WD

## Overview

WD is a dataset for object detection tasks - it contains Weapons annotations for 2,451 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).

Cricket Ball Detection for YOLOv8: Train Like a Pro! 🏏

Sharpen your Cricket AI: Unleash the power of YOLOv8 for precise cricket ball detection in images and videos with this comprehensive dataset.

Fuel Your Custom Training: Build a robust cricket ball detection model tailored to your specific needs. This dataset, featuring 1778 meticulously annotated images in YOLOv8 format, serves as the perfect launchpad.

Dive into Diversity:

In-Action Balls: Train your model to identify cricket balls in motion, capturing deliveries, fielding plays, and various gameplay scenarios.

Lighting Variations: Adapt to diverse lighting conditions (day, night, indoor) with a range of images showcasing balls under different illumination.

Background Complexity: Prepare your model for real-world environments. The dataset includes images featuring stadiums, practice nets, and various background clutter.

Ball States: Train effectively with images of new and used cricket balls, encompassing varying degrees of wear and tear.

Unlock Potential Applications:

Real-time Cricket Analysis: Power applications for in-depth player analysis, ball trajectory tracking, and automated umpiring systems.

Enhanced Broadcasting Experiences: Integrate seamless ball tracking, on-screen overlays, and real-time highlights into cricket broadcasts.

Automated Summarization: Streamline cricket video processing for automated highlight reels, focusing on key ball-related moments.

Who Should Use This Dataset:

• Computer vision researchers and developers seeking to leverage YOLOv8 for object detection in sports applications.
• Cricket enthusiasts and data scientists passionate about building AI-powered cricket analytics tools.
• Anyone venturing into custom object detection models for cricket analysis or sports technology projects.

The goal of this project is to create a specialized model for detecting and recognizing specific wild animals, including Elephant, Gorilla, Giraffe, Lion, Tiger, and Zebra. We gathered images of these animals and used the Roboflow annotation tool to manually label each animal class. After annotation, the data was exported in the YOLOv8 format.

Next, we trained a custom YOLOv8 model on this dataset to accurately detect and recognize the selected animal species in images. The project leverages YOLOv8’s object detection capabilities to improve detection accuracy for wildlife monitoring and research purposes.

You can find more details about the project on GitHub by clicking on this link. To view the training logs and metrics on wandb, click here.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F23345571%2F4471e4ade50676d782d4787f77aa08ad%2F1000_F_256252609_6WIHRGbpzSaVQwioubxwgXdSJTNONNcK.jpg?generation=1739209341333909&alt=media" alt="">

This dataset contains 2,700 images focused on detecting potholes, cracks, and open manholes on roads. It has been augmented to enhance the variety and robustness of the data. The images are organized into training and validation sets, with three distinct categories:

• Potholes: class 0
• Cracks: class 1
• Open Manholes: class 2

Included in the Dataset: - Bounding Box Annotations in YOLO Format (.txt files) - Format: YOLOv8 & YOLO11 compatible - Purpose: Ready for training YOLO-based object detection models

• Folder Structure Organized into:

  • train/ folder
  • valid/ folder
  • Class-specific folders
  • An all_classes/ folder for combined access Benefit: Easy access for training, validation, and augmentation tasks

• Dual Format Support

  • COCO JSON Annotations Included -Compatible with models like Faster R-CNN Enables flexibility across different object detection frameworks

• Use Cases Targeted

  • Model training
  • Model testing
  • Custom data augmentation
  • Specific focus: Road safety and infrastructure detection

Here's a clear breakdown of the folder structure:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F23345571%2F023b40c98bf858c58394d6ed2393bfc3%2FScreenshot%202025-05-01%20202438.png?generation=1746109541780835&alt=media" alt="">

Data Structure

The repository provides the following subdirectories:

• images: all images included in the TimberVision dataset
• labels: annotations corresponding to each image in https://docs.ultralytics.com/datasets/segment/" target="_blank" rel="noopener">YOLOv8 instance-segmentation format
• labels_eval: additional annotations
  • mot: ground-truth annotations for multi-object-tracking evaluation in custom format
  • timberseg: custom annotations for selected images from the https://data.mendeley.com/datasets/y5npsm3gkj/2" target="_blank" rel="noopener">TimberSeg dataset
• videos: complete video files used for evaluating multi-object-tracking (annotated keyframes sampled from each file are included in the images and labels directories)
• scene_parameters.csv: annotations of four scene parameters for each image describing trunk properties and context (see the https://arxiv.org/pdf/2501.07360v1" target="_blank" rel="noopener">paper for details)
• train/val/test.txt: original split files used for training, validation and testing of oriented-object-detection and instance-segmentation models with YOLOv8
• sources.md: references and licenses for images used in the open-source subset

Subsets

TimberVision consists of multiple subsets for different application scenarios. To identify them, file names of images and annotations include the following prefixes:

• tvc: core dataset recorded in forests and other outdoor locations
• tvh: images depicting harvesting scenarios in forests with visible machinery
• tvl: images depicting loading scenarios in more structured environments with visible machinery
• tvo: a small set of third-party open-source images for evaluating generalization
• tvt: keyframes extracted from videos at 2 fps for tracking evaluation

Citing

If you use the TimberVision dataset for your research, please cite the original paper:

Steininger, D., Simon, J., Trondl, A., Murschitz, M., 2025. TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

Contains Images from Call of Duty Modern Warfare & Warzone gameplay and has labels for Enemy and Head.

Originally used to train a Yolov5 model to detect when enemies are in view and used a GIMX adapter with Python to send movement controls to connected PS4. Find the complete code on my Github.

This dataset can be used to train custom Computer Vision models to recognize when enemy players appear and locate them.

Checkout this video of the model running on a Twitch streamer's video (Faze Testy): https://youtu.be/cxFpTIK8aYE

Datasets:
The HeinSight4.0 dataset comprises 3801 images of chemical experiments conducted in laboratory settings, primarily involving transparent vessels. It classifies chemical phases into five categories:

• Air:
  o Empty: Clear air above the liquid level.
  o Residue: Air contaminated with solid deposits.
• Liquid:
  o Homogeneous Layer: Clear solutions.
  o Heterogeneous Layer: Cloudy or turbid liquids.
• Solid:
  o Solid: Particles or deposits either suspended in liquid or forming a distinct phase.

The images were extracted from videos capturing dynamic chemical processes, enriching the dataset to handle diverse phase behaviors such as dissolution, melting, mixing, settling, and more. Additionally, a vessel dataset containing 6493 images is included. This dataset incorporates images from the HeinSight3.0 dataset, supplemented with new images of reactors and vessels, to enhance detection across a variety of laboratory equipment and setups.
All images were manually annotated, with bounding boxes marking the regions of chemical phases and their respective classifications. The dataset is split into a 90:10 train/validation.

Models:
Two models were trained on the custom HeinSight4.0 dataset using the YOLOv8 architecture, fine-tuned from pretrained models on the COCO dataset. Included in this release are:
• Model weights.
• Training parameters.
• Evaluation metrics.

Code and Usage:
The models and datasets can be utilized via the associated codebase, available at https://gitlab.com/heingroup/heinsight4.0