--- annotations_creators: - no-annotation language_creators: - found language: - en license: cdla-permissive-2.0 multilinguality: - monolingual size_categories: - 10K, , , , , , , ]} ``` To get a label for categorical values, use the `int2str` method: ```python data = dataset['train'] # obtain the train set key = "font" example = data[0] # obtain first sample in train set data.features[key].feature.int2str(example[key]) # obtain the text equivalent of the encoded values ``` ### Data Fields In the following, categorical fields are shown as `categorical` type, but the actual storage is `int64`. **Canvas attributes** | Field | Type | Shape | Description | | ------------- | ----------- | ------- | ----------------------------------------------------------------- | | id | string | () | Template ID from crello.com | | group | categorical | () | Broad design groups, such as social media posts or blog headers | | format | categorical | () | Detailed design formats, such as Instagram post or postcard | | category | categorical | () | Topic category of the design, such as holiday celebration | | canvas_width | categorical | () | Canvas pixel width | | canvas_height | categorical | () | Canvas pixel height | | length | int64 | () | Length of elements | | suitability | categorical | (None,) | List of display tags, only `mobile` tag exists | | keywords | string | (None,) | List of keywords associated to this template | | industries | categorical | (None,) | List of industry tags like `marketingAds` | | preview | image | () | Preview image of the template for convenience; only for debugging | | cluster_index | int64 | () | Cluster index used to split the dataset; only for debugging | **Element attributes** | Field | Type | Shape | Description | | -------------- | ----------- | --------- | -------------------------------------------------------------------- | | type | categorical | (None,) | Element type, such as vector shape, image, or text | | left | float32 | (None,) | Element left position normalized to [0, 1] range w.r.t. canvas_width | | top | float32 | (None,) | Element top position normalized to [0, 1] range w.r.t. canvas_height | | width | float32 | (None,) | Element width normalized to [0, 1] range w.r.t. canvas_width | | height | float32 | (None,) | Element height normalized to [0, 1] range w.r.t. canvas_height | | color | int64 | (None, 3) | Extracted main RGB color of the element | | opacity | float32 | (None,) | Opacity in [0, 1] range | | image | image | (None,) | Pre-rendered 256x256 preview of the element encoded in PNG format | | text | string | (None,) | Text content in UTF-8 encoding for text element | | font | categorical | (None,) | Font family name for text element | | font_size | float32 | (None,) | Font size (height) in pixels | | text_align | categorical | (None,) | Horizontal text alignment, left, center, right for text element | | angle | float32 | (None,) | Element rotation angle (radian) w.r.t. the center of the element | | capitalize | categorical | (None,) | Binary flag to capitalize letters | | line_height | float32 | (None,) | Scaling parameter to line height, default is 1.0 | | letter_spacing | float32 | (None,) | Adjustment parameter for letter spacing, default is 0.0 | Note that the color and pre-rendered images do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available. ``` https://create.vista.com/artboard/?template= ``` `left` and `top` can be negative because elements can be bigger than the canvas size. ### Data Splits The Crello dataset has 3 splits: train, validation, and test. The current split is generated based on appearance-based clustering. | Split | Count | | --------- | ----- | | train | 19095 | | validaton | 1951 | | test | 2375 | ### Visualization Each example can be visualized in the following approach using [`skia-python`](https://kyamagu.github.io/skia-python/). Note the following does not guarantee a similar appearance to the original template. Currently, the quality of text rendering is far from perfect. ```python import io from typing import Any, Dict import numpy as np import skia def render(features: datasets.Features, example: Dict[str, Any], max_size: float=512.) -> bytes: """Render parsed sequence example onto an image and return as PNG bytes.""" canvas_width = int(features["canvas_width"].int2str(example["canvas_width"])) canvas_height = int(features["canvas_height"].int2str(example["canvas_height"])) scale = min(1.0, max_size / canvas_width, max_size / canvas_height) surface = skia.Surface(int(scale * canvas_width), int(scale * canvas_height)) with surface as canvas: canvas.scale(scale, scale) for index in range(example["length"]): pil_image = example["image"][index] image = skia.Image.frombytes( pil_image.convert('RGBA').tobytes(), pil_image.size, skia.kRGBA_8888_ColorType) left = example["left"][index] * canvas_width top = example["top"][index] * canvas_height width = example["width"][index] * canvas_width height = example["height"][index] * canvas_height rect = skia.Rect.MakeXYWH(left, top, width, height) paint = skia.Paint(Alphaf=example["opacity"][index], AntiAlias=True) angle = example["angle"][index] with skia.AutoCanvasRestore(canvas): if angle != 0: degree = 180. * angle / np.pi canvas.rotate(degree, left + width / 2., top + height / 2.) canvas.drawImageRect(image, rect, paint=paint) image = surface.makeImageSnapshot() with io.BytesIO() as f: image.save(f, skia.kPNG) return f.getvalue() ``` ## Dataset Creation ### Curation Rationale The Crello dataset is compiled for the general study of vector graphic documents, with the goal of producing a dataset that offers complete vector graphic information suitable for neural methodologies. ### Source Data #### Initial Data Collection and Normalization The dataset is initially scraped from the former `crello.com` and pre-processed to the above format. #### Who are the source language producers? While [create.vista.com](https://create.vista.com/) owns those templates, the templates seem to be originally created by a specific group of design studios. ### Personal and Sensitive Information The dataset does not contain any personal information about the creator but may contain a picture of people in the design template. ## Considerations for Using the Data ### Social Impact of Dataset This dataset was developed for advancing the general study of vector graphic documents, especially for generative systems of graphic design. Successful utilization might enable the automation of creative workflow that human designers get involved in. ### Discussion of Biases The templates contained in the dataset reflect the biases appearing in the source data, which could present gender biases in specific design categories. ### Other Known Limitations Due to the unknown data specification of the source data, the color and pre-rendered images do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available. https://create.vista.com/artboard/?template= ## Additional Information ### Dataset Curators The Crello dataset was developed by [Kota Yamaguchi](https://github.com/kyamagu). ### Licensing Information The origin of the dataset is [create.vista.com](https://create.vista.com) (formally, `crello.com`). The distributor ("We") do not own the copyrights of the original design templates. By using the Crello dataset, the user of this dataset ("You") must agree to the [VistaCreate License Agreements](https://create.vista.com/faq/legal/licensing/license_agreements/). The dataset is distributed under [CDLA-Permissive-2.0 license](https://cdla.dev/permissive-2-0/). **Note** We do not re-distribute the original files as we are not allowed by terms. ### Citation Information @article{yamaguchi2021canvasvae, title={CanvasVAE: Learning to Generate Vector Graphic Documents}, author={Yamaguchi, Kota}, journal={ICCV}, year={2021} } ### Releases 4.0.0: v4 release (Dec 5, 2023) - Change the dataset split based on the template appearance to avoid near-duplicates: no compatibility with v3. - Class labels have been reordered: no compabilitity with v3. - Small improvement to font rendering. 3.1: bugfix release (Feb 16, 2023) - Fix a bug that ignores newline characters in some of the texts. 3.0: v3 release (Feb 13, 2023) - Migrate to Hugging Face Hub. - Fix various text rendering bugs. - Change split generation criteria for avoiding near-duplicates: no compatibility with v2 splits. - Incorporate a motion picture thumbnail in templates. - Add `title`, `keywords`, `suitability`, and `industries` canvas attributes. - Add `capitalize`, `line_height`, and `letter_spacing` element attributes. 2.0: v2 release (May 26, 2022) - Add `text`, `font`, `font_size`, `text_align`, and `angle` element attributes. - Include rendered text element in `image_bytes`. 1.0: v1 release (Aug 24, 2021) ### Contributions Thanks to [@kyamagu](https://github.com/kyamagu) for adding this dataset.