Add SVTR Tiny model #308
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Add SVTR Tiny model #308
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部分data transform目前跟本身有的会有重叠，会在 #300 重构。 |
SamitHuang
reviewed
May 24, 2023
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The result table should align center.
SamitHuang
approved these changes
May 24, 2023
HaoyangLee
reviewed
May 24, 2023
mindocr/models/backbones/rec_svtr.py
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| @register_backbone | ||
| def rec_svtr(pretrained: bool = True, **kwargs): |
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Is the pretrained supposed to be False by default?
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没有一个通用的 pretrained weight，所有一般是靠url提供。
configs/rec/svtr/README_CN.md
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| 如要重现报告的评估结果，您可以： | ||
| - 方法 1：对所有单个数据集重复评估步骤：CUTE80、IC03_860、IC03_867、IC13_857、IC131015、IC15_1811、IC15_2077、IIIT5k_3000、SVT、SVTP。然后取平均分。 | ||
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| - 方法 2：将所有基准数据集文件夹放在同一目录下，例如`评估/`。并使用脚本`tools/benchmarking/multi_dataset_eval.py`。 |
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| - 方法 2：将所有基准数据集文件夹放在同一目录下，例如`评估/`。并使用脚本`tools/benchmarking/multi_dataset_eval.py`。 | |
| - 方法 2：将所有基准数据集文件夹放在同一目录下，例如`evaluation/`。并使用脚本`tools/benchmarking/multi_dataset_eval.py`。 |
configs/rec/svtr/README_CN.md
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| Mindocr内置了一部分字典，均放在了 `mindocr/utils/dict/` 位置，可选择合适的字典使用。 | ||
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| - `en_dict.txt` 是一个包含94个字符的英文字典，其中有数字，常用符号以及大小写的英文字母。 | ||
| - `ch_dict.txt` 是一个包含6623个字符的中文字典，其中有常用的繁简体中文，数字，常用符号以及大小写的英文字母 |
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| - `ch_dict.txt` 是一个包含6623个字符的中文字典，其中有常用的繁简体中文，数字，常用符号以及大小写的英文字母 | |
| - `ch_dict.txt` 是一个包含6623个字符的中文字典，其中有常用的繁简体中文，数字，常用符号以及大小写的英文字母。 |
configs/rec/crnn/README.md
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| @@ -37,21 +37,25 @@ Table Format: | |||
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| According to our experiments, the evaluation results on public benchmark datasets (IC03, IC13, IC15, IIIT, SVT, SVTP, CUTE) is as follow: | |||
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| <div align="center"> | |||
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| | **Model** | **Context** | **Backbone** | **Avg Accuracy** | **Train T.** | **Recipe** | **Download** | | |||
| | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | | |||
| | CRNN (ours) | D910x8-MS1.8-G | VGG7 | 82.03% | 2445 s/epoch | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_vgg7.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_vgg7-ea7e996c.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_vgg7-ea7e996c-3a19e349.mindir) | | |||
| | CRNN (ours) | D910x8-MS1.8-G | ResNet34_vd | 84.45% | 2118 s/epoch | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_resnet34.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_resnet34-83f37f07.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_resnet34-83f37f07-2f016384.mindir) | | |||
| | CRNN (PaddleOCR) | - | ResNet34_vd | 83.99% | - | - | - | | |||
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crnn rare svtr的中英文文档都删去与paddleocr的对比。
configs/rec/crnn/README.md
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| According to our experiments, the evaluation results on public benchmark datasets (IC03, IC13, IC15, IIIT, SVT, SVTP, CUTE) is as follow: | |||
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| | **Model** | **Context** | **Backbone** | **Avg Accuracy** | **Train T.** | **Recipe** | **Download** | | |||
| | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | | |||
| | CRNN (ours) | D910x8-MS1.8-G | VGG7 | 82.03% | 2445 s/epoch | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/crnn/crnn_vgg7.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_vgg7-ea7e996c.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/crnn/crnn_vgg7-ea7e996c-3a19e349.mindir) | | |||
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删去ours，crnn rare svtr中英文文档同理。
HaoyangLee
approved these changes
May 25, 2023
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