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Update det dbnet README (#803)
* Update det dbnet README * Update det dbnet README --------- Co-authored-by: slgao <[email protected]>
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‎configs/det/dbnet/README.md‎

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DBNet and DBNet++ were trained on the ICDAR2015, MSRA-TD500, SCUT-CTW1500, Total-Text, and MLT2017 datasets. In addition, we conducted pre-training on the ImageNet or SynthText dataset and provided a URL to download pretrained weights. All training results are as follows:
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Experiments are tested on ascend 910* with mindspore 2.3.1 graph mode.
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#### ICDAR2015
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| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **recall** | **precision** | **f-score** | **recipe** | **weight** |
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| :------------: | :----------: | :------------: | :-------: | :------------: | :-----------: | :---------------: | :---------: | :-------: | :--------: | :-----------: | :---------: | :------------------------------------: | :--------------------------------------------------------------------------------------------------------: |
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| DBNet | MobileNetV3 | ImageNet | 1 | 10 | O2 | 403.87 s | 65.69 | 152.23 | 74.68% | 79.38% | 76.95% | [yaml](db_mobilenetv3_icdar15.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/dbnet/dbnet_mobilenetv3-e72f9b8b-910v2.ckpt) |
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| DBNet | MobileNetV3 | ImageNet | 1 | 10 | O2 | 403.87 s | 65.69 | 152.23 | 74.68% | 79.38% | 76.95% | [yaml](db_mobilenetv3_icdar15.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/dbnet/dbnet_mobilenetv3-e72f9b8b-910v2.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_mobilenetv3-62c44539-f14c6a13.mindir)|
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| DBNet | MobileNetV3 | ImageNet | 8 | 8 | O2 | 405.35 s | 54.46 | 1175.12 | 76.27% | 76.06% | 76.17% | [yaml](db_mobilenetv3_icdar15_8p.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/dbnet/dbnet_mobilenetv3-7e89e1df-910v2.ckpt) |
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| DBNet | ResNet-50 | ImageNet | 1 | 10 | O2 | 147.81 s | 155.62 | 64.25 | 84.50% | 85.36% | 84.93% | [yaml](db_r50_icdar15.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/dbnet/dbnet_resnet50-48153c3b-910v2.ckpt) |
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| DBNet | ResNet-50 | ImageNet | 1 | 10 | O2 | 147.81 s | 155.62 | 64.25 | 84.50% | 85.36% | 84.93% | [yaml](db_r50_icdar15.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/dbnet/dbnet_resnet50-48153c3b-910v2.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50-c3a4aa24-fbf95c82.mindir) |
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| DBNet | ResNet-50 | ImageNet | 8 | 10 | O2 | 151.23 s | 159.22 | 502.4 | 81.15% | 87.63% | 84.26% | [yaml](db_r50_icdar15_8p.yaml) | [ckpt](https://download-mindspore.osinfra.cn/toolkits/mindocr/dbnet/dbnet_resnet50-e10bad35-910v2.ckpt) |
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> The input_shape for exported DBNet MindIR and DBNet++ MindIR in the links are `(1,3,736,1280)` and `(1,3,1152,2048)`, respectively.
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Experiments are tested on ascend 910 with mindspore 2.3.1 graph mode.
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#### ICDAR2015
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| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **recall** | **precision** | **f-score** | **recipe** | **weight** |
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| :------------: | :----------: | :------------: | :-------: | :------------: | :-----------: | :---------------: | :---------: |:---------:| :--------: | :-----------: | :---------: | :------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| DBNet | MobileNetV3 | ImageNet | 1 | 10 | O2 | 321.15 s | 100 | 100 | 76.31% | 78.27% | 77.28% | [yaml](db_mobilenetv3_icdar15.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_mobilenetv3-62c44539.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_mobilenetv3-62c44539-f14c6a13.mindir) |
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| DBNet | MobileNetV3 | ImageNet | 8 | 8 | O2 | 309.39 s | 66.64 | 960 | 76.22% | 77.98% | 77.09% | [yaml](db_mobilenetv3_icdar15_8p.yaml) | Coming soon |
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| DBNet | ResNet-18 | ImageNet | 1 | 20 | O2 | 75.23 s | 185.19 | 108 | 80.12% | 83.41% | 81.73% | [yaml](db_r18_icdar15.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18-0c0c4cfa.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18-0c0c4cfa-cf46eb8b.mindir) |
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| DBNet | ResNet-50 | ImageNet | 1 | 10 | O2 | 110.54 s | 132.98 | 75.2 | 83.53% | 86.62% | 85.05% | [yaml](db_r50_icdar15.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50-c3a4aa24.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50-c3a4aa24-fbf95c82.mindir) |
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| DBNet | ResNet-50 | ImageNet | 8 | 10 | O2 | 107.91 s | 183.92 | 435 | 82.62% | 88.54% | 85.48% | [yaml](db_r50_icdar15_8p.yaml) | Coming soon |
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| DBNet++ | ResNet-50 | SynthText | 1 | 32 | O2 | 184.74 s | 409.21 | 78.2 | 86.81% | 86.85% | 86.86% | [yaml](dbpp_r50_icdar15_910.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnetpp_resnet50_910-35dc71f2.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnetpp_resnet50_910-35dc71f2-e61a9c37.mindir) |
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> The input_shape for exported DBNet MindIR and DBNet++ MindIR in the links are `(1,3,736,1280)` and `(1,3,1152,2048)`, respectively.
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#### MSRA-TD500
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| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **recall** | **precision** | **f-score** | **recipe** | **weight** |
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| :------------: | :----------: | :------------: | :-------: | :------------: | :-----------: | :---------------: | :---------: | :-------: | :--------: | :-----------: | :---------: | :-----------------------: | :---------------------------------------------------------------------------------------------: |
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| DBNet | ResNet-18 | SynthText | 1 | 20 | O2 | 76.18 s | 163.34 | 121.7 | 79.90% | 88.07% | 83.78% | [yaml](db_r18_td500.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_td500-b5abff68.ckpt) |
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| DBNet | ResNet-50 | SynthText | 1 | 20 | O2 | 108.45 s | 280.90 | 71.2 | 84.02% | 87.48% | 85.71% | [yaml](db_r50_td500.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_td500-0d12b5e8.ckpt) |
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> MSRA-TD500 dataset has 300 training images and 200 testing images, reference paper [Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/abs/1911.08947), we trained using an extra 400 traning images from HUST-TR400. You can down all [dataset](https://paddleocr.bj.bcebos.com/dataset/TD_TR.tar) for training.
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#### SCUT-CTW1500
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| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **recall** | **precision** | **f-score** | **recipe** | **weight** |
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|----------------|--------------|----------------|-----------|----------------|---------------|-------------------|-------------|-----------|------------|---------------|-------------|-----------------------------|---------------------------------------------------------------------------------------------------|
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| DBNet | ResNet-18 | SynthText | 1 | 20 | O2 | 73.18 s | 163.80 | 122.1 | 85.68% | 85.33% | 85.50% | [yaml](db_r18_ctw1500.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_ctw1500-0864b040.ckpt) |
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| DBNet | ResNet-50 | SynthText | 1 | 20 | O2 | 110.34 s | 180.11 | 71.4 | 87.83% | 84.71% | 86.25% | [yaml](db_r50_ctw1500.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_ctw1500-f637e3d3.ckpt) |
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#### Total-Text
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| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **recall** | **precision** | **f-score** | **recipe** | **weight** |
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| :------------: | :----------: | :------------: | :-------: | :------------: | :-----------: | :---------------: | :---------: | :-------: | :--------: | :-----------: | :---------: | :---------------------------: | :-------------------------------------------------------------------------------------------------: |
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| DBNet | ResNet-18 | SynthText | 1 | 20 | O2 | 77.78 s | 206.40 | 96.9 | 83.66% | 87.61% | 85.59% | [yaml](db_r18_totaltext.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_totaltext-fb456ff4.ckpt) |
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| DBNet | ResNet-50 | SynthText | 1 | 20 | O2 | 109.15 s | 289.44 | 69.1 | 84.79% | 87.07% | 85.91% | [yaml](db_r50_totaltext.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_totaltext-76d6f421.ckpt) |
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#### MLT2017
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| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **recall** | **precision** | **f-score** | **recipe** | **weight** |
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| :------------: | :----------: | :------------: | :-------: | :------------: | :-----------: | :---------------: | :---------: | :-------: | :--------: | :-----------: | :---------: | :-------------------------: | :-----------------------------------------------------------------------------------------------: |
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| DBNet | ResNet-18 | SynthText | 8 | 20 | O2 | 73.76 s | 464.00 | 344.8 | 73.62% | 83.93% | 78.44% | [yaml](db_r18_mlt2017.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_mlt2017-5af33809.ckpt) |
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| DBNet | ResNet-50 | SynthText | 8 | 20 | O2 | 105.12 s | 523.60 | 305.6 | 76.04% | 84.51% | 80.05% | [yaml](db_r50_mlt2017.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_mlt2017-3bd6e569.ckpt) |
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#### SynthText
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| **model name** | **backbone** | **pretrained** | **cards** | **batch size** | **jit level** | **graph compile** | **ms/step** | **img/s** | **train loss** | **recipe** | **weight** |
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| :------------: | :----------: | :------------: | :-------: | :------------: | :-----------: | :---------------: | :---------: | :-------: | :------------: | :---------------------------: | :-------------------------------------------------------------------------------------------------: |
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| DBNet | ResNet-18 | ImageNet | 1 | 16 | O2 | 78.46 s | 131.83 | 121.37 | 2.41 | [yaml](db_r18_synthtext.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_synthtext-251ef3dd.ckpt) |
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| DBNet | ResNet-50 | ImageNet | 1 | 16 | O2 | 108.93 s | 195.07 | 82.02 | 2.25 | [yaml](db_r50_synthtext.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_synthtext-40655acb.ckpt) |
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| DBNet++ | ResNet-50 | SynthText | 1 | 32 | O2 | 191.93 s | 549.24 | 58.26 | 86.81% | 86.85% | 86.86% | [yaml](dbpp_r50_icdar15.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnetpp_resnet50_910-35dc71f2.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnetpp_resnet50_910-35dc71f2-e61a9c37.mindir)|
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### Notes
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- Note that the training time of DBNet is highly affected by data processing and varies on different machines.
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- The input_shape for exported DBNet MindIR and DBNet++ MindIR in the links are `(1,3,736,1280)` and `(1,3,1152,2048)`, respectively.
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## References

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