3,275 machine learning datasets
3,275 dataset results
If you want to known more about this dataset and new method, please read our paper link.
The availability of well-curated datasets has driven the success of Machine Learning (ML) models. Despite greater access to earth observation data in agriculture, there is a scarcity of curated and labelled datasets, which limits the potential of its use in training ML models for remote sensing (RS) in agriculture. To this end, we introduce a first-of-its-kind dataset called SICKLE, which constitutes a time-series of multi-resolution imagery from 3 distinct satellites: Landsat-8, Sentinel-1 and Sentinel-2. Our dataset constitutes multi-spectral, thermal and microwave sensors during January 2018 - March 2021 period. We construct each temporal sequence by considering the cropping practices followed by farmers primarily engaged in paddy cultivation in the Cauvery Delta region of Tamil Nadu, India; and annotate the corresponding imagery with key cropping parameters at multiple resolutions (i.e. 3m, 10m and 30m). Our dataset comprises 2, 370 season-wise samples from 388 unique plots, having
A novel remote sensing dataset for evaluating a geospatial machine learning model's ability to learn long range dependencies and spatial context understanding. We create a task to use as a proxy for this by training models to extract roads which have been broken into disjoint pieces due to tree canopy occluding large portions of the road.
An object-centric version of Stylized COCO to benchmark texture bias and out-of-distribution robustness of vision models. See the ECCV 22 paper and supplementary material for details.
An expert benchmark aiming to comprehensively evaluate the aesthetic perception capacities of MLLMs.
Our dataset augments the TAO dataset with amodal bounding box annotations for fully invisible, out-of-frame, and occluded objects. Note that this implies TAO-Amodal also includes modal segmentation masks (as visualized in the color overlays above). Our dataset encompasses 880 categories, aimed at assessing the occlusion reasoning capabilities of current trackers through the paradigm of Tracking Any Object with Amodal perception (TAO-Amodal).
We provide all the expected data inputs to GUISS such as meshes, texture images, and blend files. Generated datasets used in our experiments along with the stereo depth estimations can be downloaded. We have defined seven dataset types: scene_reconstructions, texture_variation, gaea_texture_variation, generative_texture, terrain_variation, rocks, and generative_texture_snow. Each dataset type contains renderings with varying values of different parameters such as lighting angle, texture imgs, albedo, etc. Position each dataset type folder under data/dataset/.
Super-CLEVR-3D is a visual question answering (VQA) dataset where the questions are about the explicit 3D configuration of the objects from images (i.e. 3D poses, parts, and occlusion). It consists of objects from 5 categories: aeroplanes, buses, bicycles, cars and motorbikes. The rendered objects are from CGParts dataset, with the same setting as Super-CLEVR dataset.
Understanding comprehensive assembly knowledge from videos is critical for futuristic ultra-intelligent industry. To enable technological breakthrough, we present HA-ViD – an assembly video dataset that features representative industrial assembly scenarios, natural procedural knowledge acquisition process, and consistent human-robot shared annotations. Specifically, HA-ViD captures diverse collaboration patterns of real-world assembly, natural human behaviors and learning progression during assembly, and granulate action annotations to subject, action verb, manipulated object, target object, and tool. We provide 3222 multi-view and multi-modality videos, 1.5M frames, 96K temporal labels and 2M spatial labels. We benchmark four foundational video understanding tasks: action recognition, action segmentation, object detection and multi-object tracking. Importantly, we analyze their performance and the further reasoning steps for comprehending knowledge in assembly progress, process effici
The Instance Segmentation task, an extension of the well-known Object Detection task, is of great help in many areas, such as precision agriculture: being able to automatically identify plant organs and the possible diseases associated with them, allows to effectively scale and automate crop monitoring and its diseases control.
We filter and match the landmarks in the Google Landmarks dataset with their OpenStreetMap polygons and filter for those located in the United States, resulting in 602 landmarks. Then, we obtain the latest high-resolution aerial images of the obtained polygons through the National Agriculture Imagery Program (NAIP) of the United States Department of Agriculture (USDA). Finally, we construct multiple-choice questions about the name of the landmark with incorrect answers from other landmarks in the same category.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Multi-Modal Hate Speech Detection with Graph Context.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
CNFOOD-241 Contains a dataset of 241 Chinese dishes with 191,811 images. There are 170843 images in the training set and 20943 images in the validation set. All images are resized to 600x600. As some of the images in the dataset are from ChineseFoodNet, they are not supported for commercial use. CNFOOD-241-Chen is the CNFOOD-241 dataset spilt with the list introduced in the paper "Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning," which has random split as train, val, test three parts.
Contains a dataset of 241 Chinese dishes with 191,811 images. There are 170843 images in the training set and 20943 images in the validation set. All images are resized to 600x600. As some of the images in the dataset are from ChineseFoodNet, they are not supported for commercial use.
A total of 227 cross sectional images (20 x 54 mm with a resolution of 289 x 648 pixels) of hind-leg xenograft tumors from 29 mice were obtained with 1mm step-wise movement of the array mounted on a manual positioning device. The whole tumor volume was acquired using a diagnostic ultrasound system with a 10 MHz linear transducer and 50 MHz sampling.
The Satellite dataset forms a practical VFL scenario for location identification based on satellite imagery. Each AOI, with its unique location identifier, is captured by 16 satellite visits. Assuming each visit is carried out by a distinct satellite organization, these organizations aim to collectively train a model to classify the land type of the location without sharing original images. The Satellite dataset encompasses four land types as labels, namely Amnesty POI (4.8%), ASMSpotter (8.9%), Landcover (61.3%), and UNHCR (25.0%), making the task a 4-class classification problem of 3927 locations, containing 62,832 images across 16 parties, simulating a practical VFL scenario of collaborative location identification via multiple satellites.
Description This Dataset contains review information on Google map (ratings, text, images, etc.), business metadata (address, geographical info, descriptions, category information, price, open hours, and MISC info), and links (relative businesses) up to Sep 2021 in the United States.