Omkar2118/datasets huggingface dataset:
Biased-Cars

We introduce a challenging new dataset for simultaneous object category and viewpoint classification—the Biased-Cars dataset. Our dataset features photo-realistic outdoor scene data with fine control over scene clutter (trees, street furniture, and pedestrians), car colors, object occlusions, diverse backgrounds (building/road textures) and lighting conditions (sky maps). Biased-Cars consists of 15K images of five different car models seen from viewpoints varying between 0-90 degrees of azimuth, and 0-50 degrees of zenith across multiple scales. Our dataset offers complete control over the joint distribution of categories, viewpoints, and other scene parameters, and the use of physically based rendering ensures photo-realism.

Introducing paper: When and how CNNs generalize to out-of-distribution category-viewpoint combinations
GINC
Full name: Generative IN-Context learning Dataset

GINC (Generative In-Context learning Dataset) is a small-scale synthetic dataset for studying in-context learning. The pretraining data is generated by a mixture of HMMs and the in-context learning prompt examples are also generated from HMMs (either from the mixture or not). The prompt examples are out-of-distribution with respect to the pretraining data since every example is independent, concatenated, and separated by delimiters. The GitHub repository provides code to generate GINC-style datasets of varying vocabulary sizes, number of HMMs, and other parameters.

Introducing paper: An Explanation of In-context Learning as Implicit Bayesian Inference
HGP
Full name: Hands Guns and Phones Dataset

Hands Guns and Phones (HGP) dataset contains 2199 images (1989 for training an 210 for testing) of people using guns or phones in real-world scenarios (people making phones reviews, shooting drills, or making calls). Every image of this dataset is labeled with the bounding boxes of Hands, Phones and Guns. All the aforementioned images were collected from Youtube videos and have different sizes.

Introducing paper: TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video
THGP
Full name: Temporal Hands Guns and Phones Dataset

Temporal Hands Guns and Phones (THGP) dataset, is a collection of 5960 video frames (5000 for training and 960 for testing). The training part is composed with 50 videos of 100 frames (720 × 720 pixels). This dataset contains 20 videos of shooting drills, 20 videos of armed robberies, and 10 videos of people making calls. The testing part contains 48 videos of 20 frames (720 × 720). Videos contained in the testing dataset includes phone calls, gun reviews, shooting drills, people making calls, and armed robberies at convenience stores. This dataset is labeled with the bounding boxes of hands, phones, and guns.

Introducing paper: TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video
Pan-STARRS
Full name: Panoramic Survey Telescope and Rapid Response System (Pan-STARRS)

Pan-STARRS is a system for wide-field astronomical imaging developed and operated by the Institute for Astronomy at the University of Hawaii. Pan-STARRS1 (PS1) is the first part of Pan-STARRS to be completed and is the basis for both Data Releases 1 and 2 (DR1 and DR2).  The PS1 survey used a 1.8 meter telescope and its 1.4 Gigapixel camera  to image the sky in five broadband  filters (g, r, i, z, y).
CAR
Full name: Cityscapes Attributes Recognition

CAR contains visual attributes for objects in the Cityscapes dataset.
For each object in an image, we have a list of attributes that depend on the category of the object. For instance, a vehicle category has a visibility attribute while a pedestrian has an activity attribute (walking, standing, etc.). 

The objective of this dataset is to ease the development of better algorithms for self-driving vehicles as that requires a complete understanding of the entire scene with all of its details including attributes of all objects.

We chose Cityscapes as it already contains different types of useful annotations and adding attributes to that will remove a huge burden over developing algorithms with self or semi supervision.

Introducing paper: CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles
Robotic Interestingness
Full name: Robotic Interestingness: A Dataset to Push the Limits of Online Visual Interesting Scene Prediction

Robotic Interestingness dataset was created to promote the development visual interesting scene prediction for such purpose, for robots to better sense the world.

Introducing paper: Unsupervised Online Learning for Robotic Interestingness with Visual Memory
Haze4k

**Haze4k** is a synthesized dataset with 4,000 hazy images, in which each hazy image has the associate ground truths of a latent clean image, a transmission map, and an atmospheric light ma

Introducing paper: From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data
ChEBI-20

Dataset contains 33,010 molecule-description pairs split into 80\%/10\%/10\% train/val/test splits. The goal of the task is to retrieve the relevant molecule for a natural language description. It is defined as follows:

To push the boundaries of multimodal models, we present a new IR task: \textbf{Text2Mol}.

Given a text query and list of molecules without any reference textual information (represented, for example, as SMILES strings, graphs, or other equivalent representations) retrieve the molecule corresponding to the query. From a text description of a molecule, the model must incorporate the information in the description into a semantic representation which can be used to directly retrieve the molecule. This requires the integration of two very different types of information: the structured knowledge represented by text and the chemical properties present in molecular graphs. We assume there is only one correct (relevant) molecule for each description, so we consider two measures for this task: Hits@1 and mean reciprocal rank (MRR). 

80\% of the data is used for training. Retrieval is done against the entire corpus of molecules (train, val, test).

Introducing paper: Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries