DocumentAIKit/deploy/pipeline/docs/tutorials/ppvehicle_attribute_en.md

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Attribute Recognition Module of PP-Vehicle

Vehicle attribute recognition is widely used in smart cities, smart transportation and other scenarios. In PP-Vehicle, a vehicle attribute recognition module is integrated, which can identify vehicle color and model.

Task	Algorithm	Precision	Inference Speed	Download
Vehicle Detection/Tracking	PP-YOLOE	mAP 63.9	38.67ms	Inference and Deployment Model
Vehicle Attribute Recognition	PPLCNet	90.81	7.31 ms	Inference and Deployment Model

Note:

1. The inference speed of the attribute model is obtained from the test on NVIDIA T4, with TensorRT FP16. The time includes data pre-process, model inference and post-process.
2. For introductions, please refer to PP-LCNet Series. Related paper is available on PP-LCNet paper
3. The training and test phase of vehicle attribute recognition model are both obtained from VeRi dataset.

• The provided pre-trained model supports 10 colors and 9 models, which is the same with VeRi dataset. The details are as follows:

# Vehicle Colors
- "yellow"
- "orange"
- "green"
- "gray"
- "red"
- "blue"
- "white"
- "golden"
- "brown"
- "black"

# Vehicle Models
- "sedan"
- "suv"
- "van"
- "hatchback"
- "mpv"
- "pickup"
- "bus"
- "truck"
- "estate"

Instructions

Description of Configuration

Parameters related to vehicle attribute recognition in the config file are as follows:

VEHICLE_ATTR:
  model_dir: output_inference/vehicle_attribute_infer/ # Path of the model
  batch_size: 8     # The size of the inference batch
  color_threshold: 0.5  # Threshold of color. Confidence is required to reach this threshold to determine the specific attribute, otherwise it will be 'Unknown‘.
  type_threshold: 0.5   # Threshold of vehicle model. Confidence is required to reach this threshold to determine the specific attribute, otherwise it will be 'Unknown‘.
  enable: False         # Whether to enable this function

How to Use

1. Download models Vehicle Detection/Tracking and Vehicle Attribute Recognition from the links in Model Zoo and unzip them to ./output_inference. The models are automatically downloaded by default. If you download them manually, you need to modify the model_dir as the model storage path to use this function.

2. Set the "enable: True" of VEHICLE_ATTR in infer_cfg_ppvehicle.yml.

3. For image input, please run these commands. (Description of more parameters, please refer to QUICK_STARTED - Parameter_Description.

# For single image
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
                                                   --image_file=test_image.jpg \
                                                   --device=gpu

# For folder contains one or multiple images
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
                                                   --image_dir=images/ \
                                                   --device=gpu

1. For video input, please run these commands.

# For single video
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
                                                   --video_file=test_video.mp4 \
                                                   --device=gpu

# For folder contains one or multiple videos
python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
                                                   --video_dir=test_videos/ \
                                                   --device=gpu

1. There are two ways to modify the model path:

   • Method 1：Set paths of each model in ./deploy/pipeline/config/infer_cfg_ppvehicle.yml. For vehicle attribute recognition, the path should be modified under the VEHICLE_ATTR field.
   • Method 2: Directly add -o in command line to override the default model path in the configuration file:

python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml \
                                                   --video_file=test_video.mp4 \
                                                   --device=gpu \
                                                   -o VEHICLE_ATTR.model_dir=output_inference/vehicle_attribute_infer

The result is shown as follow:

Features to the Solution

The vehicle attribute recognition model adopts PULC, Practical Ultra Lightweight image Classification from PaddleClas. For details on data preparation, training, and testing of the model, please refer to PULC Recognition Model of Vehicle Attribute.

The vehicle attribute recognition model adopts the lightweight and high-precision PPLCNet. And on top of PPLCNet, our model optimized via::

• Improved about 0.5 percentage points accuracy by using the SSLD pre-trained model without changing the inference speed.
• Improved 0.52 percentage points accuracy further by integrating EDA data augmentation strategy.
• Improved 0.23 percentage points accuracy by using SKL-UGI knowledge distillation.