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Jun 29, 2023
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Add RARE MindIR #448

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8 changes: 5 additions & 3 deletions configs/rec/rare/README.md
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Expand Up @@ -37,7 +37,7 @@ According to our experiments, the evaluation results on public benchmark dataset

| **Model** | **Context** | **Backbone** | **Transform Module** | **Avg Accuracy** | **Train T.** | **FPS** | **Recipe** | **Download** |
| :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :--------: |:-----: |
| RARE | D910x4-MS1.10-G | ResNet34_vd | None | 85.19% | 3166 s/epoch | 4561 | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/rare/rare_resnet34.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34-309dc63e.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34-309dc63e-b65dd225.mindir) |
| RARE | D910x4-MS1.10-G | ResNet34_vd | None | 85.19% | 3166 s/epoch | 4561 | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/rare/rare_resnet34.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34-309dc63e.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34_ascend-309dc63e-b96c2a4b.mindir) |
</div>

<details open markdown>
Expand All @@ -55,7 +55,7 @@ According to our experiments, the evaluation results on public benchmark dataset
- To reproduce the result on other contexts, please ensure the global batch size is the same.
- The characters supported by model are lowercase English characters from a to z and numbers from 0 to 9. More explanation on dictionary, please refer to [4. Character Dictionary](#4-character-dictionary).
- The models are trained from scratch without any pre-training. For more dataset details of training and evaluation, please refer to [Dataset Download & Dataset Usage](#312-dataset-download) section.
- The input Shapes of MindIR of RARE is (1, 3, 32, 100).
- The input Shapes of MindIR of RARE is (1, 3, 32, 100) and it is for Ascend only.

## 3. Quick Start
### 3.1 Preparation
Expand Down Expand Up @@ -350,9 +350,11 @@ After training, evaluation results on the benchmark test set are as follows, whe

| **Model** | **Language** | **Backbone** | **Transform Module** | **Scene** | **Web** | **Document** | **Train T.** | **FPS** | **Recipe** | **Download** |
| :-----: | :-----: | :--------: | :------------: | :--------: | :--------: | :--------: | :--------: | :--------: |:---------: | :-----------: |
| RARE | Chinese | ResNet34_vd | None | 62.15% | 67.05% | 97.60% | 414 s/epoch | 2160 | [rare_resnet34_ch.yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/rare/rare_resnet34_ch.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34_ch-5f3023e2.ckpt) \| [mindir]() |
| RARE | Chinese | ResNet34_vd | None | 62.15% | 67.05% | 97.60% | 414 s/epoch | 2160 | [rare_resnet34_ch.yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/rare/rare_resnet34_ch.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34_ch-5f3023e2.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34_ch_ascend-5f3023e2-11f0d554.mindir) |
</div>

- The input Shapes of MindIR of RARE is (1, 3, 32, 320) and it is for Ascend only.

### Training with Custom Datasets
You can train models for different languages with your own custom datasets. Loading the pretrained Chinese model to finetune on your own dataset usually yields better results than training from scratch. Please refer to the tutorial [Training Recognition Network with Custom Datasets](../../../docs/en/tutorials/training_recognition_custom_dataset.md).

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8 changes: 5 additions & 3 deletions configs/rec/rare/README_CN.md
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Expand Up @@ -38,7 +38,7 @@ Table Format:

| **模型** | **环境配置** | **骨干网络** | **空间变换网络** | **平均准确率** | **训练时间** | **FPS** | **配置文件** | **模型权重下载** |
| :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :--------: |:-----: |
| RARE | D910x4-MS1.10-G | ResNet34_vd | 无 | 85.19% | 3166 s/epoch | 4561 | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/rare/rare_resnet34.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34-309dc63e.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34-309dc63e-b65dd225.mindir) |
| RARE | D910x4-MS1.10-G | ResNet34_vd | 无 | 85.19% | 3166 s/epoch | 4561 | [yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/rare/rare_resnet34.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34-309dc63e.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34_ascend-309dc63e-b96c2a4b.mindir) |
</div>

<details open markdown>
Expand All @@ -56,7 +56,7 @@ Table Format:
- 如需在其他环境配置重现训练结果，请确保全局批量大小与原配置文件保持一致。
- 模型所能识别的字符都是默认的设置，即所有英文小写字母a至z及数字0至9，详细请看[4. 字符词典](#4-字符词典)
- 模型都是从头开始训练的，无需任何预训练。关于训练和测试数据集的详细介绍，请参考[数据集下载及使用](#312-数据集下载)章节。
- RARE的MindIR导出时的输入Shape均为(1, 3, 32, 100)。
- RARE的MindIR导出时的输入Shape均为(1, 3, 32, 100)，只能在昇腾卡上使用。

## 3. 快速开始
### 3.1 环境及数据准备
Expand Down Expand Up @@ -355,9 +355,11 @@ mpirun --allow-run-as-root -n 8 python tools/train.py --config configs/rec/rare/

| **模型** | **语种** | **骨干网络** | **空间变换网络** | **街景类** | **网页类** | **文档类** | **训练时间** | **FPS** | **配置文件** | **模型权重下载** |
| :-----: | :-----: | :--------: | :------------: | :--------: | :--------: | :--------: |:--------: | :--------: |:--------: | :--------: |
| RARE | 中文 | ResNet34_vd | 无 | 62.15% | 67.05% | 97.60% | 414 s/epoch | 2160 | [rare_resnet34_ch.yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/rare/rare_resnet34_ch.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34_ch-5f3023e2.ckpt) \| [mindir]() |
| RARE | 中文 | ResNet34_vd | 无 | 62.15% | 67.05% | 97.60% | 414 s/epoch | 2160 | [rare_resnet34_ch.yaml](https://github.com/mindspore-lab/mindocr/blob/main/configs/rec/rare/rare_resnet34_ch.yaml) | [ckpt](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34_ch-5f3023e2.ckpt) \| [mindir](https://download.mindspore.cn/toolkits/mindocr/rare/rare_resnet34_ch_ascend-5f3023e2-11f0d554.mindir) |
</div>

- RARE的MindIR导出时的输入Shape均为(1, 3, 32, 320)，只能在昇腾卡上使用。

### 使用自定义数据集进行训练
您可以在自定义的数据集基于提供的预训练权重进行微调训练, 以在特定场景获得更高的识别准确率，具体步骤请参考文档 [使用自定义数据集训练识别网络](../../../docs/cn/tutorials/training_recognition_custom_dataset_CN.md)。

Expand Down