19,997 machine learning datasets
19,997 dataset results
Schwerin contains handwritten texts written in medieval German. Train sample consists of 793 lines, validation - 68 lines and test - 196 lines.
Oculus Quest2 VR gaming network traffic data collected at the gaming server.
CoWeSe is a Spanish biomedical corpus consisting of 4.5GB (about 750M tokens) of clean plain text. CoWeSe is the result of a massive crawler on 3000 Spanish domains executed in 2020.
SketchHairSalon is a dataset for hair generation containing thousands of annotated hair sketch-image pairs and corresponding hair mattes.
Flowchart Grounded Dialog Dataset (FloDial) is a corpus of troubleshooting dialogs between a user and an agent collected using Amazon Mechanical Turk. The dataset is accompanied with two knowledge sources over which the dialogs are grounded: (1) a set of troubleshooting flowcharts and (2) a set of FAQs which contains supplementary information about the domain not present in the flowchart. FloDial consists of 2,738 dialogs grounded on 12 different troubleshooting flowcharts.
Description
MFAQ is a multilingual FAQ dataset publicly available. It contains around 6M FAQ pairs from the web, in 21 different languages. Although this is significantly larger than existing FAQ retrieval datasets, it comes with its own challenges: duplication of content and uneven distribution of topics.
Riedones3D is a dataset of 2,070 scans of coins. With this dataset, the authors propose two benchmarks, one for point cloud registration, essential for coin die recognition, and a benchmark of coin die clustering
This dataset is designed to enhance the progress of event-based optical flow algorithms. The data was collected using the IniVation DAViS346 camera, which has a 346 x 260 spatial resolution. The dataset is classified into camera motion data (stationary scene and moving camera) and object motion data (stationary camera and moving objects). The camera motion data contains four real indoor sequences (namely, checkerboard, classroom, conference room, and conference room translation) with ground truth motion inferred from IMU. The movement of the camera in this category was restricted by a gimbal, and the IMU was calibrated before each collection. The object motion data includes two real sequences (called hands and cars) containing multiple object motions. This category does not have ground-truth motion since the object motion cannot be inferred from IMU.
A personalized subset of Symbolic Mathematics dataset, initially introduced in the paper Deep Learning for Symbolic Mathematics (Lample et al.). We used this subset for our paper Pretrained Language Models are Symbolic Mathematics Solvers Too! (Noorbakhsh et al.).
A special corpus of Indian languages covering 13 major languages of India. It comprises of 10000+ spoken sentences/utterances each of mono and English recorded by both Male and Female native speakers. Speech waveform files are available in .wav format along with the corresponding text. We hope that these recordings will be useful for researchers and speech technologists working on synthesis and recognition. You can request zip archives of the entire database here.
FOD in Airports (FOD-A) is an image dataset of FOD, Foreign Object Degris, which consists of 31 object categories and over 30,000 annotation instances. The object categories have been selected based on guidance from prior documentation and related research by the Federal Aviation Administration (FAA).
V-HICO is a dataset for human-object interaction in videos. There are 6,594 videos, including 5,297 training videos, 635 validation videos, 608 test videos, and 54 unseen test videos, of human-object interaction. To test the performance of models on common human-object interaction classes and generalization to new human-object interaction classes, we provide two test splits, the first one has the same human-object interaction classes in the training split while the second one consists of unseen novel classes.
LA-2A Compressor data to accompany the paper "SignalTrain: Profiling Audio Compressors with Deep Neural Networks," https://arxiv.org/abs/1905.11928
Kazakh offline Handwritten Text dataset (KOHTD) has 3000 handwritten exam papers and more than 140335 segmented images and there are approximately 922010 symbols. It can serve researchers in the field of handwriting recognition tasks by using deep and machine learning.
DEIC is a benchmark for measuring the data efficiency of models in the context of image classification. It is composed of 6 datasets that contain a small number of training samples per class (i.e., 30 < x < 80). It covers multiple image domains (i.e., natural images, fine-grained recognition, medical images, remote sensing, handwriting recognition) and data types (i.e., RGB, grayscale, multi-spectral).
CNewSum is a large-scale Chinese news summarization dataset which consists of 304,307 documents and human-written summaries for the news feed. It has long documents with high-abstractive summaries, which can encourage document-level understanding and generation for current summarization models. An additional distinguishing feature of CNewSum is that its test set contains adequacy and deducibility annotations for the summaries.
The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations". The challenge addresses the growing need for reliable predictions within the boundaries of a shopping session, as customer intentions can be different depending on the occasion. The need for efficient procedures for personalization is even clearer if we consider the e-commerce landscape more broadly: outside of giant digital retailers, the constraints of the problem are stricter, due to smaller user bases and the realization that most users are not frequently returning customers. We release a new session-based dataset including more than 30M fine-grained browsing events (product detail, add, purchase), enriched by linguistic behavior (queries made by shoppers, with items clicked and items not clicked after the query) and catalog meta-data (images, text, pricing information). On this dataset, we ask participants to showcase innovative solutions fo
We labeled 7,740 webpage screenshots spanning 408 domains (Amazon, Walmart, Target, etc.). Each of these webpages contains exactly one labeled price, title, and image. All other web elements are labeled as background. On average, there are 90 web elements in a webpage.
The DeepNets-1M dataset is composed of neural network architectures represented as graphs where nodes are operations (convolution, pooling, etc.) and edges correspond to the forward pass flow of data through the network. DeepNets-1M has 1 million training architectures and 1402 in-distribution (ID) and out-of-distribution (OOD) evaluation architectures: 500 validation and 500 testing ID architectures, 100 wide OOD architectures, 100 deep OOD architectures, 100 dense OOD architectures, 100 OOD archtectures without batch normalization, and 2 predefined architectures (ResNet-50 and 12 layer Visual Transformer).