Omkar2118/datasets huggingface dataset:
Heel Dataset
Full name: Heel Bone X-Ray Dataset

Heel Bone X-Ray Dataset consists of 3,956 X-ray images of the foot, primarily focused on detecting and classifying heel bone diseases. The images were obtained from Kirkuk General Hospital in Digital Imaging and Communications in Medicine (DICOM) format and converted to JPG format using the MicroDicom tool. 

The dataset is labeled into three distinct classes:

Normal: 1,842 images
Heel Spur: 1,316 images
Severe (Heel Spur Complications): 798 images

All labels were assigned by a specialist in orthopedics and traumatology and were cross-verified by a radiology specialist for validation, ensuring high-quality annotations. This dataset is valuable for training models on medical image classification, specifically targeting foot-related conditions.

Dataset Size: 3,956 images

Image Format: JPG (originally in DICOM format)

Source: Kirkuk General Hospital

https://www.kaggle.com/datasets/osamahtaher/heel-dataset/data

Paper:
HeCapsNet: An enhanced capsule network for automated heel disease diagnosis using lateral foot X-Ray images.

Taher, Osamah, and Kasım Özacar. "HeCapsNet: An enhanced capsule network for automated heel disease diagnosis using lateral foot X‐Ray images." International Journal of Imaging Systems and Technology 34.3 (2024): e23084.
Synthia-Seq

Synthia-Seq contains 8,000 photo-realistic frames with dense segmentation labels. We often employ video frames in the RGB
folder and choose 11 categories common with Cityscapes-Seq for domain adaptation.

Introducing paper: The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes
Cityscapes-Seq

Cityscapes-Seq  is a standard dataset for semantic urban scene understanding, featuring real-world videos from 50 cities in Germany and neighboring countries. It comprises 2,975 training video clips and 500 validation video clips, and each clip contains continuous 30 frames.

Introducing paper: The Cityscapes Dataset for Semantic Urban Scene Understanding
AssistTaxi

The availability of high-quality datasets play a crucial role in advancing research and development especially, for safety critical and autonomous systems. In this paper, we present AssistTaxi, a comprehensive novel dataset which is a collection of images for runway and taxiway analysis. The dataset comprises of more than 300,000 frames of diverse and carefully collected data, gathered from Melbourne (MLB) and Grant-Valkaria (X59) general aviation airports. The importance of AssistTaxi lies in its potential to advance autonomous operations, enabling researchers and developers to train and evaluate algorithms for efficient and safe taxiing.

Introducing paper: AssistTaxi: A Comprehensive Dataset for Taxiway Analysis and Autonomous Operations