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PULC Classification Model of Containing or Uncontaining Car

Catalogue

1. Introduction

This case provides a way for users to quickly build a lightweight, high-precision and practical classification model of car exists using PaddleClas PULC (Practical Ultra Lightweight image Classification). The model can be widely used in monitoring scenarios, massive data filtering scenarios, etc.

The following table lists the relevant indicators of the model. The first two lines means that using SwinTransformer_tiny and MobileNetV3_small_x0_35 as the backbone to training. The third to sixth lines means that the backbone is replaced by PPLCNet, additional use of EDA strategy and additional use of EDA strategy and SKL-UGI knowledge distillation strategy.

Backbone Tpr(%) Latency(ms) Size(M) Training Strategy
SwinTranformer_tiny 97.71 95.30 111 using ImageNet pretrained model
MobileNetV3_small_x0_35 81.23 2.85 2.7 using ImageNet pretrained model
PPLCNet_x1_0 94.72 2.12 7.1 using ImageNet pretrained model
PPLCNet_x1_0 95.48 2.12 7.1 using SSLD pretrained model
PPLCNet_x1_0 95.48 2.12 7.1 using SSLD pretrained model + EDA strategy
PPLCNet_x1_0 95.92 2.12 7.1 using SSLD pretrained model + EDA strategy + SKL-UGI knowledge distillation strategy

It can be seen that high Tpr can be getted when backbone is SwinTranformer_tiny, but the speed is slow. Replacing backbone with the lightweight model MobileNetV3_small_x0_35, the speed can be greatly improved, but the Tpr will be greatly reduced. Replacing backbone with faster backbone PPLCNet_x1_0, the Tpr is higher more 13 percentage points than MobileNetv3_small_x0_35. At the same time, the speed can be more than 20% faster. After additional using the SSLD pretrained model, the Tpr can be improved by about 0.7 percentage points without affecting the inference speed. Finally, after additional using the SKL-UGI knowledge distillation, the Tpr can be further improved by 0.44 percentage points. At this point, the Tpr is close to that of SwinTranformer_tiny, but the speed is more than 40 times faster. The training method and deployment instructions of PULC will be introduced in detail below.

Note:

  • About Tpr metric, please refer to 3.2 section for more information .
  • The Latency is tested on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz. The MKLDNN is enabled and the number of threads is 10.
  • About PP-LCNet, please refer to PP-LCNet Introduction and PP-LCNet Paper.

2. Quick Start

2.1 PaddlePaddle Installation

  • Run the following command to install if CUDA9 or CUDA10 is available.
python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
  • Run the following command to install if GPU device is unavailable.
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple

Please refer to PaddlePaddle Installation for more information about installation, for examples other versions.

2.2 PaddleClas wheel Installation

The command of PaddleClas installation as bellow:

pip3 install paddleclas

2.3 Prediction

First, please click here to download and unzip to get the test demo images.

  • Prediction with CLI
paddleclas --model_name=car_exists --infer_imgs=pulc_demo_imgs/car_exists/objects365_00001507.jpeg

Results:

>>> result
class_ids: [1], scores: [0.9871138], label_names: ['contains_car'], filename: pulc_demo_imgs/car_exists/objects365_00001507.jpeg
Predict complete!

Note: If you want to test other images, only need to specify the --infer_imgs argument, and the directory containing images is also supported.

  • Prediction in Python
import paddleclas
model = paddleclas.PaddleClas(model_name="car_exists")
result = model.predict(input_data="pulc_demo_imgs/car_exists/objects365_00001507.jpeg")
print(next(result))

Note: The result returned by model.predict() is a generator, so you need to use the next() function to call it or for loop to loop it. And it will predict with batch_size size batch and return the prediction results when called. The default batch_size is 1, and you also specify the batch_size when instantiating, such as model = paddleclas.PaddleClas(model_name="car_exists", batch_size=2). The result of demo above:

>>> result
[{'class_ids': [1], 'scores': [0.9871138], 'label_names': ['contains_car'], 'filename': 'pulc_demo_imgs/car_exists/objects365_00001507.jpeg'}]

3. Training, Evaluation and Inference

3.1 Installation

Please refer to Installation to get the description about installation.

3.2 Dataset

3.2.1 Dataset Introduction

All datasets used in this case are open source data. Train and validation data are the subset of Object365 data. ImageNet_val is ImageNet-1k validation data.

3.2.2 Getting Dataset

The data used in this case can be getted by processing the open source data. The detailed processes are as follows:

  • Training data. This case deals with the annotation file of Objects365 data training data. If a certain image contains the label of "car" and the area of this box is greater than 10% in the whole image, it is considered that the image contains car. If there is no label of any vehicle in a certain image, such as car, bus and so on, it is considered that the image does not contain car. After processing, 108629 images were obtained, including 27422 images containing car and 81207 images uncontaining car.
  • Validation data: Same as Training data, but checked manually to remove some labeled wrong images.

Note: the labels of objects365 are not completely mutually exclusive. For example, F1 racing cars may be "F1 formula" or "car". In order to reduce the interference, we only keep the "car" label as containing car, and the figure without any vehicle as uncontaining car.

Some image of the processed dataset is as follows:

And you can also download the data processed directly.

cd path_to_PaddleClas

Enter the dataset/ directory, download and unzip the dataset.

cd dataset
wget https://paddleclas.bj.bcebos.com/data/PULC/car_exists.tar
tar -xf car_exists.tar
cd ../

The datas under car_exists directory:


├── objects365_car
│   ├── objects365_00000039.jpg
│   ├── objects365_00000099.jpg
├── ImageNet_val
│   ├── ILSVRC2012_val_00000001.JPEG
│   ├── ILSVRC2012_val_00000002.JPEG
...
├── train_list.txt
├── train_list.txt.debug
├── train_list_for_distill.txt
├── val_list.txt
└── val_list.txt.debug

Where train/ and val/ are training set and validation set respectively. The train_list.txt and val_list.txt are label files of training data and validation data respectively. The file train_list.txt.debug and val_list.txt.debug are subset of train_list.txt and val_list.txt respectively. ImageNet_val/ is the validation data of ImageNet-1k, which will be used for SKL-UGI knowledge distillation, and its label file is train_list_for_distill.txt.

Note:

3.3 Training

The details of training config in ppcls/configs/PULC/car_exists/PPLCNet_x1_0.yaml. The command about training as follows:

export CUDA_VISIBLE_DEVICES=0,1,2,3
python3 -m paddle.distributed.launch \
    --gpus="0,1,2,3" \
    tools/train.py \
        -c ./ppcls/configs/PULC/car_exists/PPLCNet_x1_0.yaml

The best metric of validation data is between 0.95 and 0.96. There would be fluctuations because the data size is small.

Note:

  • The metric Tpr, that describe the True Positive Rate when False Positive Rate is less than a certain threshold(1/100 used in this case), is one of the commonly used metric for binary classification. About the details of Fpr and Tpr, please refer here.
  • When evaluation, the best metric TprAtFpr will be printed that include Fpr, Tpr and the current threshold. The Tpr means the Recall rate under the current Fpr. The Tpr higher, the model better. The threshold would be used in deployment, which means the classification threshold under best Fpr metric.

3.4 Evaluation

After training, you can use the following commands to evaluate the model.

python3 tools/eval.py \
    -c ./ppcls/configs/PULC/car_exists/PPLCNet_x1_0.yaml \
    -o Global.pretrained_model="output/PPLCNet_x1_0/best_model"

Among the above command, the argument -o Global.pretrained_model="output/PPLCNet_x1_0/best_model" specify the path of the best model weight file. You can specify other path if needed.

3.5 Inference

After training, you can use the model that trained to infer. Command is as follow:

python3 tools/infer.py \
    -c ./ppcls/configs/PULC/car_exists/PPLCNet_x1_0.yaml \
    -o Global.pretrained_model=output/PPLCNet_x1_0/best_model

The results:

[{'class_ids': [1], 'scores': [0.9871138], 'label_names': ['contains_car'], 'filename': 'deploy/images/PULC/car_exists/objects365_00001507.jpeg'}]

Note:

  • Among the above command, argument -o Global.pretrained_model="output/PPLCNet_x1_0/best_model" specify the path of the best model weight file. You can specify other path if needed.
  • The default test image is deploy/images/PULC/car_exists/objects365_00001507.jpeg. And you can test other image, only need to specify the argument -o Infer.infer_imgs=path_to_test_image.
  • The default threshold is 0.5. If needed, you can specify the argument Infer.PostProcess.threshold, such as: -o Infer.PostProcess.threshold=0.9794. And the argument threshold is needed to be specified according by specific case. The 0.9794 is the best threshold when Fpr is less than 1/100 in this valuation dataset.

4. Model Compression

4.1 SKL-UGI Knowledge Distillation

SKL-UGI is a simple but effective knowledge distillation algrithem proposed by PaddleClas.

4.1.1 Teacher Model Training

Training the teacher model with hyperparameters specified in ppcls/configs/PULC/car_exists/PPLCNet/PPLCNet_x1_0.yaml. The command is as follow:

export CUDA_VISIBLE_DEVICES=0,1,2,3
python3 -m paddle.distributed.launch \
    --gpus="0,1,2,3" \
    tools/train.py \
        -c ./ppcls/configs/PULC/car_exists/PPLCNet_x1_0.yaml \
        -o Arch.name=ResNet101_vd

The best metric of validation data is between 0.96 and 0.98. The best teacher model weight would be saved in file output/ResNet101_vd/best_model.pdparams.

4.1.2 Knowledge Distillation Training

The training strategy, specified in training config file ppcls/configs/PULC/car_exists/PPLCNet_x1_0_distillation.yaml, the teacher model is ResNet101_vd, the student model is PPLCNet_x1_0 and the additional unlabeled training data is validation data of ImageNet1k. The command is as follow:

export CUDA_VISIBLE_DEVICES=0,1,2,3
python3 -m paddle.distributed.launch \
    --gpus="0,1,2,3" \
    tools/train.py \
        -c ./ppcls/configs/PULC/car_exists/PPLCNet_x1_0_distillation.yaml \
        -o Arch.models.0.Teacher.pretrained=output/ResNet101_vd/best_model

The best metric is between 0.95 and 0.97. The best student model weight would be saved in file output/DistillationModel/best_model_student.pdparams.

5. Hyperparameters Searching

The hyperparameters used by 3.2 section and 4.1 section are according by Hyperparameters Searching in PaddleClas. If you want to get better results on your own dataset, you can refer to Hyperparameters Searching to get better hyperparameters.

Note: This section is optional. Because the search process will take a long time, you can selectively run according to your specific. If not replace the dataset, you can ignore this section.

6. Inference Deployment

6.1 Getting Paddle Inference Model

Paddle Inference is the original Inference Library of the PaddlePaddle, provides high-performance inference for server deployment. And compared with directly based on the pretrained model, Paddle Inference can use tools to accelerate prediction, so as to achieve better inference performance. Please refer to Paddle Inference for more information.

Paddle Inference need Paddle Inference Model to predict. Two process provided to get Paddle Inference Model. If want to use the provided by PaddleClas, you can download directly, click Downloading Inference Model.

6.1.1 Exporting Paddle Inference Model

The command about exporting Paddle Inference Model is as follow:

python3 tools/export_model.py \
    -c ./ppcls/configs/PULC/car_exists/PPLCNet_x1_0.yaml \
    -o Global.pretrained_model=output/DistillationModel/best_model_student \
    -o Global.save_inference_dir=deploy/models/PPLCNet_x1_0_car_exists_infer

After running above command, the inference model files would be saved in deploy/models/PPLCNet_x1_0_car_exists_infer, as shown below:

├── PPLCNet_x1_0_car_exists_infer
│   ├── inference.pdiparams
│   ├── inference.pdiparams.info
│   └── inference.pdmodel

Note: The best model is from knowledge distillation training. If knowledge distillation training is not used, the best model would be saved in output/PPLCNet_x1_0/best_model.pdparams.

6.1.2 Downloading Inference Model

You can also download directly.

cd deploy/models
# download the inference model and decompression
wget https://paddleclas.bj.bcebos.com/models/PULC/car_exists_infer.tar && tar -xf car_exists_infer.tar

After decompression, the directory models should be shown below.

├── car_exists_infer
│   ├── inference.pdiparams
│   ├── inference.pdiparams.info
│   └── inference.pdmodel

6.2 Prediction with Python

6.2.1 Image Prediction

Return the directory deploy:

cd ../

Run the following command to classify whether there are cars in the image ./images/PULC/car_exists/objects365_00001507.jpeg.

# Use the following command to predict with GPU.
python3.7 python/predict_cls.py -c configs/PULC/car_exists/inference_car_exists.yaml
# Use the following command to predict with CPU.
python3.7 python/predict_cls.py -c configs/PULC/car_exists/inference_car_exists.yaml -o Global.use_gpu=False

The prediction results:

objects365_00001507.jpeg:       class id(s): [1], score(s): [0.99], label_name(s): ['contains_car']

Note: The default threshold is 0.5. If needed, you can specify the argument Infer.PostProcess.threshold, such as: -o Infer.PostProcess.threshold=0.9794. And the argument threshold is needed to be specified according by specific case. The 0.9794 is the best threshold when Fpr is less than 1/100 in this valuation dataset. Please refer to 3.3 section for details.

6.2.2 Images Prediction

If you want to predict images in directory, please specify the argument Global.infer_imgs as directory path by -o Global.infer_imgs. The command is as follow.

# Use the following command to predict with GPU. If want to replace with CPU, you can add argument -o Global.use_gpu=False
python3.7 python/predict_cls.py -c configs/PULC/car_exists/inference_car_exists.yaml -o Global.infer_imgs="./images/PULC/car_exists/"

All prediction results will be printed, as shown below.

objects365_00001507.jpeg:       class id(s): [1], score(s): [0.99], label_name(s): ['contains_car']
objects365_00001521.jpeg:       class id(s): [0], score(s): [0.99], label_name(s): ['no_car']

Among the prediction results above, contains_car means that there is a car in the image, no_car means that there is no car in the image.

6.3 Deployment with C++

PaddleClas provides an example about how to deploy with C++. Please refer to Deployment with C++.

6.4 Deployment as Service

Paddle Serving is a flexible, high-performance carrier for machine learning models, and supports different protocol, such as RESTful, gRPC, bRPC and so on, which provides different deployment solutions for a variety of heterogeneous hardware and operating system environments. Please refer Paddle Serving for more information.

PaddleClas provides an example about how to deploy as service by Paddle Serving. Please refer to Paddle Serving Deployment.

6.5 Deployment on Mobile

Paddle-Lite is an open source deep learning framework that designed to make easy to perform inference on mobile, embeded, and IoT devices. Please refer to Paddle-Lite for more information.

PaddleClas provides an example of how to deploy on mobile by Paddle-Lite. Please refer to Paddle-Lite deployment.

6.6 Converting To ONNX and Deployment

Paddle2ONNX support convert Paddle Inference model to ONNX model. And you can deploy with ONNX model on different inference engine, such as TensorRT, OpenVINO, MNN/TNN, NCNN and so on. About Paddle2ONNX details, please refer to Paddle2ONNX.

PaddleClas provides an example of how to convert Paddle Inference model to ONNX model by paddle2onnx toolkit and predict by ONNX model. You can refer to paddle2onnx for deployment details.