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Jun 30, 2023
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update readme fps #452

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4 changes: 2 additions & 2 deletions configs/det/dbnet/README.md
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Expand Up @@ -114,8 +114,8 @@ DBNet and DBNet++ were trained on the ICDAR2015, MSRA-TD500, SCUT-CTW1500, Total

| **Model** | **Context** | **Backbone** | **Pretrained** | **Recall** | **Precision** | **F-score** | **Train T.** | **Throughput** | **Recipe** | **Download** |
|-------------------|----------------|--------------|----------------|------------|---------------|-------------|--------------|----------------|-----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| DBNet | D910x8-MS2.0-G | ResNet-18 | SynthText | 72.55% | 83.23% | 77.52% | 20.9 s/epoch | 43.1 img/s | [yaml](db_r18_mlt2017.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_mlt2017-5af33809.ckpt) |
| DBNet | D910x8-MS2.0-G | ResNet-50 | SynthText | 74.88% | 83.77% | 79.08% | 23.6 s/epoch | 38.2 img/s | [yaml](db_r50_mlt2017.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_mlt2017-3bd6e569.ckpt) |
| DBNet | D910x8-MS2.0-G | ResNet-18 | SynthText | 72.55% | 83.23% | 77.52% | 20.9 s/epoch | 344.8 img/s | [yaml](db_r18_mlt2017.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_mlt2017-5af33809.ckpt) |
| DBNet | D910x8-MS2.0-G | ResNet-50 | SynthText | 74.88% | 83.77% | 79.08% | 23.6 s/epoch | 305.6 img/s | [yaml](db_r50_mlt2017.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_mlt2017-3bd6e569.ckpt) |
</div>

### SynthText
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4 changes: 2 additions & 2 deletions configs/det/dbnet/README_CN.md
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Expand Up @@ -98,8 +98,8 @@ DBNet和DBNet++在ICDAR2015，MSRA-TD500，SCUT-CTW1500，Total-Text和MLT2017

| **模型** | **环境配置** | **骨干网络** | **预训练数据集** | **Recall** | **Precision** | **F-score** | **训练时间** | **吞吐量** | **配置文件** | **模型权重下载** |
|-----------------|---------------|-------------|----------------|------------|---------------|-------------|--------------|----------------|-----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| DBNet | D910x8-MS2.0-G | ResNet-18 | SynthText | 72.55% | 83.23% | 77.52% | 20.9 s/epoch | 43.1 img/s | [yaml](db_r18_mlt2017.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_mlt2017-5af33809.ckpt) |
| DBNet | D910x8-MS2.0-G | ResNet-50 | SynthText | 74.88% | 83.77% | 79.08% | 23.6 s/epoch | 38.2 img/s | [yaml](db_r50_mlt2017.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_mlt2017-3bd6e569.ckpt) |
| DBNet | D910x8-MS2.0-G | ResNet-18 | SynthText | 72.55% | 83.23% | 77.52% | 20.9 s/epoch | 344.8 img/s | [yaml](db_r18_mlt2017.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet18_mlt2017-5af33809.ckpt) |
| DBNet | D910x8-MS2.0-G | ResNet-50 | SynthText | 74.88% | 83.77% | 79.08% | 23.6 s/epoch | 305.6 img/s | [yaml](db_r50_mlt2017.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_mlt2017-3bd6e569.ckpt) |
</div>

### SynthText
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