@@ -33,8 +33,8 @@ For the introduction of PP-LCNet, please refer to [paper](https://arxiv.org/pdf/
## Features
PaddleClas release PP-HGNet、PP-LCNetv2、 PP-LCNet and **S**imple **S**emi-supervised **L**abel **D**istillation algorithms, and support plenty of
image classification and image recognition algorithms.
PaddleClas release PP-HGNet、PP-LCNetv2、 PP-LCNet and **S**imple **S**emi-supervised **L**abel **D**istillation algorithms, and support plenty of
image classification and image recognition algorithms.
Based on th algorithms above, PaddleClas release PP-ShiTu image recognition system and [**P**ractical **U**ltra **L**ight-weight image **C**lassification solutions](docs/en/PULC/PULC_quickstart_en.md).
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@@ -52,12 +52,15 @@ Based on th algorithms above, PaddleClas release PP-ShiTu image recognition syst
## Quick Start
Quick experience of PP-ShiTu image recognition system：[Link](./docs/en/tutorials/quick_start_recognition_en.md)
Quick experience of **P**ractical **U**ltra **L**ight-weight image **C**lassification models：[Link](docs/en/PULC/PULC_quickstart.md)
Quick experience of **P**ractical **U**ltra **L**ight-weight image **C**lassification models：[Link](docs/en/PULC/PULC_quickstart_en.md)
This section introduce that how to convert the Paddle Inference Model ResNet50_vd to ONNX model and deployment based on ONNX engine.
## 1. Installation
First, you need to install Paddle2ONNX and onnxruntime. Paddle2ONNX is a toolkit to convert Paddle Inference Model to ONNX model. Please refer to [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/README_en.md) for more information.
- Paddle2ONNX Installation
```
python3.7 -m pip install paddle2onnx
```
- ONNX Installation
```
python3.7 -m pip install onnxruntime
```
## 2. Converting to ONNX
Download the Paddle Inference Model ResNet50_vd:
```
cd deploy
mkdir models && cd models
wget -nc https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar && tar xf ResNet50_vd_infer.tar
The solution mainly includes 4 parts, namely: PP-LCNet lightweight backbone network, SSLD pre-trained model, Ensemble Data Augmentation (EDA) and SKL-UGI knowledge distillation algorithm. In addition, we also adopt the method of hyperparameter search to efficiently optimize the hyperparameters in training. Below, we take the person exists or not scene as an example to illustrate the solution.
The solution mainly includes 4 parts, namely: PP-LCNet lightweight backbone network, SSLD pre-trained model, Ensemble Data Augmentation (EDA) and SKL-UGI knowledge distillation algorithm. In addition, we also adopt the method of hyperparameters searching to efficiently optimize the hyperparameters in training. Below, we take the person exists or not scene as an example to illustrate the solution.
**Note**：For some specific scenarios, we provide basic training documents for reference, such as [person exists or not classification model](PULC_person_exists_en.md), etc. You can find these documents [here](./PULC_model_list_en.md). If the methods in these documents do not meet your needs, or if you need a custom training task, you can refer to this document.
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@@ -201,22 +201,22 @@ We also used the same optimization strategy in the other 8 scenarios and got the
It can be seen from the results that the PULC scheme can improve the model accuracy in multiple application scenarios. Using the PULC scheme can greatly reduce the workload of model optimization and quickly obtain models with higher accuracy.
<aname="4"></a>
### 4. Hyperparameter Search
### 4. Hyperparameters Searching
In the above training process, we adjusted parameters such as learning rate, data augmentation probability, and stage learning rate mult list. The optimal values of these parameters may not be the same in different scenarios. We provide a quick hyperparameter search script to automate the process of hyperparameter tuning. This script traverses the parameters in the search value list to replace the parameters in the default configuration, then trains in sequence, and finally selects the parameters corresponding to the model with the highest accuracy as the search result.
In the above training process, we adjusted parameters such as learning rate, data augmentation probability, and stage learning rate mult list. The optimal values of these parameters may not be the same in different scenarios. We provide a quick hyperparameters searching script to automate the process of hyperparameter tuning. This script traverses the parameters in the search value list to replace the parameters in the default configuration, then trains in sequence, and finally selects the parameters corresponding to the model with the highest accuracy as the search result.
<aname="4.1"></a>
#### 4.1 Search based on default configuration
The configuration file [search.yaml](../../../ppcls/configs/PULC/person_exists/search.yaml) defines the configuration of hyperparameter search in person exists or not scenarios. Use the following commands to complete hyperparameter search.
The configuration file [search.yaml](../../../ppcls/configs/PULC/person_exists/search.yaml) defines the configuration of hyperparameters searching in person exists or not scenarios. Use the following commands to complete hyperparameters searching.
