PatFigCLS Dataset - Patent Figure Classification Dataset

Dataset Summary The PatFigCLS dataset is introduced in the paper Patent Figure Classification using Large Vision-language Models accepted at ECIR 2025. The dataset is designed specifically for patent figure classification and evaluation across multiple aspects, including type, projection, objects and USPC class. The PatFigCLS dataset is used alongside another dataset called PatFigVQA, which is intended for fine-tuning and evaluating Large Vision-language Models (LVLMs) in few-shot learning setting for patent figure visual question answering. The dataset is sourced from two exisiting datasets: Extended CLEF-IP 2011, and DeepPatent2 Data Format The dataset is stored in .tar files for fast and efficient read access. Data Fields __key__: unique sample id image.png: patent figure file label.txt: classification label Data Splits For each classification aspect, three data splits exist: `train_150`, `val` and `test`. How to Use The recommended approach is using the Python library `webdataset`. Below is an example code. import io from PIL import Image from torchvision.transforms import Compose, ToTensor import webdataset as wds from braceexpand import braceexpand def transform(image): return Compose([ToTensor()])(image) dataset = ( wds.WebDataset( braceexpand('PatFigCLS/object/train_150/shard-{000000..000042}.tar'), shardshuffle=1000 ) .shuffle(1000) .to_tuple('__key__', 'image.png', 'label.txt') .map_tuple( lambda key: key, lambda image: transform(Image.open(io.BytesIO(image))), lambda label: label.decode('utf-8'), ) ) dataloder = wds.WebLoader(dataset) Source Code The source code used to produce this dataset can be found at https://github.com/TIBHannover/patent-figure-classification Licensing Information PatFigCLS dataset is released under GNU General Public License v3.0.数据集概览 PatFigCLS数据集由收录于ECIR 2025的论文《基于大视觉语言模型的专利图像分类》（Patent Figure Classification using Large Vision-language Models）提出。该数据集专为专利图像分类及多维度评估打造，涵盖类别、投影方式、目标对象与美国专利分类体系（United States Patent Classification, USPC）四个维度。 PatFigCLS数据集可与另一款名为PatFigVQA的数据集配合使用，后者专为少样本（few-shot）学习场景下的专利图像视觉问答任务设计，用于大视觉语言模型（Large Vision-language Models, LVLMs）的微调与评估。 该数据集源自两款现有数据集： - Extended CLEF-IP 2011 - DeepPatent2 数据格式 数据集以.tar格式存储，以实现快速高效的读取访问。 数据字段 - __key__：唯一样本标识符 - image.png：专利图像文件 - label.txt：分类标签 数据划分 针对每一个分类维度，均包含三个数据划分：`train_150`、`val`与`test`。 使用方法 推荐使用Python库`webdataset`进行加载，以下为示例代码： python import io from PIL import Image from torchvision.transforms import Compose, ToTensor import webdataset as wds from braceexpand import braceexpand def transform(image): return Compose([ToTensor()])(image) dataset = ( wds.WebDataset( braceexpand("PatFigCLS/object/train_150/shard-{000000..000042}.tar"), shardshuffle=1000 ) .shuffle(1000) .to_tuple("__key__", "image.png", "label.txt") .map_tuple( lambda key: key, lambda image: transform(Image.open(io.BytesIO(image))), lambda label: label.decode("utf-8"), ) ) dataloder = wds.WebLoader(dataset) 源代码 构建该数据集的源代码可于https://github.com/TIBHannover/patent-figure-classification获取。 授权信息 PatFigCLS数据集基于GNU通用公共许可证v3.0（GNU General Public License v3.0）发布。