SDD dataset contains a variety of indoor and outdoor scenes, designed for Image Defocus Deblurring. There are 50 indoor scenes and 65 outdoor scenes in the training set, and 11 indoor scenes and 24 outdoor scenes in the testing set.

Dataset Sdd

## Overview

Dataset Sdd is a dataset for object detection tasks - it contains C0 C1 C2 C3 C4 C5 C6 annotations for 2,853 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

X SDD

## Overview

X SDD is a dataset for object detection tasks - it contains Steel annotations for 1,350 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The used database, which is the most recent one for strip surface defect classification. This dataset contains seven types of surface defects, namely, oxide scale of temperature the system, finishing roll printing, oxide scale of plate system, surface scratches, iron sheet ash, red iron sheet, and slag inclusions.

The dataset is constructed from images of defective production items that were provided and annotated by Kolektor Group d.o.o.. The images were captured in a controlled industrial environment in a real-world case.

The dataset consists of 399 images at 500 x ~1250 px in size.

Please cite our paper published in the Journal of Intelligent Manufacturing when using this dataset:

@article{Tabernik2019JIM, author = {Tabernik, Domen and {\v{S}}ela, Samo and Skvar{\v{c}}, Jure and Sko{\v{c}}aj, Danijel}, journal = {Journal of Intelligent Manufacturing}, title = {{Segmentation-Based Deep-Learning Approach for Surface-Defect Detection}}, year = {2019}, month = {May}, day = {15}, issn={1572-8145}, doi={10.1007/s10845-019-01476-x} }

A reference repository of all compliance documentation for SDD programs, including privacy threshold analyses (PTAs), privacy impact analyses (PIAs), Systems of Record Notices (SORN), etc. For use by SDD programs; not the record copies.

for example

DSWF/sdd dataset hosted on Hugging Face and contributed by the HF Datasets community

The SDD Detector market has witnessed steady growth in recent years and is expected to continue its upward trajectory during the forecast period of 2025-2033. The market size, valued at XXX million in 2025, is projected to reach XXX million by 2033, exhibiting a CAGR of XX% from 2025 to 2033. This growth is primarily driven by the increasing demand for SDD detectors in various applications, including nuclear physics, medical imaging, and environmental monitoring. SDD detectors offer several advantages over traditional detectors, including high energy resolution, high count rate capability, and compact size. These advantages make them well-suited for use in a wide range of applications, especially in situations where high-quality data is required. Key market players include Hitachi High-Technologies, Thermo Fisher, Burker, Ketek, Oxford, Amptek, RaySpec, PNDetector, and Mirion Technologies. The market is segmented based on application, type, and region, with North America, Europe, Asia-Pacific, and the Middle East & Africa being the major regional markets.

SSD (Sub-slot Dialog) dataset: This is the dataset for the ACL 2022 paper "A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots". arxiv

When humans navigate a crowed space such as a university campus or the sidewalks of a busy street, they follow common sense rules based on social etiquette. In order to enable the design of new algorithms that can fully take advantage of these rules to better solve tasks such as target tracking or trajectory forecasting, we need to have access to better data. To that end, we contribute the very first large scale dataset (to the best of our knowledge) that collects images and videos of various types of agents (not just pedestrians, but also bicyclists, skateboarders, cars, buses, and golf carts) that navigate in a real world outdoor environment such as a university campus. In the above images, pedestrians are labeled in pink, bicyclists in red, skateboarders in orange, and cars in green. ### CITATION If you find this dataset useful, please cite this paper (and

Dataset Card for Instruct-SkillMix-SDA

This dataset was generated by the Seed-Dataset Dependent version of the Instruct-SkillMix pipeline.

  Dataset Creation

We use GPT-4-Turbo-2024-04-09 to extract instruction-following skills from random samples of existing datasets (5,200 samples from GPT-4 generated version of the Alpaca-52K and 1,000 samples from UltraChat). We then ask GPT-4-Turbo-2024-04-09 to cluster the skills into a list of broader skill categories. We then take a… See the full description on the dataset page: https://huggingface.co/datasets/PrincetonPLI/Instruct-SkillMix-SDD.

Mobilenet SSD

## Overview

Mobilenet SSD is a dataset for object detection tasks - it contains Cat annotations for 726 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Global SDD Detector Market to grow from US$ 268.7 Million in 2023 to US$ 502.36 Million by 2032, at a CAGR of 7.2% during 2024 - 2032

Global SDD Detector market size 2025 was XX Million. SDD Detector Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

Technological advancements in the SDD Sensor Market, Outlook and Forecast 2025-2031 industry are shaping the future market landscape. The report evaluates innovation-driven growth and how emerging technologies are transforming industry practices, offering a comprehensive outlook on future opportunities and market potential.

Collins Ssd

## Overview

Collins Ssd is a dataset for object detection tasks - it contains Components annotations for 988 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Silicon Drift Detectors (SDD) for Medical Imaging Market Outlook

The Silicon Drift Detectors (SDD) for Medical Imaging market size was valued at approximately USD 350 million in 2023 and is anticipated to reach around USD 780 million by 2032, exhibiting a compound annual growth rate (CAGR) of 9.1% from 2024 to 2032. This significant growth is driven by advancements in medical imaging technology, increasing healthcare expenditure, and the rising prevalence of chronic diseases requiring advanced diagnostic tools.

One of the primary growth factors for the SDD market in medical imaging is the ever-evolving landscape of medical diagnostics and the increasing importance of accurate and early diagnosis. Silicon Drift Detectors offer unparalleled energy resolution and count rate performance, making them indispensable in various imaging applications. As healthcare providers continue to seek ways to improve diagnostic precision and patient outcomes, the demand for high-performance detectors is on the rise. This technological edge that SDDs provide over traditional detectors plays a pivotal role in their growing market adoption.

Furthermore, the rise in chronic diseases, such as cancer and cardiovascular conditions, necessitates frequent and detailed imaging, boosting the demand for advanced medical imaging equipment. Silicon Drift Detectors are instrumental in providing high-resolution images critical for the diagnosis and monitoring of these conditions. Additionally, the aging global population is contributing to the increased incidence of age-related diseases, thereby driving the demand for advanced medical imaging solutions that incorporate SDD technology.

The significant growth in healthcare expenditure, particularly in developing countries, is another driving factor. As nations invest more in their healthcare infrastructure, there is a corresponding increase in the adoption of advanced medical imaging technologies. Governments and private healthcare providers are focused on integrating cutting-edge diagnostic tools to enhance their service offerings. This trend is expected to substantially boost the market for Silicon Drift Detectors over the forecast period.

Regionally, North America and Europe currently dominate the SDD for the medical imaging market due to the presence of advanced healthcare infrastructure and high adoption rates of innovative technologies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This can be attributed to the rapidly growing healthcare sector, increased government investments in healthcare infrastructure, and rising awareness about advanced diagnostic technologies. The expanding middle class and increasing healthcare expenditure in countries like China and India are also significant contributors to this regional growth.

Product Type Analysis

The Silicon Drift Detectors market is segmented into Compact SDD and Large Area SDD based on product type. Compact SDDs are gaining traction owing to their smaller size, which makes them suitable for portable and point-of-care medical imaging devices. These detectors are particularly useful in settings that require space efficiency and high-resolution imaging capabilities. Innovations in miniaturization and integration are driving the adoption of compact SDDs in various medical imaging applications, making them a critical component in modern medical diagnostics.

Large Area SDDs, on the other hand, are preferred in applications that require extensive area coverage and high sensitivity. These detectors are suitable for more comprehensive imaging tasks, such as whole-body scans and large-scale diagnostic procedures. The demand for large area SDDs is driven by their ability to provide detailed and accurate images over a larger field of view, which is essential for the diagnosis and monitoring of complex medical conditions. Research and development in enhancing the performance and efficiency of large area SDDs are contributing to their growing adoption in the medical imaging market.

The advancements in semiconductor technology and the increasing focus on improving the performance parameters of SDDs are expected to drive significant growth in both segments. Companies are investing heavily in R&D to develop detectors that offer superior energy resolution, faster processing speeds, and enhanced durability. These innovations are expected to expand the application range of both compact and large area SDDs, further propelling market growth.

Moreover, the increasing adop

Dichotomous, pathway key to species of Microphontes developed with Lucid Builder v4 in XML Structure of Descriptive Data (SDD) format.

SequenceDistanceDataset (SDD)

A Cross-Domain Benchmark for Sequence Similarity Analysis

  Overview

A comprehensive benchmarking dataset for evaluating distance metrics in two domains:

🧬 Biological Sequences (Proteins from UniProt/UniRef)
🗺️ Movement Trajectories (GPS data from 3 cities)

Designed to support research in similarity search, metric learning, and cross-domain analysis.

  Key Features

✅ Precomputed Distance Matrices

Eliminates computation… See the full description on the dataset page: https://huggingface.co/datasets/Airchang/SDD.