update benchmark doc
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## 评估指标
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说明：
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- v1.0是未添加优化策略的DB+CRNN模型，v1.1是添加多种优化策略和方向分类器的PP-OCR模型。slim_v1.1是使用裁剪或量化的模型。
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- 检测输入图像的的长边尺寸是960。
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- 评估耗时阶段为图像输入到结果输出的完整阶段，包括了图像的预处理和后处理。
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- 评估耗时阶段为图像预测耗时，不包括图像的预处理和后处理。
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- `Intel至强6148`为服务器端CPU型号，测试中使用Intel MKL-DNN 加速。
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- `骁龙855`为移动端处理平台型号。
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不同预测模型大小和整体识别精度对比
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预测模型大小和整体识别精度对比
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| 模型名称 | 整体模型<br>大小\(M\) | 检测模型<br>大小\(M\) | 方向分类器<br>模型大小\(M\) | 识别模型<br>大小\(M\) | 整体识别<br>F\-score |
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|:-:|:-:|:-:|:-:|:-:|:-:|
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| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 |
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| ch\_ppocr\_server\_v1\.1 | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.5414 |
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| ch\_ppocr\_mobile\_v1\.0 | 8\.6 | 4\.1 | \- | 4\.5 | 0\.393 |
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| ch\_ppocr\_server\_v1\.0 | 203\.8 | 98\.5 | \- | 105\.3 | 0\.4436 |
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| PP-OCRv2 | 11\.6 | 3\.0 | 0\.9 | 8\.6 | 0\.5224 |
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| PP-OCR mobile | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.503 |
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| PP-OCR server | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.570 |
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不同预测模型在T4 GPU上预测速度对比，单位ms
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| 模型名称 | 整体 | 检测 | 方向分类器 | 识别 |
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|:-:|:-:|:-:|:-:|:-:|
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| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24 | 78 |
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| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25 | 140 |
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| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \- | 76 |
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| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \- | 147 |
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预测模型在CPU和GPU上的速度对比，单位ms
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不同预测模型在CPU上预测速度对比，单位ms
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| 模型名称 | CPU | T4 GPU |
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|:-:|:-:|:-:|
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| PP-OCRv2 | 330 | 111 |
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| PP-OCR mobile | 356 | 11 6|
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| PP-OCR server | 1056 | 200 |
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| 模型名称 | 整体 | 检测 | 方向分类器 | 识别 |
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|:-:|:-:|:-:|:-:|:-:|
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| ch\_ppocr\_mobile\_v1\.1 | 421 | 164 | 51 | 206 |
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| ch\_ppocr\_mobile\_v1\.0 | 398 | 219 | \- | 179 |
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裁剪量化模型和原始模型模型大小，整体识别精度和在SD 855上预测速度对比
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| 模型名称 | 整体模型<br>大小\(M\) | 检测模型<br>大小\(M\) | 方向分类器<br>模型大小\(M\) | 识别模型<br>大小\(M\) | 整体识别<br>F\-score | SD 855<br>\(ms\) |
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|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
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| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | 306 |
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| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5 | 1\.4 | 0\.5 | 1\.6 | 0\.521 | 268 |
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更多 PP-OCR 系列模型的预测指标可以参考[PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/benchmark.md)
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@ -13,7 +13,6 @@ We collected 300 images for different real application scenarios to evaluate the
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## MEASUREMENT
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Explanation:
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- v1.0 indicates DB+CRNN models without the strategies. v1.1 indicates the PP-OCR models with the strategies and the direction classify. slim_v1.1 indicates the PP-OCR models with prunner or quantization.
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- The long size of the input for the text detector is 960.
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@ -27,30 +26,16 @@ Compares the model size and F-score:
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| Model Name | Model Size <br> of the <br> Whole System\(M\) | Model Size <br>of the Text <br> Detector\(M\) | Model Size <br> of the Direction <br> Classifier\(M\) | Model Size<br>of the Text <br> Recognizer \(M\) | F\-score |
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|:-:|:-:|:-:|:-:|:-:|:-:|
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| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 |
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| ch\_ppocr\_server\_v1\.1 | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.5414 |
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| ch\_ppocr\_mobile\_v1\.0 | 8\.6 | 4\.1 | \- | 4\.5 | 0\.393 |
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| ch\_ppocr\_server\_v1\.0 | 203\.8 | 98\.5 | \- | 105\.3 | 0\.4436 |
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| PP-OCRv2 | 11\.6 | 3\.0 | 0\.9 | 8\.6 | 0\.5224 |
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| PP-OCR mobile | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.503 |
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| PP-OCR server | 155\.1 | 47\.2 | 0\.9 | 107 | 0\.570 |
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Compares the time-consuming on T4 GPU (ms):
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Compares the time-consuming on CPU and T4 GPU (ms):
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| Model Name | Overall | Text Detector | Direction Classifier | Text Recognizer |
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|:-:|:-:|:-:|:-:|:-:|
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| ch\_ppocr\_mobile\_v1\.1 | 137 | 35 | 24 | 78 |
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| ch\_ppocr\_server\_v1\.1 | 204 | 39 | 25 | 140 |
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| ch\_ppocr\_mobile\_v1\.0 | 117 | 41 | \- | 76 |
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| ch\_ppocr\_server\_v1\.0 | 199 | 52 | \- | 147 |
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| Model Name | CPU | T4 GPU |
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|:-:|:-:|:-:|
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| PP-OCRv2 | 330 | 111 |
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| PP-OCR mobile | 356 | 116|
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| PP-OCR server | 1056 | 200 |
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Compares the time-consuming on CPU (ms):
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| Model Name | Overall | Text Detector | Direction Classifier | Text Recognizer |
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|:-:|:-:|:-:|:-:|:-:|
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| ch\_ppocr\_mobile\_v1\.1 | 421 | 164 | 51 | 206 |
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| ch\_ppocr\_mobile\_v1\.0 | 398 | 219 | \- | 179 |
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Compares the model size, F-score, the time-consuming on SD 855 of between the slim models and the original models:
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| Model Name | Model Size <br> of the <br> Whole System\(M\) | Model Size <br>of the Text <br> Detector\(M\) | Model Size <br> of the Direction <br> Classifier\(M\) | Model Size<br>of the Text <br> Recognizer \(M\) | F\-score | SD 855<br>\(ms\) |
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|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
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| ch\_ppocr\_mobile\_v1\.1 | 8\.1 | 2\.6 | 0\.9 | 4\.6 | 0\.5193 | 306 |
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| ch\_ppocr\_mobile\_slim\_v1\.1 | 3\.5 | 1\.4 | 0\.5 | 1\.6 | 0\.521 | 268 |
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More indicators of PP-OCR series models can be referred to [PP-OCR Benchamrk](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_en/benchmark_en.md)
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