold |
number | array | object | null |
Please refer to the description of the cls_threshold parameter of the pipeline object's predict method. |
No |
visualize |
boolean | null |
Whether to return the final visualization image and intermediate images during the processing.
For example, adding the following setting to the pipeline config file: visualize parameter in the request.If neither the request body nor the configuration file is set (If visualize is set to null in the request and not defined in the configuration file), the image is returned by default.
|
No |
result of the response body has the following attributes:| Name | Type | Description |
|---|---|---|
vehicles |
array |
Information about the vehicle's location and attributes. |
image |
string| null |
The vehicle attribute recognition result image. The image is in JPEG format and is Base64-encoded. |
Each element in vehicles is an object with the following attributes:
| Name | Type | Description |
|---|---|---|
bbox |
array |
The location of the vehicle. 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. |
attributes |
array |
The attributes of the vehicle. |
score |
number |
Detection score. |
Each element in attributes is an object with the following attributes:
| Name | Type | Description |
|---|---|---|
label |
string |
The attribute label. |
score |
number |
Classification score. |
import base64
import requests
API_URL = "http://localhost:8080/vehicle-attribute-recognition" # Service URL
image_path = "./demo.jpg"
output_image_path = "./out.jpg"
# Encode the local image using 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 response data
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 vehicles:")
print(result["vehicles"])
| Scenario | Fine-Tuning Module | Fine-Tuning Reference Link |
|---|---|---|
| Inaccurate vehicle detection | Vehicle Detection Module | Link |
| Inaccurate attribute recognition | Vehicle Attribute Recognition Module | Link |