Conserved Domain Database (CDD) is a protein annotation resource that consists of a collection of well-annotated multiple sequence alignment models for ancient domains and full-length proteins.

Dataset

This dataset was created by ai131452151!

Contents

DENT CDD

## Overview

DENT CDD is a dataset for object detection tasks - it contains Dent annotations for 435 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).

CDD

## Overview

CDD is a dataset for object detection tasks - it contains Container Damage annotations for 2,158 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).

CDD & CloudNet: A Benchmark Dataset & Model for Object Detection Performance

Overview

Welcome to the CloudNet repository. This project provides a cloud detection dataset and a pre-trained model designed to enhance object detection accuracy in remote sensing aerial images, particularly in challenging cloud-covered scenarios. The dataset comprises two classes: cloud and non-cloud images, sourced from the publicly available Maxar "Hurricane Ian" repository.

CloudNet Dataset

Description

The CloudNet dataset consists of cloud and non-cloud images, facilitating research in cloud detection for object detection in remote sensing imagery.

Dataset Structure

• cloud_images: Contains images with clouds.
• non_cloud_images: Includes images without clouds.

CloudNet Model

Description

The CloudNet model is a pre-trained model specifically designed for cloud detection in remote sensing imagery. It is trained on the CloudNet dataset and serves as a valuable tool for enhancing object detection accuracy in the presence of clouds.

Model Weights

You can download the pre-trained CloudNet model weights from the following link: CloudNet Model Weights

Citation

If you find the CloudNet dataset or model useful in your research, please cite our work using the following BibTeX entry:

@INPROCEEDINGS{10747011,
 author={Haque, Mohd Ariful and Rifat, Rakib Hossain and Kamal, Marufa and George, Roy and Gupta, Kishor Datta and Shujaee, Khalil},
 booktitle={2024 Fifth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)}, 
 title={CDD & CloudNet: A Benchmark Dataset & Model for Object Detection Performance}, 
 year={2024},
 volume={},
 number={},
 pages={118-122},
 abstract={Aerial imagery obtained through remote sensing is extensively utilized across diverse industries, particularly for object detection applications where it has demonstrated considerable efficacy. However, clouds in these images can obstruct evaluation and detection tasks. This study therefore involved the compilation of a cloud dataset, which categorized images into two classes: those containing clouds and those without. These images were sourced from the publicly available Maxar ‘Hurricane Ian’ repository, which contains images from various natural events. We demonstrated the impact of cloud removal during pre-processing on object detection using this dataset and employed six CNN models, including a custom model, for cloud detection benchmarking. These models were used to detect objects in aerial images from two other events in the Maxar dataset. Our results show significant improvements in precision, recall, and F1-score for CNN models, along with optimized training times for object detection in the CloudNet+YOLO combination. The findings demonstrate the effectiveness of our approach in improving object detection accuracy and efficiency in remote sensing imagery, particularly in challenging cloud-covered scenarios.},
 keywords={Training;Industries;Accuracy;Object detection;Benchmark testing;Data science;Data models;Remote sensing;Cloud Detection;Dataset;Deep Learning;CNN;ResNet;Vgg16;DenseNet169;EfficientNet;MobileNet},
 doi={10.1109/IDSTA62194.2024.10747011},
 ISSN={},
 month={Sep.},}

License

The CloudNet dataset and model are released under the License.

FINAL CDD

## Overview

FINAL CDD is a dataset for object detection tasks - it contains Damage annotations for 1,732 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 [MIT license](https://creativecommons.org/licenses/MIT).

TEMP CDD

## Overview

TEMP CDD is a dataset for object detection tasks - it contains Damage annotations for 2,538 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).

Users can display, sort, subset and download position-specific score matrices (PSSMs) either from CDD records or from Position Specific Iterated (PSI)-BLAST protein searches.

Cdd Vt 25

## Overview

Cdd Vt 25 is a dataset for object detection tasks - it contains 0 1 2 3 4 5 6 7 8 9 annotations for 214 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).

This tabular dataset contains the results reported in the indicators 'Long-term Change in Heat Energy Available for Plant Growth in B.C. (1900-2013)' and 'Long-term Change in Energy Requirements for Heating & Cooling Buildings in B.C. (1900-2013)' published in Indicators of Climate Change in British Columbia (2015-16 Update) and by Environmental Reporting BC. The analyses used climate monitoring data for B.C. available from the BC Provincial Climate Data Set. The dataset includes estimates of long-term change in growing degree days (GDD per century), heating degree days (HDD per century) and cooling degree days (CDD per century). Data are provided for British Columbia as well as for each of the nine terrestrial ecoprovinces of British Columbia.

Этот набор данных предоставляет муниципальную и местную статистику по градусам тепла в течение нескольких дней (CDDs и HDDs) Данные упорядочены на территориальном уровне 2 (TL2). Чтобы просмотреть данные на местном уровне, щелкните на соответствующем индикаторе TL2. CDD измеряют интенсивность (в °C) и продолжительность (в днях) охлаждения. Годовые CDD - это сумма разниц между среднесуточной температурой наружного воздуха за год и пороговой температурой (22°C). Аналогичным образом, жесткие диски измеряют потребности в отоплении и соответствуют сумме разниц между пороговой температурой и среднесуточной температурой наружного воздуха за год, когда температура наружного воздуха ниже пороговой температуры (15°C). Дни похолодания рассчитываются на основе данных ERA5-Land с использованием среднегодовой температуры воздуха на высоте 2 метра. Полный набор данных можно загрузить здесь: Загрузить микроданные Источники данных и методология Смотрите Местный информационный портал для получения дополнительной информации. Дополнительная информация По любым вопросам или комментариям, пожалуйста, пишите на RegionStat@oecd.org Датасет содержит следующие поля: Поток данных (DATAFLOW) — Идентификатор потока данных SDMX Страна/Регион (REF_AREA) — Код страны или региона по ISO 3166 Частота (FREQ) — Частота данных (A - годовая, Q - квартальная, M - месячная) Временной период (TIME_PERIOD) — Период времени наблюдения Значение (OBS_VALUE) — Численное значение показателя Единица измерения (UNIT_MEASURE) — Единица измерения показателя Статус наблюдения (OBS_STATUS) — Статус или качество данных This dataset provides municipal and local area statistics on Cooling and Heating Degree Days (CDDs and HDDs) The data is organized at Territorial Level 2 (TL2). To visualize the data at Local Area level, click on the corresponding TL2 indicator. CDDs measure the intensity (in °C) and the length (in days) of cooling needs. Annual CDDs are the sum over a year of the differences between the daily mean outdoor air temperature and the threshold temperature (22°C). Similarly, HDDs measure heating needs, and correspond to the sum over a year of the differences between the threshold temperature and the daily mean outdoor air temperature when the outdoor temperature is below the threshold temperature (15°C). Cooling Degree Days are assessed based on ERA5-Land using the average annual air temperature at 2 meters height. The full dataset can be downloaded here: Download Microdata

This data captures climate information and HVAC energy use for a baseline prototype home and for a replacement alternative energy home. The baseline home is a traditional DX cooling/gas furnace system, and the alternate system is a geothermal heat pump. Cooling degree days (CDD), heating degree days (HDD) and relative humidity were gathered from historical weather data for 12 cities across the contiguous United States. Geothermal heat pump coefficients were generated as inputs to EnergyPlus simulation software. These heat pump coefficients are generated by compiling heat pump performance data from 5 market leading, high efficiency residential geothermal heat pump manufacturers. These coefficients can be used to represent a general, market available heat pump in 2-ton, 3-ton, and 4-ton capacities. Baseline prototype home energy use by city was generated by EnergyPlus using the prototype home download file from www.energy.gov and the respective weather file for that city. This data can be interpreted as energy use per month by certain HVAC components. The GSHP home energy use by city was generated from EnergyPlus and the respective city weather file. The GSHP model was created by the authors to model the alternate closed loop, GSHP system.

Hons FTuning

## Overview

Hons FTuning is a dataset for classification tasks - it contains Coconut Diseases annotations for 10,779 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).

Cdd_vd_test

## Overview

Cdd_vd_test is a dataset for object detection tasks - it contains Auto Rickshaw Bicycle Car Motorc annotations for 240 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).

CDD_3classes_detection

## Overview

CDD_3classes_detection is a dataset for object detection tasks - it contains Containers annotations for 87 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).

หมู่บ้านท่องเที่ยว

Dent Training Merged 2022511

## Overview

Dent Training Merged 2022511 is a dataset for object detection tasks - it contains Dent annotations for 226 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).

Download Peraturan Badan Pengawas Perdagangan Berjangka Komoditi Nomor 10 Tahun 2021 tentang Penerimaan Nasabah Secara Elektronik Online dengan Mekanisme Customer Due Diligence (CDD) Sederhana di Bidang Perdagangan Berjangka Komoditi. File pdf ini disediakan oleh website Paralegal.id - Portal Hukum dan Peraturan Indonesia. Silakan kunjungi website kami untuk menemukan peraturan lainnya serta gunakan fitur riset hukum lainnya.