Omkar2118/datasets huggingface dataset:
CoCaHis
Full name: Colon Cancer Histology Dataset

Highlights

• Publicly available dataset with 82 H&E stained images of frozen sections.
• Images are acquired on 19 patients with metastatic colon cancer in a liver.
• Originally stained and two stain-normalized sets of images included
• Pixel wise ground truths provided by seven domain experts.
VALSE
Full name: VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena

We propose VALSE (Vision And Language Structured Evaluation), a novel benchmark designed for testing general-purpose pretrained vision and language (V&L) models for their visio-linguistic grounding capabilities on specific linguistic phenomena. VALSE offers a suite of six tests covering various linguistic constructs. Solving these requires models to ground linguistic phenomena in the visual modality, allowing more fine-grained evaluations than hitherto possible.
We expect VALSE to serve as an important benchmark to measure future progress of pretrained V&L models from a linguistic perspective, complementing the canonical task-centred V&L evaluations.

Introducing paper: VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena
MFR
Full name: Ongoing version of ICCV-2021 Masked Face Recognition Challenge & Workshop(MFR)

During the COVID-19 coronavirus epidemic, almost everyone wears a facial mask, which poses a huge challenge to face recognition. Traditional face recognition systems may not effectively recognize the masked faces, but removing the mask for authentication will increase the risk of virus infection. Inspired by the COVID-19 pandemic response, the widespread requirement that people wear protective face masks in public places has driven a need to understand how face recognition technology deals with occluded faces, often with just the periocular area and above visible. 

To cope with the challenge arising from wearing masks, it is crucial to improve the existing face recognition approaches. Recently, some commercial providers have announced the availability of face recognition algorithms capable of handling face masks, and an increasing number of research publications have surfaced on the topic of face recognition on people wearing masks. However, due to the sudden outbreak of the epidemic, there is yet no publicly available masked face recognition benchmark. In this workshop, we will organise Masked Face Recognition (MFR) challenge and focus on bench-marking deep face recognition methods under the existence of facial masks.

In this challenge, we will evaluate the accuracy of following testsets:

Accuracy between masked and non-masked faces.
Accuracy among children(2~16 years old).
Accuracy of globalised multi-racial benchmarks.
We ensure that there's no overlap between these testsets and public available training datasets, as they are not collected from online celebrities.

The globalised multi-racial testset contains 242,143 identities and 1,624,305 images.
Mask testset contains 6,964 identities, 6,964 masked images and 13,928 non-masked images. There are totally 13,928 positive pairs and 96,983,824 negative pairs.
Children testset contains 14,344 identities and 157,280 images. There are totally 1,773,428 positive pairs and 24,735,067,692 negative pairs.

For Mask set, TAR is measured on mask-to-nonmask 1:1 protocal, with FAR less than 0.0001(e-4).

For Children set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.0001(e-4).

For other sets, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6).

Participants are ordered in terms of highest scores across two datasets: TAR@Mask and TAR@MR-All, by the formula of 0.25 * TAR@Mask + 0.75 * TAR@MR-All.

Introducing paper: Masked Face Recognition Challenge: The InsightFace Track Report
The Mafia Dataset

The Mafia Dataset was created to model the behavior of deceptive actors in the context of the Mafia game, as described in the paper “Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia”. We hope that this dataset will be of use to others studying the effects of deception on language use.

Introducing paper: Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia