Ultralytics

## Overview

Ultralytics is a dataset for object detection tasks - it contains Training Yolo5 annotations for 323 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).

MONAI Ultralytics

## Overview

MONAI Ultralytics is a dataset for classification tasks - it contains Nuclei Pathology annotations for 9,865 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).

A Dataset containing annotated images of human and vehicles for training with Ultralytics Yolov8

The dataset was generated using the Roboflow platform. The annotations are compatible with the PyTorch YOLOv5 architecture.

Dataset details:

# Images: 1220 images

Image Split:

Train / Test Split: 92

Training Set: 1.1k

Preprocessing

Auto-Orient: Applied

Resize: Stretch to 416x416

Augmentations

Outputs per training example: 5

Flip: Horizontal, Vertical

Crop: 0% Minimum Zoom, 49% Maximum Zoom

Grayscale: Apply to 47% of images

Hue: Between -25° and +25°

Saturation: Between -42% and +42%

Exposure: Between -22% and +22%

Blur: Up to 3.25px

Cutout: 8 boxes with 10% size each

Mosaic: Applied

Details

Version Name: 2022-07-24 12:50am

Version ID: 1

Generated: Jul 24, 2022

Object_Detection_Ultralytics

## Overview

Object_Detection_Ultralytics is a dataset for object detection tasks - it contains Face Mask annotations for 838 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 crayfish dataset was generated using the Roboflow platform. The annotations are compatible with the PyTorch YOLOv5 architecture.

Dataset details:

Training Set: 92%, 1.2k images

Validation Set: 5%, 69 images

Testing Set: 3%, 35 images

Dimensions: 416x416

Augmentations:

Flip: Horizontal

90° Rotate: Clockwise, Counter-Clockwise

Crop: 0% Minimum Zoom, 20% Maximum Zoom

Grayscale: Apply to 50% of images

Hue: Between -48° and +48°

Saturation: Between -25% and +25%

Brightness: Between -25% and +25%

Exposure: Between -25% and +25%

Blur: Up to 10px

Noise: Up to 5% of pixels

Cutout: 3 boxes with 10% size each

Mosaic: Applied

TFG_Ultralytics_Dataset

## Overview

TFG_Ultralytics_Dataset is a dataset for object detection tasks - it contains Traffic Signs Final EbvC annotations for 1,228 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).

Wallet Keys Glasses Detection

## Overview

Wallet Keys Glasses Detection is a dataset for object detection tasks - it contains Wallet Keys Glasses annotations for 2,020 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).

RugbyEvents

## Overview

RugbyEvents is a dataset for object detection tasks - it contains YOLOv8 Ultralytics annotations for 9,392 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).

Vape Detector

## Overview

Vape Detector is a dataset for object detection tasks - it contains Smoke annotations for 10,650 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).

Started out as a pumpkin detector to test training YOLOv5. Now suffering from extensive feature creep and probably ending up as a cat/dog/spider/pumpkin/randomobjects-detector. Or as a desaster.

The dataset does not fit https://docs.ultralytics.com/tutorials/training-tips-best-results/ well. There are no background images and the labeling is often only partial. Especially in the humans and pumpkin category where there are often lots of objects in one photo people apparently (and understandably) got bored and did not labe everything. And of course the images from the cat-category don't have the humans in it labeled since they come from a cat-identification model which ignored humans. It will need a lot of time to fixt that.

Dataset used: - Cat and Dog Data: Cat / Dog Tutorial NVIDIA Jetson https://github.com/dusty-nv/jetson-inference/blob/master/docs/pytorch-cat-dog.md © 2016-2019 NVIDIA according to bottom of linked page - Spider Data: Kaggle Animal 10 image set https://www.kaggle.com/datasets/alessiocorrado99/animals10 Animal pictures of 10 different categories taken from google images Kaggle project licensed GPL 2 - Pumpkin Data: Kaggle "Vegetable Images" https://www.researchgate.net/publication/352846889_DCNN-Based_Vegetable_Image_Classification_Using_Transfer_Learning_A_Comparative_Study https://www.kaggle.com/datasets/misrakahmed/vegetable-image-dataset Kaggle project licensed CC BY-SA 4.0 - Some pumpkin images manually copied from google image search - https://universe.roboflow.com/chess-project/chess-sample-rzbmc Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/steve-pamer-cvmbg/pumpkins-gfjw5 Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/nbduy/pumpkin-ryavl Provided by a Roboflow user License: CC BY 4.0 - https://universe.roboflow.com/homeworktest-wbx8v/cat_test-1x0bl/dataset/2 - https://universe.roboflow.com/220616nishikura/catdetector - https://universe.roboflow.com/atoany/cats-s4d4i/dataset/2 - https://universe.roboflow.com/personal-vruc2/agricultured-ioth22 - https://universe.roboflow.com/sreyoshiworkspace-radu9/pet_detection - https://universe.roboflow.com/artyom-hystt/my-dogs-lcpqe - license: Public Domain url: https://universe.roboflow.com/dolazy7-gmail-com-3vj05/sweetpumpkin/dataset/2 - https://universe.roboflow.com/tristram-dacayan/social-distancing-g4pbu - https://universe.roboflow.com/fyp-3edkl/social-distancing-2ygx5 License MIT - Spiders: https://universe.roboflow.com/lucas-lins-souza/animals-train-yruka

Currently I can't guarantee it's all correctly licenced. Checks are in progress. Inform me if you see one of your pictures and want it to be removed!

Lanternflies

## Overview

Lanternflies is a dataset for classification tasks - it contains Lanternflies annotations for 710 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).

https://www.youtube.com/watch?v=4MA_6oZQz7s&ab_channel=tektronix475

Spotted caps, are the normal OK class (fully closed). Clean caps, are the bad or anomally target class (partially closed). One double prediction at 3:59. 100x100 classification accuracy, out of 200 samples. Inference over unseen test dataset. 150 epochs training. 700 samples training dataset, no data augmentation.

PREPROCESSING Auto-Orient: Applied Resize: Stretch to 416x416 Grayscale: Applied AUGMENTATIONS No augmentations were applied.

Anomaly detection with: Roboflow, tensorflow, google colab, Ultralytics, yolo v5, cvat,

Match Attax

## Overview

Match Attax is a dataset for object detection tasks - it contains Cards annotations for 340 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).

🏏 Bowling Analysis & Ball Tracking Project 📌 Project Overview This project focuses on real-time ball and stump detection in cricket videos. The goal is to provide an AI-powered analysis of bowling performance by tracking the ball's speed, trajectory, and movement. The project leverages YOLOv8 for object detection and uses custom datasets to improve accuracy. The final application will be a Flutter-based mobile app, enabling seamless video analysis for bowlers at all levels.

🎯 Class Descriptions The dataset includes 5 key classes for training the YOLOv8 model: Ball - Tracks the cricket ball’s movement. Stumps - Detects all three stumps together. Stump - Detects individual stumps. Person - Identifies players in the video. Bat - Detects the presence of the cricket bat. These annotations are manually labeled using LabelImg and formatted for YOLOv8 training.

📅 Current Status & Timeline ✅ Project Setup & Planning (✅ Completed) ✅ Dataset Collection (✅ Completed) ✅ Data Annotation & Labeling (✅ Completed) ✅ YOLOv8 Model Training (🔄 Testing Different Configurations) 🚀 Final Model Optimization & Evaluation (🔜 Next Step) 📱 Flutter App Development & Integration (🔜 Final Phase)

🔗 External Resources 📺 YouTube Tutorial Followed: YOLOv8 Object Detection Guide 📄 Project Blog Updates: Bowling Analysis Blog 📂 GitHub Repository (To be added) 📖 YOLOv8 Documentation: Ultralytics YOLOv8 Docs 📝 Contribution & Labeling Guidelines

Dataset Standardization: Ensure class labels match across all datasets before training. Annotation Format: Use YOLO format [class_id x_center y_center width height] in .txt files. Merging Datasets: Keep consistent class IDs across multiple datasets to avoid conflicts. Training Best Practices: Train on diverse clips for better model generalization

This project uses a Raspberry Pi equipped with the official Pi Camera and a custom-trained YOLOv8 nano model to detect crickets in real time. It leverages picamera2, OpenCV, and the ultralytics library to capture video frames, run inference, and display bounding boxes over detected insects. After detection, a mechanical flipper arm—similar to those found in pinball machines—is triggered to sort the crickets into two separate boxes based on their predicted sex (male or female).

https://drive.google.com/uc?id=1x6OsMmimLrwrYwiNm9EuIDh0-GThLik-" alt="">

Project Overview: The Caridina and Neocaridina Shrimp Detection Project aims to develop and improve computer vision algorithms for detecting and distinguishing between different shrimp varieties. This project is centered around aquarium fish keeping hobbyist and how computer vision can be beneficial to improving the care of dwarf shrimp. This project will focus on zoning a feeding area and tracking and counting caridina shrimp in area.

Caridina and neo-caridina shrimp are two distinct species that require different water parameters for optimal health. Neocaridina shrimp are generally more hardy and easier to keep than caridina species, while caridina shrimp are known for their striking distinctive patterns. The body structure of both species are similar. However, there are specific features that should allow enough sensitivity to at least distinguish between caridina shrimp.

Descriptions of Each Class Type: The dataset for this project includes thirteen different class types. The neo-caridina species have been grouped together to test if the model can distinguish between caridina and neo-caridina shrimp. The remaining classes are all different types of caridina shrimp.

The RGalaxyPinto and BGalaxyPinto varieties are caridina shrimp, with the only difference being their color: one is wine-red while the other dark-blue-black. Both varieties have distinctive spots on the head region and stripes on their backs, making them ideal for testing the model's ability to distinguish between color.

https://drive.google.com/uc?id=19zPYu8YbCiRHUF9K_3kCsyw0X2Tog-Ts" alt="">	https://drive.google.com/uc?id=1Ay728IysDP8yMCwPEi743Bp6mnq5Xrix" alt="">
https://drive.google.com/uc?id=1Asa3DwuWop5UDpBThHgGG6otBSyXgJTV" alt="">

The CRS-CBS Crystal Red Shrimp and Crystal Black Shrimp have similar patterns to the Panda Bee shrimp, but the hues are different. Panda shrimp tend to be a deeper and richer color than CRS-CBS shrimp, CRS-CBS tend to have thicker white rings.

https://drive.google.com/uc?id=1AXlBcHGGZ9VEnNuoxeEFZf0DTPQa5hTR" alt="">	https://drive.google.com/uc?id=1BO2DwW77AqzDrj3xP9VOEYOXSP4wgRzz" alt="">
https://drive.google.com/uc?id=19yO42UW_ai11Da3KgaEiUEHn0OnJc0As" alt="">

The Panda Bee variety, on the other hand, is known for its panda-like pattern white and black/red rings.The color rings tend to be thicker and more pronounced than the Crystal Red/Black Shrimp.

Within the Caridina species, there are various tiger varieties. These include Fancy Tiger, Raccoon Tiger, Tangerine Tiger, Orange Eyed Tiger (Blonde and Full Body). All of these have stripes along the sides of their bodies. Fancy Tiger shrimp have a similar color to CRS, but with a tiger stripe pattern. Raccoon Tiger and Orange Eyed Tiger Blonde look very similar, but the body of the Raccoon Tiger appears larger, and the Orange Eyed Tiger is known for its orange eyes. Tangerine Tigers vary in stripe pattern and can often be confused with certain neo-caridina, specifically yellow or orange varieties.

https://drive.google.com/uc?id=1APx9jQ5WUdPbv1US8ihOEBpVBjvhN0Z3" alt="">	https://drive.google.com/uc?id=1B6MbiN9FY9fomf6-P6zy-jkoGJKEiXlW" alt="">	https://drive.google.com/uc?id=1A3qYXbPkqjeK2oCJfSLAPwEsEZN9nw8NN" alt="">
https://drive.google.com/uc?id=19ukHly3uZ05FeGdW_hVBWwlHRFvgnMMC" alt="">	https://drive.google.com/uc?id=1AztJj471aIWcRYHNC1lrJse7raO2dUqm" alt="">

The remaining are popular favorites for breeding and distinct color patterns namely Bluebolt, Shadow Mosura, White Bee/Golden Bee, and King Kong Bee.

https://drive.google.com/uc?id=19yEpuJ6ENmkcImu0OfCzliITP_UnCNoM" alt="">	https://drive.google.com/uc?id=19uglS20nyTSi-_b1ls8f09cIuJUHOpSm" alt="">
https://drive.google.com/uc?id=1AbbCVRnlIQL1MlqY3MJnX9t2WVdyq2zJ" alt="">

Links to External Resources: Here are some resources that provide additional information on the shrimp varieties and other resources used in this project:

Caridina Shrimp: https://en.wikipedia.org/wiki/Bee_shrimp
Neo-Caridina Shrimp: https://en.wikipedia.org/wiki/Neocaridina
  Roboflow Polygon Zoning/Tracking/Counting:https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-detect-and-count-objects-in-polygon-zone.ipynb
  Roboflow SAM: https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-segment-anything-with-sam.ipynb
  Ultralytics Hub:https://github.com/ultralytics/hub