PatFigCLS Dataset - Patent Figure Classification Dataset Dataset uri icon

abstract

  • 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.

publication date

  • 2025