27 lines
1.4 KiB
Markdown
27 lines
1.4 KiB
Markdown
<h1 align="center">Dataset Management</h1>
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SCEPTER supports three types of dataset formats: TXT, CSV, and ModelScope.
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Below are examples for each format, illustrating their details and basic usage.
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## Modelscope Format
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We use a [custom-stylized dataset](https://modelscope.cn/datasets/damo/style_custom_dataset/summary), which included classes 3D, anime, flat illustration, oil painting, sketch, and watercolor, each with 30 image-text pairs.
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```python
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# pip install modelscope
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from modelscope.msdatasets import MsDataset
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ms_train_dataset = MsDataset.load('style_custom_dataset', namespace='damo', subset_name='3D', split='train_short')
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print(next(iter(ms_train_dataset)))
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```
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## CSV Format
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For the data format used by SCEPTER Studio, please refer to [3D_example_csv.zip](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/3D_example_csv.zip).
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## TXT Format
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To facilitate starting training in command-line mode, you can use a dataset in text format, please refer to [3D_example_txt.zip](https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/3D_example_txt.zip)
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```shell
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mkdir -p cache/datasets/ && wget 'https://www.modelscope.cn/api/v1/models/iic/scepter/repo?Revision=master&FilePath=datasets/3D_example_txt.zip' -O cache/datasets/3D_example_txt.zip && unzip cache/datasets/3D_example_txt.zip -d cache/datasets/ && rm cache/datasets/3D_example_txt.zip
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