DFEW: A large-scale database for recognizing dynamic facial expressions in the wild.

Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities and 4,601,223 tokens. Three benchmark tasks are built, one is supervised: Few-NERD (SUP) and the other two are few-shot: Few-NERD (INTRA) and Few-NERD (INTER).

Test Drew

## Overview

Test Drew is a dataset for object detection tasks - it contains Cars annotations for 1,520 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Citation

@article{DBLP:journals/corr/abs-2101-04775, author = {Bingchen Liu and Yizhe Zhu and Kunpeng Song and Ahmed Elgammal}, title = {Towards Faster and Stabilized {GAN} Training for High-fidelity Few-shot Image Synthesis}, journal = {CoRR}, volume = {abs/2101.04775}, year = {2021}, url = {https://arxiv.org/abs/2101.04775}, eprinttype = {arXiv}, eprint = {2101.04775}, timestamp… See the full description on the dataset page: https://huggingface.co/datasets/huggan/few-shot-anime-face.

gokulg02/DFW dataset hosted on Hugging Face and contributed by the HF Datasets community

Object recognition predominately still relies on many high-quality training examples per object category. In contrast, learning new objects from only a few examples could enable many impactful applications from robotics to user personalization. Most few-shot learning research, however, has been driven by benchmark datasets that lack the high variation that these applications will face when deployed in the real-world. To close this gap, we present the ORBIT dataset, grounded in a real-world application of teachable object recognizers for people who are blind/low vision. We provide a full, unfiltered dataset of 4,733 videos of 588 objects recorded by 97 people who are blind/low-vision on their mobile phones, and a benchmark dataset of 3,822 videos of 486 objects collected by 77 collectors. The code for loading the dataset, computing all benchmark metrics, and running the baseline models is available at https://github.com/microsoft/ORBIT-DatasetThis version comprises several zip files:- train, validation, test: benchmark dataset, organised by collector, with raw videos split into static individual frames in jpg format at 30FPS- other: data not in the benchmark set, organised by collector, with raw videos split into static individual frames in jpg format at 30FPS (please note that the train, validation, test, and other files make up the unfiltered dataset)- *_224: as for the benchmark, but static individual frames are scaled down to 224 pixels.- *_unfiltered_videos: full unfiltered dataset, organised by collector, in mp4 format.

Room-B

Citation

@article{DBLP:journals/corr/abs-2101-04775, author = {Bingchen Liu and Yizhe Zhu and Kunpeng Song and Ahmed Elgammal}, title = {Towards Faster and Stabilized {GAN} Training for High-fidelity Few-shot Image Synthesis}, journal = {CoRR}, volume = {abs/2101.04775}, year = {2021}, url = {https://arxiv.org/abs/2101.04775}, eprinttype = {arXiv}, eprint = {2101.04775}, timestamp… See the full description on the dataset page: https://huggingface.co/datasets/huggan/few-shot-obama.

quidangz/uid-data-few-shot dataset hosted on Hugging Face and contributed by the HF Datasets community