19,997 machine learning datasets
19,997 dataset results
McQueen dataset contains 15k visual conversations and over 80k queries where each one is associated with a fully-specified rewrite version. In addition, for entities appearing in the rewrite, the corresponding image box annotation is provided.
CoreSearch is a dataset for Cross-Document Event Coreference Search. It consists of two separate passage collections: (1) a collection of passages containing manually annotated coreferring event mention, and (2) an annotated collection of destructor passages.
VD-Ref is a dataset with ground-truth mappings from both noun phrases and pronouns to image regions. This dataset contains a set of 10k complete sets from the VisDialog dataset, and uses the StanfordCoreNLP tool to tokenize the sentences, making it proper for the succeeding human annotation.
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Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Korean Multi-label Hate Speech Dataset
SF-MASK is a collection made from 20k low-resolution images exported from diverse and heterogeneous datasets, ranging from 7 x 7 to 64 x 64 pixel resolution. An accurate visualization of this collection, through counting grids, made it possible to highlight gaps in the variety of poses assumed by the heads of the pedestrians.
DiscoSense is a benchmark sourced from datasets that contain two sentences connected through a discourse connective. Specifically, it is sourced from two peer reviewed academic datasets, DISCOVERY and DISCOFUSE for commonsense reasoning via understanding a wide variety of discourse connectives.
YouwikiHow is a dataset for Weakly-Supervised temporal Article Grounding (WSAG). It contains 47K videos and an average of 20.8 query sentences for each video.
Reddit Engagement Dataset (RED), a distant-supervision set, with 80k single-turn conversations. RED is sourced from Reddit, sampling from 43 popular subreddits, and processed from a total of 5 million posts, filtering out data that was either non-conversational, toxic, or posts not possible to ascertain popularity.
MultiRefKGC is a dataset created from conversations from Reddit designed for Knowledge-Grounded Dialogue Generation tasks.
This dataset provides a collection of 162K images and 70 Videos of Meta-Humans. There are 10 Highly realistic Meta-Humans expressing 7 facial expressions.
We introduce GLAMI-1M: the largest multilingual image-text classification dataset and benchmark. The dataset contains images of fashion products with item descriptions, each in 1 of 13 languages. Categorization into 191 classes has high-quality annotations: all 100k images in the test set and 75% of the 1M training set were human-labeled. The paper presents baselines for image-text classification showing that the dataset presents a challenging fine-grained classification problem: The best scoring EmbraceNet model using both visual and textual features achieves 69.7% accuracy. Experiments with a modified Imagen model show the dataset is also suitable for image generation conditioned on text.
MUSIED is a large-scale Chinese event detection dataset based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service, designed for event detection tasks.
The 42Street dataset is based on a theater play as an example of such an application. The dataset is created using a public recording of the 42Street theatre play [42street]. The play is 1.5 hours long and was split into 5 equally long parts of 20 minutes each, with various clothes changes between the different parts.
SeaDronesSee-Object Detection v2 (S-ODv2) dataset contains 14,227 RGB images (training: 8,930; validation: 1,547; testing: 3,750). The images are captured from various altitudes and viewing angles ranging from 5 to 260 meters and 0 to 90° degrees (gimbal pitch angle) while providing the respective meta information for altitude, viewing angle and other meta data for almost all frames.
KD-EmoR is socio-behavioral emotion dataset for emotion recognition in realistic conversation scenarios. It consists of total 12289 sentences from 1513 scenes of a Korean TV show named 'Three Brothers'. The dataset is split into Training and testing sets. Each sample consists of sentence_id, person(speaker), sentence, scene_ID, context(Scene description) labeled with one of the following complex emotion labels: euphoria, dysphoria and neutral. This dataset can be used to study Emotion recognition in Korean conversations.
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The raw .mat data collected in two different scenarios are provided.
Data collection: Finding a suitable source of data is considered a first step toward building a database. The first step in building a database is finding a suitable source. Here, the main goal is to collect images of Kurdish handwritten characters written by many writers. So, a form is designed to do so. The form is shown in Figure 1. It consists of 1 alphabet at a time letter that has been printed on the top right corner, and it has 125 empty blocks. The writers have been asked to write each letter three times in the three empty blocks. The total number of writers is 390. The forms have been distributed among two main categories: The academic staff of the Information Technology department at Tishk International University, the university students of the University of Kurdistan-Hawler, Salahaddin University, and Tishk International University As shown in Table 2. In total there were ten sets of forms, each set with 35 forms for 35 different letters, at first, we decided that nine sets