PaddleOCR/deploy/slim/quantization/README_en.md

\> PaddleSlim 1.2.0 or higher version should be installed before runing this example.



# Model compress tutorial (Quantization)

Compress results：
<table>
<thead>
  <tr>
    <th>ID</th>
    <th>Task</th>
    <th>Model</th>
    <th>Compress Strategy</th>
    <th>Criterion(Chinese dataset)</th>
    <th>Inference Time(ms)</th>
    <th>Inference Time(Total model)(ms)</th>
    <th>Acceleration Ratio</th>
    <th>Model Size(MB)</th>
    <th>Commpress Ratio</th>
    <th>Download Link</th>
  </tr>
</thead>
<tbody>
  <tr>
    <td rowspan="2">0</td>
    <td>Detection</td>
    <td>MobileNetV3_DB</td>
    <td>None</td>
    <td>61.7</td>
    <td>224</td>
    <td rowspan="2">375</td>
    <td rowspan="2">-</td>
    <td rowspan="2">8.6</td>
    <td rowspan="2">-</td>
    <td></td>
  </tr>
  <tr>
    <td>Recognition</td>
    <td>MobileNetV3_CRNN</td>
    <td>None</td>
    <td>62.0</td>
    <td>9.52</td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="2">1</td>
    <td>Detection</td>
    <td>SlimTextDet</td>
    <td>PACT Quant Aware Training</td>
    <td>62.1</td>
    <td>195</td>
    <td rowspan="2">348</td>
    <td rowspan="2">8%</td>
    <td rowspan="2">2.8</td>
    <td rowspan="2">67.82%</td>
    <td></td>
  </tr>
  <tr>
    <td>Recognition</td>
    <td>SlimTextRec</td>
    <td>PACT Quant Aware Training</td>
    <td>61.48</td>
    <td>8.6</td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="2">2</td>
    <td>Detection</td>
    <td>SlimTextDet_quat_pruning</td>
    <td>Pruning+PACT Quant Aware Training</td>
    <td>60.86</td>
    <td>142</td>
    <td rowspan="2">288</td>
    <td rowspan="2">30%</td>
    <td rowspan="2">2.8</td>
    <td rowspan="2">67.82%</td>
    <td></td>
  </tr>
  <tr>
    <td>Recognition</td>
    <td>SlimTextRec</td>
    <td>PPACT Quant Aware Training</td>
    <td>61.48</td>
    <td>8.6</td>
    <td></td>
  </tr>
  <tr>
    <td rowspan="2">3</td>
    <td>Detection</td>
    <td>SlimTextDet_pruning</td>
    <td>Pruning</td>
    <td>61.57</td>
    <td>138</td>
    <td rowspan="2">295</td>
    <td rowspan="2">27%</td>
    <td rowspan="2">2.9</td>
    <td rowspan="2">66.28%</td>
    <td></td>
  </tr>
  <tr>
    <td>Recognition</td>
    <td>SlimTextRec</td>
    <td>PACT Quant Aware Training</td>
    <td>61.48</td>
    <td>8.6</td>
    <td></td>
  </tr>
</tbody>
</table>



## Overview

Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model. Quantization is a technique that reduces this redundancyby reducing the full precision data to a fixed number, so as to reduce model calculation complexity and improve model inference performance.

This example uses PaddleSlim provided [APIs of Quantization](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/) to compress the OCR model.

It is recommended that you could understand following pages before reading this example,：



- [The training strategy of OCR model](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/detection.md)

- [PaddleSlim Document](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/)



## Install PaddleSlim

```bash
git clone https://github.com/PaddlePaddle/PaddleSlim.git

cd Paddleslim

python setup.py install

```


## Download Pretrain Model

[Download link of Detection pretrain model]()

[Download link of recognization pretrain model]()


## Quan-Aware Training

After loading the pre training model, the model can be quantified after defining the quantization strategy. For specific details of quantization method, see：[Model Quantization](https://paddleslim.readthedocs.io/zh_CN/latest/api_cn/quantization_api.html)

Enter the PaddleOCR root directory，perform model quantization with the following command：

```bash
python deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Global.pretrain_weights=./deploy/slim/prune/pretrain_models/det_mv3_db/best_accuracy Global.test_batch_size_per_card=1
```



## Export inference model

After getting the model after pruning and finetuning we, can export it as inference_model for predictive deployment:

```bash
python deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=output/quant_model/best_accuracy Global.save_model_dir=./output/quant_inference_model
```