Omkar2118/datasets huggingface dataset:
ESPADA
Full name: Extended Synthetic and Photogrammetric Aerial-Image Dataset

We present a new aerial image dataset, named ESPADA, intended for the training of deep neural networks for depth image estimation from a single aerial image. Given the difficulty of creating aerial image datasets containing image pairs of chromatic images related to their depth images, simulators such as AirSim have been proposed to generate synthetic images from photorealistic scenes. The latter enables the generation of thousands of images that can be used to train and evaluate neural models. However, we argue that synthetic photorealistic aerial image datasets can be improved by adding images generated from photogrammetric models imported into the simulator, thus enabling a less artificial generation of both chromatic and depth images. To assess the quality of these images, we compare the performance of 4 deep neural networks whose pre-trained models and code for re-training are publicly available. We also use ORB-SLAM, in its RGB-D version, to indirectly assess the estimated depth image. To accomplish this, chromatic images from 3 aerial videos and their depth images, estimated with the networks trained with ESPADA, are fed into ORB-SLAM. The estimated camera pose is compared against the trajectory retrieved from the GPS flight trajectory. Our results indicate that images generated from photogrammetric models improve the performance of depth estimation from a single aerial image.
Cats and Dogs

A large set of images of cats and dogs. 

Homepage: https://www.microsoft.com/en-us/download/details.aspx?id=54765

Source code: tfds.image_classification.CatsVsDogs

Versions:

4.0.0 (default): New split API (https://tensorflow.org/datasets/splits)
Download size: 786.68 MiB


Source: https://www.tensorflow.org/datasets/catalog/cats_vs_dogs
GESTURES
Full name: Generalized Epileptic Seizure classification from video-Telemetry Using REcurrent convolutional neural networkS

This is the dataset to support the paper:

Fernando Pérez-García et al., 2021, Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures.

The paper has been accepted for publication at the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021).
A preprint is available on arXiv: https://arxiv.org/abs/2106.12014

Contents:
1) A CSV file "seizures.csv" with the following fields:
- Subject: subject number
- Seizure: seizure number
- OnsetClonic: annotation marking the onset of the clonic phase
- GTCS: whether the seizure generalises
- Discard: whether one (Large, Small), none (No) or both (Yes) views were discarded for training.
2) A folder "features_fpc_8_fps_15" containing two folders per seizure.
The folders contain features extracted from all possible snippets from the small (S) and large (L) views. The snippets were 8 frames long and downsampled to 15 frames per second. The features are in ".pth" format and can be loaded using PyTorch: https://pytorch.org/docs/stable/generated/torch.load.html
The last number of the file name indicates the frame index. For example, the file "006_01_L_000015.pth" corresponds to the features extracted from a snippet starting one second into the seizure video. Each file contains 512 numbers representing the deep features extracted from the corresponding snippet.
3) A description file, "README.txt"

Introducing paper: Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures
ASR-GLUE

The **ASR-GLUE** benchmark is a collection of 6 different NLU (Natural Language Understanding) tasks for evaluating the performance of models under automatic speech recognition (ASR) error across 3 different levels of background noise and 6 speakers with various voice characteristics.

Introducing paper: ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding
SHIFT15M

**SHIFT15M** is a dataset that can be used to properly evaluate models in situations where the distribution of data changes between training and testing. 
The SHIFT15M dataset has several good properties: (i) Multiobjective. Each instance in the dataset has several numerical values that can be used as target variables. (ii) Large-scale. The SHIFT15M dataset consists of 15million fashion images. (iii) Coverage of types of dataset shifts. SHIFT15M contains multiple dataset shift problem settings (e.g., covariate shift or target shift). SHIFT15M also enables the performance evaluation of the model under various magnitudes of dataset shifts by switching the magnitude.

Introducing paper: SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts
VesselGraph

**VesselGraph** is a dataset of whole-brain vessel graphs based on specific imaging protocols. Specifically, vascular graphs are extracted using a refined graph extraction scheme leveraging the volume rendering engine Voreen and provided in an accessible and adaptable form through the OGB and PyTorch Geometric dataloaders.

Introducing paper: Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)
CrossedWires

**CrossedWires** is a living dataset of models and hyperparameters that exposes semantic differences between two popular deep learning frameworks: PyTorch and Tensorflow. 

The CrossedWires dataset currently consists of models trained on CIFAR10 images using three different computer vision architectures: VGG16, ResNet50 and DenseNet121 across a large hyperparameter space. Using hyperparameter optimization, each of the three models was trained on 400 sets of hyperparameters suggested by the HyperSpace search algorithm. 

The CrossedWires dataset includes PyTorch and Tensforflow models with test accuracies as different as 0.681 on syntactically equivalent models and identical hyperparameter choices. The 340 GB dataset and benchmarks presented here include the performance statistics, training curves, and model weights for all 1200 hyperparameter choices, resulting in 2400 total models. The CrossedWires dataset provides an opportunity to study semantic differences between syntactically equivalent models across popular deep learning frameworks.

Introducing paper: CrossedWires: A Dataset of Syntactically Equivalent but Semantically Disparate Deep Learning Models
HeadlineCause

**HeadlineCause** is a dataset for detecting implicit causal relations between pairs of news headlines. The dataset includes over 5000 headline pairs from English news and over 9000 headline pairs from Russian news labeled through crowdsourcing. The pairs vary from totally unrelated or belonging to the same general topic to the ones including causation and refutation relations.

Introducing paper: HeadlineCause: A Dataset of News Headlines for Detecting Causalities
Lyra

Lyra is a dataset for code generation that consists on Python code with embedded SQL. This dataset contains 2,000 carefully annotated database manipulation programs from real usage projects. Each program is paired with both a Chinese comment and an English comment.

Introducing paper: Lyra: A Benchmark for Turducken-Style Code Generation
ReadingBank

ReadingBank is a benchmark dataset for reading order detection built with weak supervision from WORD documents, which contains 500K document images with a wide range of document types as well as the corresponding reading order information.

Introducing paper: LayoutReader: Pre-training of Text and Layout for Reading Order Detection