Omkar2118/datasets huggingface dataset:
Various URL Datasets
Full name: https://github.com/ada-url/url-various-datasets

## Various URL Datasets

These are collections of URLs for  benchmarking purposes.

- files/node_files.txt: all source files from a given Node.js snapshot as URLs (43415 URLs).
- files/linux_files.txt: all files from a Linux systems as URLs (169312 URLs).
- wikipedia/wikipedia_100k.txt: 100k URLs from a snapshot of all Wikipedia articles as URLs (March 6th 2023)
- others/kasztp.txt: test URLs from https://github.com/kasztp/URL_Shortener (MIT License) (48009 URLs).
- others/userbait.txt : test URLs from https://github.com/userbait/phishing_sites_detector (unknown copyright) (11430 URLs).
- top100/top100.txt: crawl of  the top visited 100 websites and extracts unique URLs

**Disclaimer**: This repository is developed and released for research purposes only. 
  - This project reshares some publicly available datasets. When in doubt, investigate the copyright of the files you want to use. 
  - There may be errors and duplicates in these files.

Introducing paper: Parsing Millions of URLs per Second
NPO
Full name: Negative and Positive Obstacles

The dataset is recorded with an on-vehicle ZED stereo camera in both urban and rural environments

The dataset contains various lighting conditions, such as normal lights, large-area shadows, dim lights, and sun glare. There are also different weather conditions, such as sunny, cloudy, and
snowy.

Negative obstacles (i.e., potholes and cracks) and positive obstacles (i.e., pedestrians, cars, and motorcycles) in 5, 000 images are labelled
NC4K

As far as we know, there only exists one large camouflaged object testing dataset, the COD10K, while the sizes of other testing datasets are less than 300. We then contribute another camouflaged object testing dataset, namely NC4K, which includes 4,121 images downloaded from the Internet. The new testing dataset can be used to evaluate the generalization ability of existing models.

Introducing paper: Simultaneously Localize, Segment and Rank the Camouflaged Objects
COD10K
Full name: Camouflaged/Concealed Object Detection

Sensory ecologists have found that this s background matching camouflage strategy works by deceiving the visual perceptual system of the observer. Naturally, addressing concealed object detection (COD) requires a significant amount of visual perception knowledge. Understanding COD has not only scientific value in itself, but it also important for applications in many fundamental fields, such as computer vision (e.g., for search-and-rescue work, or rare species discovery), medicine (e.g., polyp segmentation, lung infection segmentation), agriculture (e.g., locust detection to prevent invasion), and art (e.g., recreational art). The high intrinsic similarities between the targets and non-targets make COD far more challenging than traditional object segmentation/detection. Although it has gained increased attention recently, studies on COD still remain scarce, mainly due to the lack of a sufficiently large dataset and a standard benchmark like Pascal-VOC, ImageNet, MS-COCO, ADE20K, and DAVIS.

To build the large-scale COD dataset, we build the COD10K, which contains 10,000 images (5,066 camouflaged, 3,000 background, 1,934 noncamouflaged), divided into 10 super-classes, and 78 sub-classes (69 camouflaged, nine non-camouflaged) which are collected from multiple photography websites.

Introducing paper: Camouflaged Object Detection
Camouflaged Animal Dataset
Full name: Camouflaged Animal Dataset

The nine (moving camera) videos in this benchmark exhibit camouflaged animals that are difficult to see in a single frame, but can be detected based upon their motion across frames.

Introducing paper: It's Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videos
MoCA-Mask
Full name: Moving Camouflaged Animals (MoCA)-Mask

The original Moving Camouflaged Animals (MoCA) Dataset includes 37K frames from 141 YouTube Video sequences with resolution and sampling rate of 720 × 1280 and 24fps in the majority of cases. The dataset covers 67 types of animals moving in natural scenes, but some are not camouflaged animals. Also, the ground truth of the original dataset is bounding boxes rather than dense segmentation masks, which makes it hard to evaluate the VCOD segmentation performance. To this end, we reorganize the dataset as MoCA-Mask and build a comprehensive benchmark with more comprehensive evaluation criteria.

Introducing paper: Implicit Motion Handling for Video Camouflaged Object Detection