EMNIST (extended MNIST) has 4 times more data than MNIST. It is a set of handwritten digits with a 28 x 28 format.
234 PAPERS • 9 BENCHMARKS
Visible-infrared Paired Dataset for Low-light Vision 30976 images (15488 pairs) 24 dark scenes, 2 daytime scenes Support for image-to-image translation (visible to infrared, or infrared to visible), visible and infrared image fusion, low-light pedestrian detection, and infrared pedestrian detection (The original image and video pairs (before registration) of LLVIP are also released!)
49 PAPERS • 6 BENCHMARKS
Multi-Modal-CelebA-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA dataset by following CelebA-HQ. Each image has high-quality segmentation mask, sketch, descriptive text, and image with transparent background.
27 PAPERS • 3 BENCHMARKS
A dataset of 90,000 high-resolution nature landscape images, crawled from Unsplash and Flickr and preprocessed with Mask R-CNN and Inception V3.
24 PAPERS • 4 BENCHMARKS
The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. The dataset contains 4870 registered image pairs, covering a variety of HER2 expression levels (0, 1+, 2+, 3+).
10 PAPERS • 1 BENCHMARK
We introduce ArtBench-10, the first class-balanced, high-quality, cleanly annotated, and standardized dataset for benchmarking artwork generation. It comprises 60,000 images of artwork from 10 distinctive artistic styles, with 5,000 training images and 1,000 testing images per style. ArtBench-10 has several advantages over previous artwork datasets. Firstly, it is class-balanced while most previous artwork datasets suffer from the long tail class distributions. Secondly, the images are of high quality with clean annotations. Thirdly, ArtBench-10 is created with standardized data collection, annotation, filtering, and preprocessing procedures. We provide three versions of the dataset with different resolutions (32×32, 256×256, and original image size), formatted in a way that is easy to be incorporated by popular machine learning frameworks.
7 PAPERS • 1 BENCHMARK
WHOOPS! Is a dataset and benchmark for visual commonsense. The dataset is comprised of purposefully commonsense-defying images created by designers using publicly-available image generation tools like Midjourney. It contains commonsense-defying image from a wide range of reasons, deviations from expected social norms and everyday knowledge.
2 PAPERS • 4 BENCHMARKS
Dataset information (e.g., google drive link) is attached in the GitHub repo: https://github.com/YY-GX/Annotated-Hands-Dataset
1 PAPER • NO BENCHMARKS YET
ENTIGEN is a benchmark dataset to evaluate the change in image generations conditional on ethical interventions across three social axes -- gender, skin color, and culture. It contains 246 prompts based on an attribute set containing diverse professions, objects, and cultural scenarios.
MatSynth MatSynth is a Physically Based Rendering (PBR) materials dataset designed for modern AI applications. This dataset consists of over 4,000 ultra-high resolution, offering unparalleled scale, diversity, and detail.
A high-resolution version of VGGFace2 for academic face editing purposes. This project uses GFPGAN for image restoration and insightface for data preprocessing (crop and align).
This dataset is the images of corn seeds considering the top and bottom view independently (two images for one corn seed: top and bottom). There are four classes of the corn seed (Broken-B, Discolored-D, Silkcut-S, and Pure-P) 17802 images are labeled by the experts at the AdTech Corp. and 26K images were unlabeled out of which 9k images were labeled using the Active Learning (BatchBALD)
0 PAPER • NO BENCHMARKS YET
Lemon dataset has been prepared to investigate the possibilities to tackle the issue of fruit quality control. It contains 2690 annotated images (1056 x 1056 pixels). Raw lemon images have been captured using the procedure described in the following blogpost and manually annotated using CVAT.