Omkar2118/datasets huggingface dataset:
ECUSTFD
Full name: ECUST Food Dataset

The **ECUST Food Dataset** is a food recognition dataset that contains 2978 images

Source: [https://github.com/Liang-yc/ECUSTFD-resized-](https://github.com/Liang-yc/ECUSTFD-resized-)
Image Source: [https://github.com/Liang-yc/ECUSTFD-resized-](https://github.com/Liang-yc/ECUSTFD-resized-)

Introducing paper: Computer vision-based food calorie estimation: dataset, method, and experiment
Edge-Map-345C

**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.

Source: [https://github.com/PengBoXiangShang/EdgeMap345C_Dataset](https://github.com/PengBoXiangShang/EdgeMap345C_Dataset)
Image Source: [https://github.com/PengBoXiangShang/EdgeMap345C_Dataset](https://github.com/PengBoXiangShang/EdgeMap345C_Dataset)

Introducing paper: On Learning Semantic Representations for Million-Scale Free-Hand Sketches
Edina-DR

Edina-DR is a novel corpus of discourse relation pairs; the first of its kind to attempt to identify the discourse relations connecting the dialogic turns in open-domain discourse. 

Source: [Implicit Discourse Relation Identification for Open-domain Dialogues](/paper/implicit-discourse-relation-identification)

Introducing paper: Implicit Discourse Relation Identification for Open-domain Dialogues
EDUB-Seg
Full name: Egocentric Dataset of the University of Barcelona – Segmentation

Egocentric Dataset of the University of Barcelona – Segmentation (EDUB-Seg) is a dataset for egocentric event segmentation acquired by the Narrative Clip, which takes a picture every 30 seconds. The dataset contains a total of 18,735 images captured by 7 different users during overall 20 days. To ensure diversity, all users were wearing the camera in different contexts: while attending a conference, on holiday, during the weekend, and during the week.

Introducing paper: SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation
EGOK360

Contains annotations of human activity with different sub-actions, e.g., activity Ping-Pong with four sub-actions which are pickup-ball, hit, bounce-ball and serve. 

Source: [Egok360: A 360 Egocentric Kinetic Human Activity Video Dataset](/paper/egok360-a-360-egocentric-kinetic-human)

Introducing paper: Egok360: A 360 Egocentric Kinetic Human Activity Video Dataset
EgoShots

Egoshots is a 2-month Ego-vision Dataset with Autographer Wearable Camera annotated "for free" with transfer learning. Three state of the art pre-trained image captioning models are used. The dataset represents the life of 2 interns while working at Philips Research (Netherlands) (May-July 2015) generously donating their data.

Source: [https://github.com/NataliaDiaz/Egoshots](https://github.com/NataliaDiaz/Egoshots)

Introducing paper: Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning models
EYTH
Full name: EgoYouTubeHands

Includes egocentric videos containing hands in the wild.

Source: [Analysis of Hand Segmentation in the Wild](/paper/analysis-of-hand-segmentation-in-the-wild)

Introducing paper: Analysis of Hand Segmentation in the Wild
Egyptian Arabic Segmentation Dataset

Contains 350 tweets with more than 8,000 words including 3,000 unique words written in Egyptian dialect. The tweets have much dialectal content covering most of dialectal Egyptian phonological, morphological, and syntactic phenomena. It also includes Twitter-specific aspects of the text, such as #hashtags, @mentions, emoticons and URLs.

Source: [A Neural Architecture for Dialectal Arabic Segmentation](/paper/a-neural-architecture-for-dialectal-arabic)

Introducing paper: A Neural Architecture for Dialectal Arabic Segmentation
EiTB-ParCC

A large comparable corpus for Basque-Spanish was prepared, on the basis of independently-produced news by the Basque public broadcaster EiTB.

Source: [Handle with Care: A Case Study in Comparable Corpora Exploitation for Neural Machine Translation](/paper/handle-with-care-a-case-study-in-comparable)

Introducing paper: Handle with Care: A Case Study in Comparable Corpora Exploitation for Neural Machine Translation
Electro-Magnetic Emanations Interception Dataset

An open data corpus of 123.610 labeled samples, 

Source: [Electro-Magnetic Side-Channel Attack Through Learned Denoising and Classification](/paper/electro-magnetic-side-channel-attack-through)

Introducing paper: Electro-Magnetic Side-Channel Attack Through Learned Denoising and Classification