GRP1 CS174 BM1 RAINING DATA

## Overview

GRP1 CS174 BM1 RAINING DATA is a dataset for object detection tasks - it contains Helmet Backlight annotations for 227 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 Synthetic Rain Datasets consists of 13,712 clean-rain image pairs gathered from multiple datasets (Rain14000, Rain1800, Rain800, Rain12). With a single trained model, evaluation could be performed on various test sets, including Rain100H, Rain100L, Test100, Test2800, and Test1200.

PSNR and SSIM are computed on Y-channel in YCbCr color space.

The AAU VIRADA dataset contains a total of 215 hours of surveillance video from two different locations in Aalborg, Denmark. The purpose of the dataset is to enable the use of surveillance cameras as surrogate rain gauges.

The ground truth precipitation data comes from two disparate sources: 1) mechanical, tipping-bucket rain gauges and 2), an advanced laser disdrometer.

The footage from the first location, Crossing1, is split into a training (trn) and a validation (val) split. The footage from the second location, Crossing2, is used entirely for testing.

More details on the setup is found in the CVPR Workshop paper:

Haurum, Joakim Bruslund, Chris Holmberg Bahnsen, and Thomas B. Moeslund. "Is it Raining Outside? Detection of Rainfall using General-Purpose Surveillance Cameras." 2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2019.

Rain.night.car

## Overview

Rain.night.car is a dataset for object detection tasks - it contains Pose annotations for 237 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).

GRP1 CS174 BM1 NON RAINING DATA

## Overview

GRP1 CS174 BM1 NON RAINING DATA is a dataset for object detection tasks - it contains Motorcycles Fq3Q annotations for 328 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).

New conclusions about this problem in the paper. √main parameters-speed of rain and body. angle of rain and body(new). body size. and density and volume of rain drops, distance and time.(new) New conclusions about this problem in the paper.√main parameters-speed of rain and body. angle of rain and body(new). body size.and density and volume of rain drops, distance and time.(new)New conclusions about this problem in the paper.√main parameters-speed of rain and body. angle of rain and body(new). body size.and density and volume of rain drops, distance and time.(new)(1) When going the same distance1. When it rains from the front1-1 When the shape of your body is a box-cube,1-1-1 Angle of body leaning forward > Critical angleRun as fast as you can!1-1-2 Body tilted forward = critical angleAbove a certain speed, the number of raindrops hit when running at the speed of light is exactly the same as the number of raindrops hit when running at the speed of an airplane.1-1-3 Angle of body leaning forward < Critical angleLean forward and run slightly faster than the speed of the rain. Running at the speed of light doesn't mean getting less rain.1-2 When your body is oval or cylindrical,To get the least amount of rain, you should lean forward and run slightly faster than the speed of the rain.2. When it's raining from behind2-1 When the shape of your body is a box-cube,2-1- 1 If you run with your body upright, you must run at the horizontal speed of the rain.2-1-2 To get the least amount of rain, you must lean forward and run a little faster than the horizontal speed of the rain.2-2 When your body is in the shape of an oval,To get the least amount of rain, you have to lean forward and run a little faster than the horizontal speed of the rain.(2) When you have to travel the same amount of time (for example, when you have to travel in the rain for 10 minutes)1. When it rains from the frontYou should lean in the direction of the rain and not move.2. When it's raining from behindInterestingly, regardless of the shape of the object, you have to stand upright and move at the horizontal speed of the rain for the fewest number of raindrops to hit you (the difference from walking the same distance is that when you walk the same distance and when the rain falls from behind, you To get the least amount of rain, you must lean forward and run a little faster than the horizontal speed of the rain.)3. In a three-dimensional space, for example, when rain is pouring out sideways, the optimal speed according to the angle of the body is slightly different from that in a two-dimensional space. Interestingly, however, when you should move at any given time and when the rain falls backwards, regardless of the shape of the object, as in two-dimensional space, the least amount of raindrops will be hit by keeping the body upright and moving at the horizontal speed of the rain.4. In this paper, not only moving at a constant speed but also moving with acceleration were considered.

Raining statistics

This data release contains time-lapse imagery taken at U.S. Geological Survey (USGS) stream gaging stations with associated hydrologic and meteorological data related to each image. These data are to help improve the development of models in detecting water elevation at a given stream gaging station. Images of the water surface and surroundings at USGS stream gaging stations were taken at varying time intervals ranging between every five minutes to an hour. Cameras used include trail cameras, web cameras, and the custom river imagery sensing (RISE) camera. Time-lapse images for each USGS stream gaging station are provided in compressed files (file extension .7z). These files are named in a format to identify the USGS stream gaging station’s site number and station name. Hydrologic and meteorological data including stage, discharge, gage height, water surface elevation (or level), precipitation, relative humidity, water and/or air temperature, dew point, air pressure, and other weather condition information associated with each image are provided in comma-separated values files (file extension .csv). These files are named for users to easily relate the time-lapse images provided in the compressed files to the associated data using the same file naming convention. The hydrologic and meteorological data associated to each image is not the same and varies for each stream gaging station.

This dataset has been released as a one-off extra dataset to supplement the existing rain gauge rainfall data for the FloodHack16 hack event held at ODI Leeds on 11 & 12 March 2016.

Information

• Every time 0.2mm of rain has fallen, the time stamp is recorded. This means that the closer the time stamp, the harder it is raining.

In 2023, there were about *** rainy days in Singapore. The country has a tropical climate, with a relatively constant temperature range all year round.

Most outdoor vision systems can be influenced by rainy weather conditions. We present a single-image rain removal method, called ResDerainNet.The proposed network can automatically detect rain streaks and remove them. Based on the deep convolutional neural networks (CNN), we learn the mapping relationship between rainy and residual images from data. Furthermore, for training, we synthesize rainy images considering various rain models. Specifically, we mainly focus on the composite models as well as orientations and scales of rain streaks. In summary, we make following contributions; - A residual deep network is introduced to remove rain noise. Unlike the plane deep network which learns the mapping relationship between noisy and clean images, we learn the relationship between rainy and residual images from data. This speeds up the training process and improves the de-raining performance.

• An automatic rain noise generator is introduced to obtain synthetic rain noise. Most de-raining methods create rain noise by using Photoshop. Since synthetic rain noise has many parameters, it is difficult to automatically adjust these parameters. In our method, we can easily change some parameters on MATLAB, which saves time and effort to get natural rain noise.

• A combination of linear additive composite model and screen blend model is proposed to make synthetic rainy images. In order for the training network to be applicable to a wide range of rainy images, only one composite model is not enough. Our experimental results show that a combination of these models achieves better performance than using either model.

Learning Rain Location Prior for Nighttime Deraining (ICCV2023)

Fan Zhang, Shaodi You, Yu Li, Ying Fu

Github | Paper

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17592922%2Fcf1ecfda4728e46f4527df49e084537a%2FQQ20231203181309.gif?generation=1701598398912732&alt=media%20=640x360" alt="">

This is the data collected in ICCV2023 paper titled "Learning Rain Location Prior for Nighttime Deraining" by Fan Zhang, et.al., which is a new collection of GTAV-NightRain dataset.

In this new version, we collected 5000 rainy images paired with 500 clean images for the training set, and 500/100 for the test set. Each clean image corresponds to 10/5 rainy images. The image resolution is 1920x1080.

If you find this data useful, please consider citing our papers: ``` @inproceedings{zhang2023learning, title={Learning Rain Location Prior for Nighttime Deraining}, author={Zhang, Fan and You, Shaodi and Li, Yu and Fu, Ying}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages={13148--13157}, year={2023} }

@article{zhang2022gtav, title={GTAV-NightRain: Photometric Realistic Large-scale Dataset for Night-time Rain Streak Removal}, author={Zhang, Fan and You, Shaodi and Li, Yu and Fu, Ying}, journal={arXiv preprint arXiv:2210.04708}, year={2022} } ```

Training dataset for nodes between 10-15 nodes with throughput labels. The graphs are generated by the SNR-BA [1] model with nodes scattered uniformly over a grid the size of north america with mimum distances between nodes set to 100km. The throughput labels are generated by maximising the routing and wavelength assignment by a integer linear programming formulation at zero blocking and calculating the physical layer impairements via the gaussian noise model.

We generate a synthesized dataset, namely VRDS, with 102 rainy videos from diverse scenarios, and each video frame has the corresponding rain streak map, raindrop mask, and the underlying rain-free clean image (ground truth). This dataset serves as a valuable resource for researchers in this field to develop and test novel methods for the removal of rain streaks and raindrops from video data. To enable our model to cope with various lighting conditions, we considered different weather scenarios, particularly cloudy conditions due to the close correlation between cloudy and rainy conditions. All of the scenarios are present in both the training and test sets, thereby allowing for fair and accurate comparisons between different methods on our dataset. We captured a total of 102 videos, 72 of which were used for training and 30 for testing. The selected video resolution is 1280$\times$720, and each contains 100 frames.

Paper: Link

Website: Github

Rainy Days

## Overview

Rainy Days is a dataset for object detection tasks - it contains Vehicles annotations for 295 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).

LLM Fine-Tuning Dataset - 4,000,000+ logs, 32 languages

The dataset contains over 4 million+ logs written in 32 languages and is tailored for LLM training. It includes log and response pairs from 3 models, and is designed for language models and instruction fine-tuning to achieve improved performance in various NLP tasks - Get the data

  Models used for text generation:

GPT-3.5 GPT-4 Uncensored GPT Version (is not included inthe sample)

  Languages in the… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/llm-training-dataset.

The number of rainy days recorded for the summer of 2024 in South Korea was **. The summer of 2020 marked a five-year high in terms of recorded rainy days in the country.

Number of Rainy Days: West Nusa Tenggara data was reported at 179.000 Day in 2017. This records a decrease from the previous number of 213.000 Day for 2016. Number of Rainy Days: West Nusa Tenggara data is updated yearly, averaging 162.000 Day from Dec 2006 (Median) to 2017, with 11 observations. The data reached an all-time high of 220.000 Day in 2010 and a record low of 25.000 Day in 2006. Number of Rainy Days: West Nusa Tenggara data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Agriculture Sector – Table ID.RIH004: Number of Rainy Days: by Province.

Data Description

We release the training dataset of ChatQA. It is built and derived from existing datasets: DROP, NarrativeQA, NewsQA, Quoref, ROPES, SQuAD1.1, SQuAD2.0, TAT-QA, a SFT dataset, as well as a our synthetic conversational QA dataset by GPT-3.5-turbo-0613. The SFT dataset is built and derived from: Soda, ELI5, FLAN, the FLAN collection, Self-Instruct, Unnatural Instructions, OpenAssistant, and Dolly. For more information about ChatQA, check the website!

  Other… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/ChatQA-Training-Data.

Number of Rainy Days: Central Sulawesi data was reported at 243.000 Day in 2017. This records an increase from the previous number of 197.000 Day for 2016. Number of Rainy Days: Central Sulawesi data is updated yearly, averaging 197.000 Day from Dec 2006 (Median) to 2017, with 9 observations. The data reached an all-time high of 953.400 Day in 2007 and a record low of 68.000 Day in 2015. Number of Rainy Days: Central Sulawesi data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Agriculture Sector – Table ID.RIH004: Number of Rainy Days: by Province.