Omkar2118/datasets huggingface dataset:
DTTD

**Digital-Twin Tracking Dataset (DTTD)** is a novel RGB-D dataset to enable further research of the problem and extend potential solutions towards longer ranges and mm localization accuracy. In total, 103 scenes of 10 common off-the-shelf objects with rich textures are recorded, with each frame annotated with a per-pixel semantic segmentation and ground-truth object poses provided by a commercial motion capturing system.

Source: [Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications](https://arxiv.org/pdf/2302.05991v1.pdf)

Image Source: [https://arxiv.org/pdf/2302.05991v1.pdf](https://arxiv.org/pdf/2302.05991v1.pdf)

Introducing paper: Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications
CSL-Daily

CSL-Daily (Chinese Sign Language Corpus) is a large-scale continuous SLT dataset. It provides both spoken language translations and gloss-level annotations. The topic revolves around people's daily lives (e.g., travel, shopping, medical care), the most likely SLT application scenario.

[1] [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Improving_Sign_Language_Translation_With_Monolingual_Data_by_Sign_Back-Translation_CVPR_2021_paper.pdf), CVPR, 2021.

Introducing paper: Improving Sign Language Translation with Monolingual Data by Sign Back-Translation
PN9

It is a new large-scale pulmonary nodule dataset named PN9, which contains 8,798 thoracic CT scans and a total of 40,439 annotated nodules.
Office-Home-LMT
Full name: Imbalance datasets for multi-domain adaptation

The dataset is for research on the label distribution shift between multiple domain adaptations. We use **Cl**,  **Pr**, and **Rw** to resample two reverse long-tailed distributions and one Gaussian d for each of them for BTDA with label shift.

Introducing paper: Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation
Prophesee GEN4 Dataset
Full name: Prophesee 1 Megapixel Automotive Detection Dataset

The dataset is split between train, test and val folders. 

Files consist of 60 seconds recordings that were cut from longer recording sessions. Cuts from a single recording session are all in the same training split.

Each dat file is a binary file in which events are encoded using 4 bytes (unsigned int32) for the timestamps and 4 bytes (unsigned int32) for the data, encoding is little-endian ordering.

The data is composed of 14 bits for the x position, 14 bits for the y position and 1 bit for the polarity (encoded as -1/1).

Annotations use the numpy format and can simply be loaded form python using numpy boxes = np.load(path)

Boxes have the following fields:

* x abscissa of the top left corner in pixels
* y ordinate of the top left corner in pixels
* w width of the boxes in pixel
* h height of the boxes in pixel
* ts timestamp of the box in the sequence in microseconds
* class_id 0 for pedestrians, 1 for two wheelers, 2 for cars, 3 for trucks, 4 for buses, 5 for traffic signs, 6 for traffic lights

Introducing paper: Learning to Detect Objects with a 1 Megapixel Event Camera
ETD500

The paper used 500 scanned Electronic Theses and Dissertation cover pages (i.e., front pages). The dataset contains several intermediate datasets, briefly discussed in the paper.

Introducing paper: Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and Dissertations
A-FB15k237

This dataset is based on FB15k237 and a pre-trained language-model-based KGE. The main task is to add the new knowledge that the pre-trained model didn't see in the previous training stage. The model can be downloaded from [here](https://drive.google.com/drive/folders/1EOHdg8rC9iwgSyKl5RnEv9z6ATW5Ntbr?usp=share_link).
E-WN18RR

This dataset is based on WN18RR and a pre-trained language-model-based KGE.  The main task is to correct the wrong knowledge stored in the pre-trained model and replace the incorrect entities with alternative entities.  The model can be downloaded from [here](https://drive.google.com/drive/folders/1EOHdg8rC9iwgSyKl5RnEv9z6ATW5Ntbr?usp=share_link).
A-WN18RR

This dataset is based on WN18RR and a pre-trained language-model-based KGE. The main task is to add the new knowledge that the pre-trained model didn't see in the previous training stage. The model can be downloaded from [here](https://drive.google.com/drive/folders/1EOHdg8rC9iwgSyKl5RnEv9z6ATW5Ntbr?usp=share_link).
YouTube Driving

YouTube Driving Dataset contains a massive amount of real-world driving frames with various conditions, from different weather, different regions, to diverse scene types

Introducing paper: Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining
CCTSDB2021

Traffic signs are one of the most important information that guide cars to travel, and the detection of traffic signs is an important component of autonomous driving and intelligent transportation systems. Constructing a traffic sign dataset with many samples and sufficient attribute categories will promote the development of traffic sign detection research. In this paper, we propose a new Chinese traffic sign detection benchmark, which adds more than 4,000 real traffic scene images and corresponding detailed annotations based on our CCTSDB 2017, and replaces many original easily-detected images with difficult samples to adapt to the complex and changing detection environment. Due to the increase of the number of difficult samples, the new benchmark can improve the robustness of the detection network to some extent compared to the old version. At the same time, we create new dedicated test sets and categorize them according to three aspects: category meanings, sign sizes, and weather conditions. Finally, we present a comprehensive evaluation of nine classic traffic sign detection algorithms on the new benchmark. Our proposed benchmark can help determine the future research direction of the algorithm and develop a more precise traffic sign detection algorithm with higher robustness and real-time performance.

Source: [CCTSDB 2021: A more comprehensive traffic sign detection benchmark](https://centaur.reading.ac.uk/106129/)
PACE 2022 Exact
Full name: PACE 2022 Directed Feedback Vertex Set, Exact Track

This is the set of graphs used in the [PACE 2022 challenge](https://pacechallenge.org/2022/) for computing the [Directed Feedback Vertex Set](https://en.wikipedia.org/wiki/Feedback_vertex_set), from the Exact track. It consists of 200 labelled directed graphs. The graphs range in size up to from N=512 up to N=131072 vertices, and up to 1315170 edges. The graphs are mostly not symmetric (an edge form u->v does not imply an edge from v->u), although some are symmetric. The graph labels are integers ranging from 1 to N.

There is the related [PACE 2022 Heuristic](https://paperswithcode.com/dataset/pace-2022-heuristic) dataset, which allowed for approximate solutions (feedback vertex sets that were not necessarily of minimum size). Those graphs are generally larger and denser, as approximate solutions were still accepted.

The [data format](https://pacechallenge.org/2022/tracks/#input-format) begins with one line `N E 0`, where N is the number of vertices, E is the number of edges, and 0 is the literal integer zero. The N subsequent lines are each a space-separated list of integers, such as `2 5 11 19`. If that appeared on line number 1 (the first after `N E 0`), it would indicate that there are edges from vertex 1 to each of the vertices 2, 5, 11, and 19. Some lines are blank, and these indicates vertices with outdegree zero. An example graph would be
```
4 4 0
2 3
3

1
```

The dataset can be downloaded [here](https://heibox.uni-heidelberg.de/f/75c18a4d83b642db9a58/?dl=1). The 100 instances that were available for public testing are precisely the odd-numbered ones in that link; the public instances can be downloaded on their own [here](https://heibox.uni-heidelberg.de/f/be4337d9e4234bca8606/?dl=1).
PACE 2022 Heuristic
Full name: PACE 2022 Directed Feedback Vertex Set, HeuristicTrack

This is the set of graphs used in the [PACE 2022 challenge](https://pacechallenge.org/2022/) for computing the [Directed Feedback Vertex Set](https://en.wikipedia.org/wiki/Feedback_vertex_set), from the Heuristic track. It consists of 200 labelled directed graphs. The graphs are mostly not symmetric (an edge form u->v does not imply an edge from v->u), although some are symmetric. The graph labels are integers ranging from 1 to N.

There is the related [PACE 2022 Exact](https://paperswithcode.com/dataset/pace-2022-exact) dataset, which was for exact computation; those graphs are generally smaller and sparser, as only exact solutions were accepted.

The [data format](https://pacechallenge.org/2022/tracks/#input-format) begins with one line `N E 0`, where N is the number of vertices, E is the number of edges, and 0 is the literal integer zero. The N subsequent lines are each a space-separated list of integers, such as `2 5 11 19`. If that appeared on line number 1 (the first after `N E 0`), it would indicate that there are edges from vertex 1 to each of the vertices 2, 5, 11, and 19. Some lines are blank, and these indicates vertices with outdegree zero. An example graph would be
```
4 4 0
2 3
3

1
```

The dataset can be downloaded [here](https://heibox.uni-heidelberg.de/f/97634323e3cb4aab8291/?dl=1). The 100 instances that were available for public testing are precisely the odd-numbered ones in that link; the public instances can be downloaded on their own [here](https://heibox.uni-heidelberg.de/f/4fc21bd9748140bd8307/?dl=1).
AfriSenti
Full name: AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages

AfriSenti is the largest sentiment analysis dataset for under-represented African languages, covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba).
Jet Flavor dataset

Dataset for 'Jet Flavor Classification in High-Energy Physics with Deep Neural Networks'

Introducing paper: Jet Flavor Classification in High-Energy Physics with Deep Neural Networks