Omkar2118/datasets huggingface dataset:
TArC

A morpho-syntactically annotated Tunisian Arabish Corpus (TArC).

Source: [TArC: Incrementally and Semi-Automatically Collecting a Tunisian Arabish Corpus](/paper/tarc-incrementally-and-semi-automatically)

Introducing paper: TArC: Incrementally and Semi-Automatically Collecting a Tunisian Arabish Corpus
Taskmaster-2

The **Taskmaster-2** dataset consists of 17,289 dialogs in seven domains: restaurants (3276), food ordering (1050), movies (3047), hotels (2355), flights (2481), music (1602), and sports (3478).

Introducing paper: Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
Tasty Videos

A collection of 2511 recipes for zero-shot learning, recognition and anticipation.

Source: [Zero-Shot Anticipation for Instructional Activities](/paper/zero-shot-anticipation-for-instructional)

Introducing paper: Zero-Shot Anticipation for Instructional Activities
TB-Places

TB-Places is a data set of garden images for testing algorithms for visual place recognition. It contains images with ground truth camera pose recorded in two real gardens at different times, with a total of four different recording sessions, with varying light conditions.

Source: [TB-Places](https://github.com/marialeyva/TB_Places)
TCG
Full name: Traffic Control Gesture

The **TCG** dataset is used to evaluate **Traffic Control Gesture** recognition for autonomous driving. The dataset is based on 3D body skeleton input to perform traffic control gesture classification on every time step. The dataset consists of 250 sequences from several actors, ranging from 16 to 90 seconds per sequence.

Source: [https://arxiv.org/pdf/2007.16072.pdf](https://arxiv.org/pdf/2007.16072.pdf)
Image Source: [https://github.com/againerju/tcg_recognition](https://github.com/againerju/tcg_recognition)

Introducing paper: Traffic Control Gesture Recognition for Autonomous Vehicles
TCIA Test & Validation Radiotherapy CT Planning Scan

A dataset of 663 deidentified computed tomography (CT) scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations.

Source: [Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy](/paper/deep-learning-to-achieve-clinically)

Introducing paper: Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
TE141K

A new text effects dataset with 141,081 text effect/glyph pairs in total. The dataset consists of 152 professionally designed text effects rendered on glyphs, including English letters, Chinese characters, and Arabic numerals. 

Source: [TE141K: Artistic Text Benchmark for Text Effect Transfer](/paper/190503646)

Introducing paper: TE141K: Artistic Text Benchmark for Text Effect Transfer