Yolov8 Hand Training

## Overview

Yolov8 Hand Training is a dataset for object detection tasks - it contains 3 annotations for 1,705 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).

Train Dataset Complete

## Overview

Train Dataset Complete is a dataset for instance segmentation tasks - it contains Damage 6VC0 annotations for 1,077 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 extensive dataset is tailored for ship detection tasks utilizing the YOLOv8 object detection framework. It comprises over 80,000 high-resolution images containing various maritime scenes, captured under diverse environmental conditions and viewpoints. Each image is meticulously annotated with bounding boxes encompassing ships of different sizes, orientations, and contexts, ensuring comprehensive coverage of real-world scenarios.

The dataset is partitioned into sizable training and testing subsets, each exceeding 1 GB in size, to facilitate robust model training and evaluation. With its vast collection of annotated samples and compatibility with YOLOv8 architecture, this dataset serves as an invaluable resource for researchers, practitioners, and enthusiasts in the field of maritime object detection.

Yolov8 Training

## Overview

Yolov8 Training is a dataset for object detection tasks - it contains Objects annotations for 637 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).

DATASET SAMPLE

Duality.ai just released a 1000 image dataset used to train a YOLOv8 model in multiclass object detection -- and it's 100% free! Just create an EDU account here. This HuggingFace dataset is a 20 image and label sample, but you can get the rest at no cost by creating a FalconCloud account. Once you verify your email, the link will redirect you to the dataset page. What makes this dataset unique, useful, and capable of bridging the Sim2Real gap?

The digital twins are… See the full description on the dataset page: https://huggingface.co/datasets/duality-robotics/YOLOv8-Multiclass-Object-Detection-Dataset.

Yolov8 Weapon Train

## Overview

Yolov8 Weapon Train is a dataset for object detection tasks - it contains Gun annotations for 3,118 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).

📄 Description

Overview This dataset contains annotated images of 7 types of kitchen utensils — fork, butter knife, kitchen knife, peeler, spoon, tongs, and wooden spoon — organized into train/ and val/ sets. Each split includes subfolders images/ (JPEG/PNG files) and labels/ (YOLO-format .txt files), along with a classes.txt listing the class names mapped to indices 0–6.

Dataset Contents

• train/images/ & val/images/: Raw utensil photos
• train/labels/ & val/labels/: YOLO-format .txt annotations (one line per object: class_id x_center y_center width height, all normalized)

• classes.txt:

  fork
  butter knife
  kitchen knife
  peeler
  spoon
  tongs
  wooden spoon
  

Use Cases

• Train or fine-tune object detection models (e.g., YOLOv8, YOLOv5) on kitchen utensil recognition
• Benchmark multi‑class detection performance in indoor/home environments
• Serve as a starting point for kitchen inventory automation, robotics, and smart cooking applications

Structure and Labeling Standards

• 2, XXX images total — pre-split into train/validation
• Each image’s annotation file shares its base name and contains bounding boxes in YOLO format
• Class indices align with entries in classes.txt, ensuring compatibility with common detection frameworks

Getting Started

1. Clone or download this dataset

2. Reference the folder paths in your data.yaml:

   train: train/images
   val:  val/images
   nc: 7
   names:
    0: fork
    1: butter knife
    2: kitchen knife
    3: peeler
    4: spoon
    5: tongs
    6: wooden spoon
   

3. Train a YOLOv8 model:

   model.train(data='data.yaml', epochs=50, imgsz=640)
   

Recommended Citation / Acknowledgment If you publish research using this dataset, please mention:

“Kitchen utensil detection dataset uploaded via Kaggle by Raunak gola.”

Future Extensions

• Expand with more utensil types or larger image sets
• Support segmentation annotations
• Add real-world kitchen scene backgrounds or occluded images

Cellphone Yolov8 Training

## Overview

Cellphone Yolov8 Training is a dataset for object detection tasks - it contains Cellphone annotations for 294 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

YOLOv8 Image segmentation dataset: PELLET Casimir Marius

This dataset includes 100 images from the PELLET Casimir Marius story on Europeana. It is available in YOLOv8 format, to train a model to segment text lines and illustrations from page images. The ground truth was generated using Teklia's open-source annotation interface Callico. This work is marked with CC0 1.0. To view a copy of this license, visit https://creativecommons.org/publicdomain/zero/1.0/.

South American Flags Dataset (YOLOv8 Format)

Created by Ishan Chauhan and Miilee Sharma

  Dataset Overview

This dataset contains labeled images of South American country flags intended for training object detection models using the YOLOv8 format. The annotations are structured for seamless integration with Ultralytics' YOLOv8 training pipeline.

  Contents

images/train/ – Training images
images/val/ – Validation images
images/test/ – Test images
labels/ –… See the full description on the dataset page: https://huggingface.co/datasets/7mgppp/south-american-flags.

Chess Piece Detection Dataset: chess_pieces_dominique

  Dataset Description

This dataset contains chess piece detection annotations in YOLOv8 format. Chess piece detection dataset from Dominique with 12 classes of chess pieces, optimized for YOLOv8 training.

  Dataset Structure

The dataset follows the YOLOv8 format with the following structure:

train/: Training images and labels valid/: Validation images and labels test/: Test images and labels

  Classes… See the full description on the dataset page: https://huggingface.co/datasets/dopaul/chess-pieces-dominique.

Accident-Detection-Model

Accident Detection Model is made using YOLOv8, Google Collab, Python, Roboflow, Deep Learning, OpenCV, Machine Learning, Artificial Intelligence. It can detect an accident on any accident by live camera, image or video provided. This model is trained on a dataset of 3200+ images, These images were annotated on roboflow.

Problem Statement

• Road accidents are a major problem in India, with thousands of people losing their lives and many more suffering serious injuries every year.
• According to the Ministry of Road Transport and Highways, India witnessed around 4.5 lakh road accidents in 2019, which resulted in the deaths of more than 1.5 lakh people.
• The age range that is most severely hit by road accidents is 18 to 45 years old, which accounts for almost 67 percent of all accidental deaths.

Accidents survey

https://user-images.githubusercontent.com/78155393/233774342-287492bb-26c1-4acf-bc2c-9462e97a03ca.png" alt="Survey">

Literature Survey

• Sreyan Ghosh in Mar-2019, The goal is to develop a system using deep learning convolutional neural network that has been trained to identify video frames as accident or non-accident.
• Deeksha Gour Sep-2019, uses computer vision technology, neural networks, deep learning, and various approaches and algorithms to detect objects.

Research Gap

• Lack of real-world data - We trained model for more then 3200 images.
• Large interpretability time and space needed - Using google collab to reduce interpretability time and space required.
• Outdated Versions of previous works - We aer using Latest version of Yolo v8.

Proposed methodology

• We are using Yolov8 to train our custom dataset which has been 3200+ images, collected from different platforms.
• This model after training with 25 iterations and is ready to detect an accident with a significant probability.

Model Set-up

Preparing Custom dataset

• We have collected 1200+ images from different sources like YouTube, Google images, Kaggle.com etc.
• Then we annotated all of them individually on a tool called roboflow.
• During Annotation we marked the images with no accident as NULL and we drew a box on the site of accident on the images having an accident
• Then we divided the data set into train, val, test in the ratio of 8:1:1
• At the final step we downloaded the dataset in yolov8 format.
  #### Using Google Collab
• We are using google colaboratory to code this model because google collab uses gpu which is faster than local environments.
• You can use Jupyter notebooks, which let you blend code, text, and visualisations in a single document, to write and run Python code using Google Colab.
• Users can run individual code cells in Jupyter Notebooks and quickly view the results, which is helpful for experimenting and debugging. Additionally, they enable the development of visualisations that make use of well-known frameworks like Matplotlib, Seaborn, and Plotly.
• In Google collab, First of all we Changed runtime from TPU to GPU.
• We cross checked it by running command ‘!nvidia-smi’
  #### Coding
• First of all, We installed Yolov8 by the command ‘!pip install ultralytics==8.0.20’
• Further we checked about Yolov8 by the command ‘from ultralytics import YOLO from IPython.display import display, Image’
• Then we connected and mounted our google drive account by the code ‘from google.colab import drive drive.mount('/content/drive')’
• Then we ran our main command to run the training process ‘%cd /content/drive/MyDrive/Accident Detection model !yolo task=detect mode=train model=yolov8s.pt data= data.yaml epochs=1 imgsz=640 plots=True’
• After the training we ran command to test and validate our model ‘!yolo task=detect mode=val model=runs/detect/train/weights/best.pt data=data.yaml’ ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt conf=0.25 source=data/test/images’
• Further to get result from any video or image we ran this command ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt source="/content/drive/MyDrive/Accident-Detection-model/data/testing1.jpg/mp4"’
• The results are stored in the runs/detect/predict folder.
  Hence our model is trained, validated and tested to be able to detect accidents on any video or image.

Challenges I ran into

I majorly ran into 3 problems while making this model

• I got difficulty while saving the results in a folder, as yolov8 is latest version so it is still underdevelopment. so i then read some blogs, referred to stackoverflow then i got to know that we need to writ an extra command in new v8 that ''save=true'' This made me save my results in a folder.
• I was facing problem on cvat website because i was not sure what

Supplementary Data Protocol

This supplementary dataset includes all files necessary to reproduce and evaluate the training and validation of YOLOv8 and CNN models for detecting GUS-stained and haustoria-containing cells with the BluVision Haustoria software.

1. gus_training_set_yolo/
- Contains the complete YOLOv8-compatible training dataset for GUS classification.
- Format: PyTorch YOLOv5/8 structure from Roboflow export.
- Subfolders:
- train/, test/, val/: Image sets and corresponding label files.
- data.yaml: Configuration file specifying dataset structure and classes.

2. haustoria_training_set_yolo/
- Contains the complete YOLOv8-compatible training dataset for haustoria detection.
- Format identical to gus_training_set_yolo/.

3. haustoria_training_set_cnn/
- Dataset formatted for CNN-based classification.
- Structure:
- gus/: Images of cells without haustoria.
- hau/: Images of cells with haustoria.
- Suitable for binary classification pipelines (e.g., Keras, PyTorch).

4. yolo_models/
- Directory containing the final trained YOLOv8 model weights.
- Includes:
- gus.pt: YOLOv8 model trained on GUS data.
- haustoria.pt: YOLOv8 model trained on haustoria data.

The SmartBay Observatory in Galway Bay is an important contribution by Ireland to the growing global network of real-time data capture systems deployed within the ocean – technology giving us new insights into the ocean which we have not had before.

The observatory was installed on the seafloor 1.5km off the coast of Spiddal, County Galway, Ireland . The observatory uses cameras, probes and sensors to permit continuous and remote live underwater monitoring. This observatory equipment allows ocean researchers unique real-time access to monitor ongoing changes in the marine environment. Data relating to the marine environment at the site is transferred in real-time from the SmartBay Observatory through a fibre optic telecommunications cable to the Marine Institute headquarters and onwards onto the internet. The data includes a live video stream, the depth of the observatory node, the sea temperature and salinity, and estimates of the chlorophyll and turbidity levels in the water which give an indication of the volume of phytoplankton and other particles, such as sediment, in the water.

The Smartbay Marine Species Object Detection training Dataset is an initial Bounding Box Annotated image dataset used in attempting to Train a YOLOv8 Object Detection Model to classify the Marine Fauna observed in the Smartbay Observatory Video footage using species names.

The imagery used in this training dataset consists of image frame captures from the Smartbay video Archive files, CC-BY imagery from the www.minka-sdg.org website and images taken by Eva Cullen in the "Galway Atlantaquaria" Aquarium in Galway, Ireland.

The imagery were annotated using CVAT, collated on Roboflow and exported in YOLOv8 training dataset format.

This is part of the dataset concerning the YOLO training of satellite images (Barchan dunes). In this dataset, you find the "YOLOv8 train" folder that contains the structure and images obtained from HiRISE, CTX global mosaic, Google Earth Pro, and Copernicus. We saved the images with the HiView, Google Earth Pro, and Copernicus software to train a CNN with images of barchan dunes, the "Train Results" folder that contains the figures and weights of YOLO detection of barchan dunes, the "Earth detections" that contains some barchan dune detections on different locations of Earth, the "Mars detections" that contains some barchan dune detections on different locations of Mars, and the "Code Files" that contains the scripts to detect barchan dunes, train a YOLOv8, convert masks to polygons and plot resulting YOLO parameters.

Train 200

## Overview

Train 200 is a dataset for instance segmentation tasks - it contains Weed FKzS annotations for 200 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).

Train E

## Overview

Train E is a dataset for instance segmentation tasks - it contains Yirtik YXfG annotations for 531 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).

Dataset For Train

## Overview

Dataset For Train is a dataset for instance segmentation tasks - it contains Pothole annotations for 782 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 [BY-NC-SA 4.0 license](https://creativecommons.org/licenses/BY-NC-SA 4.0).

Train a YOLOv8 model on given dataset, get the output metrices and analyse them.

BD Step2 V1_3 Train

## Overview

BD Step2 V1_3 Train is a dataset for instance segmentation tasks - it contains Coal Rock annotations for 755 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).