PaddleDetection-release-2.6/deploy/pipeline/docs/tutorials/ppvehicle_attribute_en.md

English | [简体中文](ppvehicle_attribute.md)

# 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](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) |
| Vehicle Attribute Recognition | PPLCNet | 90.81 | 7.31 ms | [Inference and Deployment Model](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) |


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](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/en/models/PP-LCNet_en.md). 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](https://www.v7labs.com/open-datasets/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:

```yaml
# 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](../../config/infer_cfg_ppvehicle.yml) are as follows:

```yaml
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](./PPVehicle_QUICK_STARTED.md).

```bash
# 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
```

4. For video input, please run these commands.

```bash
# 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
```

5. 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:

```bash
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:

<div width="600" align="center">
  <img src="https://user-images.githubusercontent.com/22989727/205599146-56abd72f-6e0a-4a21-bd11-f8bb421f2887.gif"/>
</div>


### Features to the Solution

The vehicle attribute recognition model adopts PULC, Practical Ultra Lightweight image Classification from [PaddleClas](https://github.com/PaddlePaddle/PaddleClas). For details on data preparation, training, and testing of the model, please refer to [PULC Recognition Model of Vehicle Attribute](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/en/PULC/PULC_vehicle_attribute_en.md).

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.