3,275 machine learning datasets
3,275 dataset results
The MSRA-B dataset is a dataset for salient object detection. It contains 5,000 images with a variety of image contents. Most of the images have a single salient object. There is a large variation among images including natural scenes, animals, indoor, outdoor, etc.
Placepedia contains 240K places with 35M images from all over the world. Each place is associated with its district, city/town/village, state/province, country, continent, and a large amount of diverse photos. Both administrative areas and places have rich side information, e.g. discription, population, category, function. In addition, two cleaned subsets (Places-Coarse and Places-Fine) for experiments are provided.
A large-scale hand pose dataset, collected using a novel capture method.
The dataset has 93 image stacks and their corresponding Extended Depth of Field (EDF) image acquired from cases with grades Nagative, LSIL or HSIL (The Bethesda System): - Negative: 16 - LSIL: 46 - HSIL: 31 The ground truth includes the grade labels for each frame and manually marked points inside cervical cells in each frame. There are in total 2705 manually marked points inside all frames: - Negative: 238 - LSIL: 1536 - HSIL: 931
The Chinese Traditional Painting dataset for style transfer contains 1000 content images and 100 style images. The content images are mostly the photorealistic scenes of mountain, lake, river, bridge, and buildings in regions south of the Yangtze River. It includes not only the scenes of China, but also beautiful pictures of Rhine, Alps, Yellow Stone, Grand Canyon, etc. The content images include diverse types of Chinese traditional paintings.
Contains more than 5,000 images of 10,000 liquid containers in context labelled with volume, amount of content, bounding box annotation, and corresponding similar 3D CAD models.
The COVID-19 Posteroanterior Chest X-Ray fused (CPCXR) dataset is generated by the fusion of three publicly available datasets: COVID-19 cxr image, Radiological Society of North America (RSNA), and U.S. national library of medicine (USNLM) collected Montgomery country - NLM(MC). The dataset consists of samples of diseases labeled as COVID-19, Tuberculosis, Other pneumonia (SARS, MERS, etc.), and Normal. The dataset can be utilized to train an evaulate deep learning and machine learning models as binary and multi-class classification problem.
CUHK-QA is a dataset for natural language-based person search using iterative questioning.
The Curated AFD dataset is a curated version of the Asian Face Dataset (AFD) for face recognition research. The original AFD dataset has been curated to remove wrong identity labels, duplicate images and duplicate subjects.
This dataset contains videos where a flying drone (hexacopter) is captured with multiple consumer-grade cameras (smartphones, compact cameras, gopro,...) with highly accurate 3D drone trajectory ground truth recorderd by a precise real-time RTK system from Fixposition. In some videos, the ground truth temporal synchronization and ground truth camera locations are also provided.
Edge-Map-345C is a large-scale edge-map dataset including 290,281 edge-maps corresponding to 345 object categories of QuickDraw dataset. In particular, these 345 categories are corresponding to the 345 free-hand sketch categories of Google QuickDraw dataset.
The database is comprised of two datasets, the 4-Class eTRIMS Dataset with 4 annotated object classes and the 8-Class eTRIMS Dataset with 8 annotated object classes.
FAS100K is a large-scale visual localization dataset. This dataset is comprised of two traverses of 238 and 130 kms respectively where the latter is a partial repeat of the former. The data was collected using stereo cameras in Australia under sunny day conditions. It covers a variety of road and environment types including urban and rural areas. The raw image data from one of the cameras streaming at 5 Hz constitutes 63,650 and 34,497 image frames for the two traverses respectively.
The FeatherV1 dataset is a dataset for fine-grained visual classification. It contains 28,272 images of feathers categorized by 595 bird species.
This is a dataset of 9428 images, 1754 of which are target images with memorability scores. The images span 21 scene categories from the SUN database. Each scene category was chosen to contain at least 300 images of size 700x700 or greater. All images were cropped to 700x700 pixels.
FSOCO is a collaborative dataset for vision-based cone detection systems in Formula Student Driverless competitions. It contains human annotated ground truth labels for both bounding boxes and instance-wise segmentation masks. The data buy-in philosophy of FSOCO asks student teams to contribute to the database first before being granted access ensuring continuous growth. By providing clear labeling guidelines and tools for a sophisticated raw image selection, new annotations are guaranteed to meet the desired quality.
A genomics dataset for OOD detection that allows other researchers to benchmark progress on this important problem.
The Generix Object Zero-shot Learning (GOZ) dataset is a benchmark dataset for zero-shot learning.
A new dataset containing over 550K pairs (covering 143 km^2 area) of RGB and aerial LIDAR depth images.
A large-scale grasp pose detection dataset with a unified evaluation system. The dataset contains 87,040 RGBD images with over 370 million grasp poses.