Omkar2118/datasets huggingface dataset:
GSV-Cities

GSV-Cities is a large-scale dataset for training deep neural network for the task of Visual Place Recognition.

The dataset contains more than **530k** images:

* There are more than **62k** different places, spread across multiple cities around the globe.
* Each place is depited by at least 4 images (up to 20 images).
* All places are physically distant (at least 100 meters between any pair of places).

Introducing paper: GSV-Cities: Toward Appropriate Supervised Visual Place Recognition
A collection of 131 CT datasets of pieces of modeling clay containing stones

This dataset contains a collection of 131 X-ray CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted, retrieved in the FleX-ray lab at CWI. The dataset consists of 5 parts. It is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection". The dataset can be used to set up other CT-based experiments concerning similar objects with variations in shape and composition.

Introducing paper: A tomographic workflow to enable deep learning for X-ray based foreign object detection
A collection of X-ray projections of 131 pieces of modeling clay containing stones for machine learning-driven object detection

This dataset contains a collection of 235800 X-ray projections of 131 pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The dataset is intended as an extensive and easy-to-use training dataset for supervised machine learning driven object detection. The ground truth locations of the stones are included.

Introducing paper: A tomographic workflow to enable deep learning for X-ray based foreign object detection
DIVOTrack

**DIVOTrack** is a cross-view multi-object tracking dataset for DIVerse Open scenes with dense tracking pedestrians in realistic and non-experimental environments. DIVOTrack has ten distinct scenarios and 550 cross-view tracks.

Source: [DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes](https://arxiv.org/pdf/2302.07676v1.pdf)

Image Source: [https://github.com/shengyuhao/DIVOTrack](https://github.com/shengyuhao/DIVOTrack)

Introducing paper: DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes
TT100K
Full name: Tsinghua-Tencent 100K(official training and testing set)

Trainging and testing data: The original training set includes 6105 images, and the original testing set includes 3071 images.

Description: Although promising results have been achieved in the areas of traffic-sign detection and classification, few works have provided simultaneous solutions to these two tasks for realistic real world images. We make two contributions to this problem. Firstly, we have created a large traffic-sign benchmark from 100000 Tencent Street View panoramas, going beyond previous benchmarks. We call this benchmark Tsinghua-Tencent 100K. It provides 100000 images containing 30000 traffic-sign instances. These images cover large variations in illuminance and weather conditions. Each traffic-sign in the benchmark is annotated with a class label, its bounding box and pixel mask. Secondly, we demonstrate how a robust end-to-end convolutional neural network (CNN) can simultaneously detect and classify traffic-signs. Most previous CNN image processing solutions target objects that occupy a large proportion of an image, and such networks do not work well for target objects occupying only a small fraction of an image like the traffic-signs here. Experimental results show the robustness of our network and its superiority to alternatives. The benchmark, source code and the CNN model introduced in this paper is publicly available.