19,997 machine learning datasets
19,997 dataset results
SPRIGHT is the first, large-scale vision-language dataset that focuses on spatial relationships. It contains ~6M images that have been re-captioned with a synthetic focus.
The HRPlanesv2 dataset contains 2120 VHR Google Earth images. To further improve experiment results, images of airports from many different regions with various uses (civil/military/joint) selected and labeled. A total of 14,335 aircrafts have been labelled. Each image is stored as a ".jpg" file of size 4800 x 2703 pixels and each label is stored as YOLO ".txt" format. Dataset has been split in three parts as 70% train, %20 validation and test. The aircrafts in the images in the train and validation datasets have a percentage of 80 or more in size. Link: https://github.com/dilsadunsal/HRPlanesv2-Data-Set
The GDIT Aerial Airport dataset consists of aerial images containing instances of parked airplanes. All plane types have been grouped into a single classification named "airplane".
The LM-O (Linemod-Occluded) dataset, introduced by Brachmann et al. in their work on 6D object pose estimation, provides additional ground-truth annotations for all modeled objects in one of the test sets from the Linemod (LM) dataset. This extension introduces challenging test cases with various levels of occlusion ¹²³.
Diffusion generated image dataset
Radiology Objects in COntext (ROCO): A Multimodal Image Dataset
A large scale, C2C marketplace e-commerce dataset.
This dataset contains both the artificial and real flower images of bramble flowers. The real images were taken with a realsense D435 camera inside the West Virginia University greenhouse. All the flowers are annotated in YOLO format with bounding box and class name. The trained weights after training also have been provided. They can be used with the python script provided to detect the bramble flowers. Also the classifier can classify whether the flowers center is visible or hidden which will be helpful in precision pollination projects. Images are also augmented to make the task robust in various environmental conditions.
3D design file repository for the Stickbug Robot a 6 armed holonomic precision pollination robot
Demonstration video of the Stickbug Robot
This dataset is based on the movie review polarity dataset (v2.0) collected and maintained by Bo Pang and Lillian Lee. Their dataset (we'll call it PL2.0) consists of 1000 positive and 1000 negative movie reviews obtained from the Internet Movie Database (IMDb) review archive.
"My ridiculous dog is amazing." [sentiment: positive]
Large collection of accumulated shadow tiles for over 100 cities (in 6 continents), computed using Deep Umbra. The complete dataset contains 999,807 map tiles, totaling over 16 GB. The selected cities cover six continents and have different building morphologies and urban street networks. We hope that the data will provide new opportunities for urban experts to perform not only fine-grained analyses (i.e., what is the best location for a certain facility?) but also large-scale comparisons and exploratory analyses across neighborhoods or cities (e.g., what is the park with least shadow amount? or what is the city with least shadow amount?). The dataset currently contains accumulated shadow information, but the accumulated sunlight can be obtained by simply computing the complement of the accumulated shadow value. The dataset and web viewer can be accessed at urbantk.org/shadows.
The VlogQA consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from YouTube - an extensive source of user-uploaded content, covering the topics of food and travel in the Vietnamese language. This dataset is used for research in Vietnamese Spoken-Based Machine Reading Comprehension.
Paper: GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery
The $\text{BEAR}$ dataset and its larger version, $\text{BEAR}_{\text{big}}$, are benchmarks for evaluating common factual knowledge contained in language models.
The dataset contains the training and test data for the SOftware Mention Detection challenge. The data is derived from the SoMeSci Knowledge Graph of software mentions.
WikiEvalFacts This is an augmented version of the WikiEval dataset which additionally includes generated fact statements for each QA pair and human annotation of their truthfulness against each answer type.
WikiEval Dataset for to do correlation analysis of difference metrics proposed in Ragas This dataset was generated from 50 pages from Wikipedia with edits post 2022.
This dataset labeled by SAR experts was created using 102 Chinese Gaofen-3 images and 108 Sentinel-1 images. It consists of 39,729 ship chips(remove some repeat clips) of 256 pixels in both range and azimuth. These ships mainly have distinct scales and backgrounds. It can be used to develop object detectors for multi-scale and small object detection. The details of this dataset is referred to "Wang, Yuanyuan, Chao Wang, Hong Zhang, Yingbo Dong, and Sisi Wei. 2019. "A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds." Remote Sensing 11 (7). doi: 10.3390/rs11070765."