Omkar2118/datasets huggingface dataset:
TopLogo-10

Collected from top 10 most popular clothing/wearable brandname logos captured in rich visual context.

Source: [Deep Learning Logo Detection with Data Expansion by Synthesising Context](/paper/deep-learning-logo-detection-with-data)

Introducing paper: Deep Learning Logo Detection with Data Expansion by Synthesising Context
Topology Optimization Dataset

TOP is a synthetic dataset for topology optimization generated using Topy. The generated dataset has 10,000 objects which consist on 100 iterations of the optimization process for the problem defined on a regular 40 x 40 grid.

Source: [https://arxiv.org/pdf/1709.09578.pdf](https://arxiv.org/pdf/1709.09578.pdf)
Image Source: [https://github.com/ISosnovik/top](https://github.com/ISosnovik/top)

Introducing paper: Neural networks for topology optimization
Toronto-3D

**Toronto-3D** is a large-scale urban outdoor point cloud dataset acquired by an MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of road and consists of about 78.3 million points. Point clouds has 10 attributes and classified in 8 labelled object classes.

Source: [https://github.com/WeikaiTan/Toronto-3D](https://github.com/WeikaiTan/Toronto-3D)
Image Source: [https://github.com/WeikaiTan/Toronto-3D](https://github.com/WeikaiTan/Toronto-3D)

Introducing paper: Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
Torque

Torque is an English reading comprehension benchmark built on 3.2k news snippets with 21k human-generated questions querying temporal relationships.

Source: [TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions](/paper/torque-a-reading-comprehension-dataset-of)

Introducing paper: TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions
Touchdown Dataset

Touchdown is a corpus for executing navigation instructions and resolving spatial descriptions in visual real-world environments. The task is to follow instruction to a goal position and there find a hidden object, Touchdown the bear.

Source: [https://github.com/lil-lab/touchdown](https://github.com/lil-lab/touchdown)
Image Source: [https://github.com/lil-lab/touchdown](https://github.com/lil-lab/touchdown)

Introducing paper: Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
Tour20

Contains 140 videos with multiple human created summaries, which were acquired in a controlled experiment. 

Source: [Diversity-aware Multi-Video Summarization](/paper/diversity-aware-multi-video-summarization)

Introducing paper: Diversity-aware Multi-Video Summarization
ToyADMOS

**ToyADMOS** dataset is a machine operating sounds dataset of approximately 540 hours of normal machine operating sounds and over 12,000 samples of anomalous sounds collected with four microphones at a 48kHz sampling rate, prepared by Yuma Koizumi and members in NTT Media Intelligence Laboratories. The ToyADMOS dataset is designed for anomaly detection in machine operating sounds (ADMOS) research. It is designed for three tasks of ADMOS: product inspection (toy car), fault diagnosis for fixed machine (toy conveyor), and fault diagnosis for moving machine (toy train).

Source: [https://github.com/YumaKoizumi/ToyADMOS-dataset](https://github.com/YumaKoizumi/ToyADMOS-dataset)

Introducing paper: ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection
Toyota Smarthome Dataset

A large scale dataset with daily-living activities performed in a natural manner.

Source: [Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection](/paper/toyota-smarthome-untrimmed-real-world)

Introducing paper: Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection
TPIC17
Full name: Temporal Popularity Image Collection

Image dataset with about 600K Flickr photos.

Source: [Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks](/paper/sequential-prediction-of-social-media)

Introducing paper: Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
TRACT
Full name: Tweets Reporting Abuse Classification Task Corpus

TRACT is a small scale manually annotated corpus for abuse classification problem.

Source: [TRACT](https://github.com/Saichethan/TRACT)
Traditional Chinese Landscape Painting Dataset

This dataset consists of 2,192 high-quality traditional Chinese landscape paintings (中国山水画). All paintings are sized 512x512, from the following sources:
* Princeton University Art Museum, 362 paintings
* Harvard University Art Museum, 101 paintings
* Metropolitan Museum of Art, 428 paintings
* Smithsonian's Freer Gallery of Art, 1,301 paintings

Source: [https://github.com/alicex2020/Chinese-Landscape-Painting-Dataset](https://github.com/alicex2020/Chinese-Landscape-Painting-Dataset)
Image Source: [https://github.com/alicex2020/Chinese-Landscape-Painting-Dataset](https://github.com/alicex2020/Chinese-Landscape-Painting-Dataset)

Introducing paper: End-to-End Chinese Landscape Painting Creation Using Generative Adversarial Networks
CVL Traffic Signs Dataset

A video dataset for recognising traffic signs hosted with the first IEEE Video and Image Processing (VIP) Cup within the IEEE Signal Processing Society.

Source: [Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions](/paper/challenging-environments-for-traffic-sign)

Introducing paper: Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions
Trans10K

A large-scale dataset for transparent object segmentation, named Trans10K, consisting of 10,428 images of real scenarios with carefully manual annotations, which are 10 times larger than the existing datasets. 

Source: [Segmenting Transparent Objects in the Wild](/paper/segmenting-transparent-objects-in-the-wild)

Introducing paper: Segmenting Transparent Objects in the Wild
Transient Biometrics Nails Dataset

An extended version of an experimental dataset, called **Transient Biometrics Nails Dataset** (TBND), was created. TBND is composed of images of the right index finger. During acquisition the subject was instructed to lay her finger over a flat white surface and a simple point-and-shoot camera was used to acquire an image without the the use of a flash. No explicit instructions with respect to force applied were given and thus the results incorporate arbitrary force differences between users and capture sessions. Acquisition was thus done in a semi-controlled environment; apart from the white background and indirect lighting, the images present variation with respect to scale, focal plane and illumination. The dataset consists of three subsets, each one compromising the same 93 subjects, but varying on acquisition date. The first subset D01 consists of images acquired on the first acquisition day. The second subset D02 is composed of images acquired one day later. The third subset D30 was acquired 1 month after the first acquisition date. Given acquisition restrictions, the acquisitions of D30 have up to two days’ tolerance. This represents a massive expansion of the originally collected dataset TBND V01

Source: [Transient Biometrics Nails Dataset](http://www.idi.ntnu.no/grupper/vis/tbnd/)
TREK-100

The dataset is composed of 100 video sequences densely annotated with 60K bounding boxes, 17 sequence attributes, 13 action verb attributes and 29 target object attributes. 

Source: [Is First Person Vision Challenging for Object Tracking? The TREK-100 Benchmark Dataset](/paper/is-first-person-vision-challenging-for-object)

Introducing paper: Is First Person Vision Challenging for Object Tracking?