- 1. Introduction
- 2. Quick Start
- 3. Training, Evaluation and Inference
- 4. Model Compression
- 5. SHAS
- 6. Inference Deployment
This case provides a way for users to quickly build a lightweight, high-precision and practical classification model of language in the image using PaddleClas PULC (Practical Ultra Lightweight image Classification). The model can be widely used in various scenarios involving multilingual OCR processing, such as finance and government affairs.
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. When replacing the backbone with PPLCNet_x1_0, the input shape of model is changed to [192, 48], and the stride of the network is changed to [2, [2, 1], [2, 1], [2, 1]].
| Backbone | Top1-Acc(%) | Latency(ms) | Size(M) | Training Strategy |
|---|---|---|---|---|
| SwinTranformer_tiny | 98.12 | 89.09 | 111 | using ImageNet pretrained model |
| MobileNetV3_small_x0_35 | 95.92 | 2.98 | 3.7 | using ImageNet pretrained model |
| PPLCNet_x1_0 | 98.35 | 2.58 | 7.1 | using ImageNet pretrained model |
| PPLCNet_x1_0 | 98.7 | 2.58 | 7.1 | using SSLD pretrained model |
| PPLCNet_x1_0 | 99.12 | 2.58 | 7.1 | using SSLD pretrained model + EDA strategy |
| PPLCNet_x1_0 | 99.26 | 2.58 | 7.1 | using SSLD pretrained model + EDA strategy + SKL-UGI knowledge distillation strategy |
It can be seen that high accuracy 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 accuracy will be greatly reduced. Replacing backbone with faster backbone PPLCNet_x1_0 and changing the input shape and stride of network, the accuracy is higher more 2.43 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 accuracy can be improved by about 0.35 percentage points without affecting the inference speed. Further, additional using the EDA strategy, the accuracy can be increased by 0.42 percentage points. Finally, after additional using the SKL-UGI knowledge distillation, the accuracy can be further improved by 0.14 percentage points. At this point, the accuracy is higher than that of SwinTranformer_tiny, but the speed is more faster. The training method and deployment instructions of PULC will be introduced in detail below.
Note:
- 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.
- 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/simplePlease refer to PaddlePaddle Installation for more information about installation, for examples other versions.
The command of PaddleClas installation as bellow:
pip3 install paddleclasFirst, please click here to download and unzip to get the test demo images.
- Prediction with CLI
paddleclas --model_name=language_classification --infer_imgs=pulc_demo_imgs/language_classification/word_35404.pngResults:
>>> result
class_ids: [4, 6], scores: [0.88672, 0.01434], label_names: ['japan', 'korean'], filename: pulc_demo_imgs/language_classification/word_35404.png
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="language_classification")
result = model.predict(input_data="pulc_demo_imgs/language_classification/word_35404.png")
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="language_classification", batch_size=2). The result of demo above:
>>> result
[{'class_ids': [4, 6], 'scores': [0.88672, 0.01434], 'label_names': ['japan', 'korean'], 'filename': 'pulc_demo_imgs/language_classification/word_35404.png'}]
Please refer to Installation to get the description about installation.
The models wo provided are trained with internal data, which is not open source yet. So it is suggested that constructing dataset based on open source dataset Multi-lingual scene text detection and recognition to experience the this case.
Some image of the processed dataset is as follows:
The models provided support to classcify 10 languages, which as shown in the following list:
0 : means Arabic
1 : means chinese_cht
2 : means cyrillic
3 : means devanagari
4 : means Japanese
5 : means ka
6 : means Korean
7 : means ta
8 : means te
9 : means Latin
In the Multi-lingual scene text detection and recognition, only Arabic, Japanese, Korean and Latin data are included. 1600 images from each of the four languages are taken as the training data of this case, 300 images as the evaluation data, and 400 images as the supplementary data is used for the SKL-UGI Knowledge Distillation.
Therefore, for the demo dataset in this case, the language categories are shown in following list:
0 : means arabic
4 : means japan
6 : means korean
9 : means latin
Note: The images used in this task should be cropped by text from original image. Only the text line part is used as the image data.
If you want to create your own dataset, you can collect and sort out the data of the required languages in your task as required. 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/language_classification.tar
tar -xf language_classification.tar
cd ../The datas under language_classification directory:
├── img
│ ├── word_1.png
│ ├── word_2.png
...
├── train_list.txt
├── train_list_for_distill.txt
├── test_list.txt
└── label_list.txt
Where img/ is the directory including 9200 images in 4 languages. The train_list.txt and test_list.txt are label files of training data and validation data respectively. label_list.txt is the mapping file corresponding to the four languages. train_list_for_distill.txt is the label list of images used for SKL-UGI Knowledge Distillation.
Note:
- About the contents format of
train_list.txtandval_list.txt, please refer to Description about Classification Dataset in PaddleClas. - About the
train_list_for_distill.txt, please refer to Knowledge Distillation Label.
The details of training config in ppcls/configs/PULC/person_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/language_classification/PPLCNet_x1_0.yaml \
-o Arch.class_num=4Note: Because the class num of demo dataset is 4, the argument -o Arch.class_num=4 should be specifed to change the prediction class num of model to 4.
After training, you can use the following commands to evaluate the model.
python3 tools/eval.py \
-c ./ppcls/configs/PULC/language_classification/PPLCNet_x1_0.yaml \
-o Global.pretrained_model="output/PPLCNet_x1_0/best_model" \
-o Arch.class_num=4Among 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.
After training, you can use the model that trained to infer. Command is as follow:
python3 tools/infer.py \
-c ./ppcls/configs/PULC/language_classification/PPLCNet_x1_0.yaml \
-o Global.pretrained_model="output/PPLCNet_x1_0/best_model" \
-o Arch.class_num=4The results:
[{'class_ids': [4, 9], 'scores': [0.96809, 0.01001], 'file_name': 'deploy/images/PULC/language_classification/word_35404.png', 'label_names': ['japan', 'latin']}]
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/person_exists/objects365_02035329.jpg. And you can test other image, only need to specify the argument-o Infer.infer_imgs=path_to_test_image. - Among the prediction results,
japanmeans japanese andkoreanmeans korean.
SKL-UGI is a simple but effective knowledge distillation algrithem proposed by PaddleClas.
Training the teacher model with hyperparameters specified in ppcls/configs/PULC/language_classification/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/language_classification/PPLCNet_x1_0.yaml \
-o Arch.name=ResNet101_vd \
-o Arch.class_num=4The best teacher model weight would be saved in file output/ResNet101_vd/best_model.pdparams.
Note: Training the ResNet101_vd model requires more GPU memory. If the memory is not enough, you can reduce the learning rate and batch size in the same proportion.
The training strategy, specified in training config file ppcls/configs/PULC/language_classification/PPLCNet_x1_0_distillation.yaml, the teacher model is ResNet101_vd, the student model is PPLCNet_x1_0. 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/language_classification/PPLCNet_x1_0_distillation.yaml \
-o Arch.models.0.Teacher.pretrained=output/ResNet101_vd/best_model \
-o Arch.class_num=4The best student model weight would be saved in file output/DistillationModel/best_model_student.pdparams.
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.
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.
The command about exporting Paddle Inference Model is as follow:
python3 tools/export_model.py \
-c ./ppcls/configs/PULC/language_classification/PPLCNet_x1_0.yaml \
-o Global.pretrained_model=output/DistillationModel/best_model_student \
-o Global.save_inference_dir=deploy/models/PPLCNet_x1_0_language_classification_inferAfter running above command, the inference model files would be saved in deploy/models/PPLCNet_x1_0_language_classification_infer, as shown below:
├── PPLCNet_x1_0_language_classification_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.
You can also download directly.
cd deploy/models
# download the inference model and decompression
wget https://paddleclas.bj.bcebos.com/models/PULC/language_classification_infer.tar && tar -xf language_classification_infer.tar
After decompression, the directory models should be shown below.
├── language_classification_infer
│ ├── inference.pdiparams
│ ├── inference.pdiparams.info
│ └── inference.pdmodel
Return the directory deploy:
cd ../
Run the following command to classify language about the image ./images/PULC/language_classification/word_35404.png.
# Use the following command to predict with GPU.
python3.7 python/predict_cls.py -c configs/PULC/language_classification/inference_language_classification.yaml
# Use the following command to predict with CPU.
python3.7 python/predict_cls.py -c configs/PULC/language_classification/inference_language_classification.yaml -o Global.use_gpu=FalseThe prediction results:
word_35404.png: class id(s): [4, 6], score(s): [0.89, 0.01], label_name(s): ['japan', 'korean']
Note: Among the prediction results, japan means japanese and korean means korean.
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/language_classification/inference_language_classification.yaml -o Global.infer_imgs="./images/PULC/language_classification/"All prediction results will be printed, as shown below.
word_17.png: class id(s): [9, 4], score(s): [0.80, 0.09], label_name(s): ['latin', 'japan']
word_20.png: class id(s): [0, 4], score(s): [0.91, 0.02], label_name(s): ['arabic', 'japan']
word_35404.png: class id(s): [4, 6], score(s): [0.89, 0.01], label_name(s): ['japan', 'korean']
Among the prediction results above, japan means japanese, latin means latin, arabic means arabic and korean means korean.
PaddleClas provides an example about how to deploy with C++. Please refer to Deployment with C++.
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.
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.
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.