This composite dataset comprising of mid-sagittal views of lumbar spine is composed of images of lumbar spine with ground truth images duly labelled/annotated as well the spinal measurements. The purpose of creating this dataset was to establish a strong correlation in the images with the spinal measurements being clinically relevant. Presently, these measurements are being taken either completely through manual methods or by the use of computer assisted tools. The spinal measurements are clinically significant for a spinal surgeon before suggesting or shortlisting suitable surgical intervention procedure. Traditionally, the spinal surgeon evaluates the condition of the patient before surgical procedure in order to ascertain the usefulness of the adopted procedure. It also helps the surgeon in establishing a relation regarding effectiveness of the procedure adopted. For example, in case of spinal fusion procedure, will the fusion procedure be able to restore the spinal balance is a question for which the answered is obtained through making relevant spinal measurements, including lumbar lordotic curve angle, both segmental and for whole lumbar spine, lumbosacral angle, spinal heights, dimensions of vertebral bodies etc.

The Composite Dataset is acquired in following steps:- 1. Exporting mid-sagittal view from the MRI dataset. (Originally taken from Sudirman, Sud; Al Kafri, Ala; natalia, friska; Meidia, Hira; Afriliana, Nunik; Al-Rashdan, Wasfi; Bashtawi, Mohammad; Al-Jumaily, Mohammed (2019), “Label Image Ground Truth Data for Lumbar Spine MRI Dataset”, Mendeley Data, V2, doi: 10.17632/zbf6b4pttk.2). The original dataset comprises of axial views with annotations however, to determine the efficacy of spinal deformities and analyzing spinal balance sagittal views are used instead. 2. Manual labelling of lumbar vertebral bodies from L1 to L5 and first sacrum bone. Total 6 regions were labelled in consultation with expert radiologists followed by validation by expert spinal surgeon. 3. Performing fully automatic spinal measurements, including, vertebral bodies identification and labelling, lumbar height, lumbosacral angle, lumbar lordotic angle, estimation of spinal curve, intervertebral body dimensions, vertebral body dimensions. All the angular measurements are in degrees, whereas the distance measurements are in millimeters.

A total of 514 images and annotations with spinal measurements can be downloaded with request to please cite out work in your research.

The FloodNet 2021: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding provides high-resolution UAS imageries with detailed semantic annotation regarding the damages. To advance the damage assessment process for post-disaster scenarios, the authors of the dataset presented a unique challenge considering classification, semantic segmentation, and visual question answering highlighting the UAS imagery-based FloodNet dataset. The Challenge has two tracks: 1) Image Classification and Semantic Segmentation (available on DatasetNinja); and 2) Visual Question Answering (current).

Endowing Large Multimodal Models (LMMs) with visual grounding capability can significantly enhance AIs’ understanding of the visual world and their interaction with humans. However, existing methods typically fine-tune the parameters of LMMs to learn additional segmentation tokens and overfit grounding and segmentation datasets. Such a design would inevitably cause a catastrophic diminution in the indispensable conversational capability of general AI assistants. In this paper, we comprehensively evaluate state-of-the-art grounding LMMs across a suite of multimodal question-answering benchmarks, observing drastic performance drops that indicate vanishing general knowledge comprehension and weakened instruction following ability. To address this issue, we present FLMM—grounding frozen off-the-shelf LMMs in human-AI conversations—a straightforward yet effective design based on the fact that word-pixel correspondences conducive to visual grounding inherently exist in the attention mechanism of well-trained LMMs. Using only a few trainable CNN layers, we can translate word-pixel attention weights to mask logits, which a SAM-based mask refiner can further optimise. Our F-LMM neither learns special segmentation tokens nor utilises high-quality grounded instruction-tuning data, but achieves competitive performance on referring expression segmentation and panoptic narrative grounding benchmarks while completely preserving LMMs’ original conversational ability. Additionally, with instructionfollowing ability preserved and grounding ability obtained, F-LMM can be directly applied to complex tasks like reasoning segmentation, grounded conversation generation and visual chain-of-thought reasoning. Our code can be found at https://github.com/wusize/F-LMM.

Analysis of ‘🍒 Social Influence on Shopping’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/social-influence-on-shoppinge on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This data was collected on our social survey mobile platform Whatsgoodly. We have 300,000 millennial and Gen Z members, and have collected 150,000,000 survey responses from this demographic to date.

This dataset was created by Adam Halper and contains around 1000 samples along with Count, Segment Type, technical information and other features such as: - Segment Description - Percentage - and more.

How to use this dataset

• Analyze Answer in relation to Question
• Study the influence of Count on Segment Type
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Adam Halper

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Overview

Frequent, and increasingly severe, natural disasters threaten human health, infrastructure, and natural systems. The provision of accurate, timely, and understandable information has the potential to revolutionize disaster management. For quick response and recovery on a large scale, after a natural disaster such as a hurricane, access to aerial images is critically important for the response team. The emergence of small unmanned aerial systems (UAS) along with inexpensive sensors presents the opportunity to collect thousands of images after each natural disaster with high flexibility and easy maneuverability for rapid response and recovery. Moreover, UAS can access hard-to-reach areas and perform data collection tasks that can be unsafe for humans if not impossible. Despite all these advancements and efforts to collect such large datasets, analyzing them and extracting meaningful information remains a significant challenge in scientific communities.

FloodNet provides high-resolution UAS imageries with detailed semantic annotation regarding the damages. To advance the damage assessment process for post-disaster scenarios, we present a unique challenge considering classification, semantic segmentation, visual question answering highlighting the UAS imagery-based FloodNet dataset.

Track 1: Image Classification and Semantic Segmentation Track 2: Visual Question Answering

Dataset Details

The data is collected with a small UAS platform, DJI Mavic Pro quadcopters, after Hurricane Harvey. The whole dataset has 2343 images, divided into training (~60%), validation (~20%), and test (~20%) sets.

For Track 1 ( Semi-supervised Classification and Semantic Segmentation), in the training set, we have around 400 labeled images (~25% of the training set) and around 1050 unlabeled images (~75% of the training set ).

For Track 2 ( Supervised VQA), in the training set we have around 1450 images and there are a total 4511 image-question pairs.

Track 1

In this track, participants are required to complete two semi-supervised tasks. The first task is image classification, and the second task is semantic segmentation.

Semi-Supervised Classification: Classification for FloodNet dataset requires classifying the images into ‘Flooded’ and ‘Non-Flooded’ classes. Only a few of the training images have their labels available, while most of the training images are unlabeled.

Semi-Supervised Semantic Segmentation: The semantic segmentation labels include:

0 - Background

1 - Building Flooded

2 - Building Non-Flooded

3 - Road Flooded

4 - Road Non-Flooded

5 - Water

6 - Tree

7 - Vehicle

8 - Pool

9 - Grass.

Only a small portion of the training images have their corresponding masks available.

Track 2

For the Visual Question Answering (VQA) task, we provide images associated with multiple questions. These questions will be divided into the following categories:

• Simple Counting: Questions will be designed to count the number of objects regardless of their attribute. For example: “how many buildings are there in the image?”.
• Complex Counting: Questions will be asked to count the number of objects belonging to a specific attribute. For example: “how many flooded buildings are there in the image?”.
• Condition Recognition: In this category, all the questions are mainly designed to ask questions regarding the condition of the object and the neighborhood. For example: “What is the condition of the road in the given image?”.
• Yes/No type of question: For this type of question, the answer will be either a ‘Yes’ or a ‘No’. For example: “Is there any flooded road?”.

Citation

@article{rahnemoonfar2020floodnet,
 title={FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding},
 author={Rahnemoonfar, Maryam and Chowdhury, Tashnim and Sarkar, Argho and Varshney, Debvrat and Yari, Masoud and Murphy, Robin},
 journal={arXiv preprint arXiv:2012.02951},
 year={2020}
}