Brand Logo Recognition Yolov8

## Overview

Brand Logo Recognition  Yolov8 is a dataset for object detection tasks - it contains Logos annotations for 503 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).

Logo Detection YOLOv8

## Overview

Logo Detection YOLOv8 is a dataset for object detection tasks - it contains Logos annotations for 386 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).

Football Team Logos

## Overview

Football Team Logos is a dataset for instance segmentation tasks - it contains Logo annotations for 754 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.

ABESIT Logo

## Overview

ABESIT Logo is a dataset for instance segmentation tasks - it contains Logo annotations for 202 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).

Soup Can Object Detection Dataset Sample

Duality.ai just released a 1000 image dataset used to train a YOLOv8 model for 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.

  Dataset Overview

This dataset consists of high-quality images of soup cans… See the full description on the dataset page: https://huggingface.co/datasets/duality-robotics/YOLOv8-Object-Detection-02-Dataset.

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.

This dataset contains annotated images for object detection for containers and hands in a first-person view (egocentric view) during drinking activities. Both YOLOV8 format and COCO format are provided.Please refer to our paper for more details.Purpose: Training and testing the object detection model.Content: Videos from Session 1 of Subjects 1-20.Images: Extracted from the videos of Subjects 1-20 Session 1.Additional Images:~500 hand/container images from Roboflow Open Source data.~1500 null (background) images from VOC Dataset and MIT Indoor Scene Recognition Dataset:1000 indoor scenes from 'MIT Indoor Scene Recognition'400 other unrelated objects from VOC DatasetData Augmentation:Horizontal flipping±15% brightness change±10° rotationFormats Provided:COCO formatPyTorch YOLOV8 formatImage Size: 416x416 pixelsTotal Images: 16,834Training: 13,862Validation: 1,975Testing: 997Instance Numbers:Containers: Over 10,000Hands: Over 8,000

This dataset is designed for training and evaluating object detection models, specifically for detecting plastic bottles and classifying them based on the presence or absence of a label. It is structured to work seamlessly with YOLOv8 and follows the standard YOLO format.

🔍 Classes: 0: Bottle with Label

1: Bottle without Label

📁 Folder Structure: images/: Contains all image files

labels/: Corresponding YOLO-format annotation files

data.yaml: Configuration file for training with YOLOv8

🛠 Use Case: This dataset is ideal for real-time detection systems, quality control applications, recycling automation, and projects focused on object classification in cluttered or real-world environments.

Discover a comprehensive Traffic Sign Recognition dataset designed for YOLOv8.

OpenLogo

## Overview

OpenLogo is a dataset for object detection tasks - it contains Logos annotations for 9,617 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).

Explore the Player Detection and Tracking in Sports Videos Dataset, designed for training YOLOv8 models. Featuring diverse sports images and detailed annotations, this dataset supports robust development of player detection and tracking models, enhancing sports analytics and AI-driven analysis tools.

Dataset

This dataset was created by Samar Kale

Released under Attribution 3.0 Unported (CC BY 3.0)

Contents

Dataset

This dataset was created by cubeai

Contents

ankityadav09/yolov8 dataset hosted on Hugging Face and contributed by the HF Datasets community

YOLOv8 Plate Detection on Image ```python from ultralytics import YOLO from PIL import Image

model = YOLO('best.pt')

img_path = 'path/to/img.jpeg' # Change this section with your image path image = Image.open(img_path)

predictions = model.predict(image)[0].boxes

Here is an example output of the predictions.

print(predictions)

""" cls: tensor([0., 0.]) conf: tensor([0.7624, 0.7307]) data: tensor([[6.6032e-02, 2.3500e+02, 8.7492e+01, 2.7904e+02, 7.6236e-01, 0.0000e+00], [5.6500e+02, 2.4907e+02, 6.4560e+02, 2.7620e+02, 7.3072e-01, 0.0000e+00]]) id: None is_track: False orig_shape: (450, 800) shape: torch.Size([2, 6]) xywh: tensor([[ 43.7788, 257.0177, 87.4255, 44.0393], [605.2970, 262.6343, 80.6021, 27.1266]]) xywhn: tensor([[0.0547, 0.5712, 0.1093, 0.0979], [0.7566, 0.5836, 0.1008, 0.0603]]) xyxy: tensor([[6.6032e-02, 2.3500e+02, 8.7492e+01, 2.7904e+02], [5.6500e+02, 2.4907e+02, 6.4560e+02, 2.7620e+02]]) xyxyn: tensor([[8.2541e-05, 5.2222e-01, 1.0936e-01, 6.2008e-01], [7.0624e-01, 5.5349e-01, 8.0700e-01, 6.1377e-01]]) """

**YOLOv8 Plate Tracking on Video**

```python

from ultralytics import YOLO
import cv2

# import the weights
model = YOLO('best.pt')

# Open the video file
video_path = 'path/to/video.mp4' # Change this section with your video path
cap = cv2.VideoCapture(video_path)

# Loop through the video frames
while cap.isOpened():
  # Read a frame from the video
  success, frame = cap.read()

  if success:
    # Run YOLOv8 tracking on the frame, persisting tracks between frames
    results = model.track(frame, persist=True)

    # Visualize the results on the frame
    annotated_frame = results[0].plot()

    # Display the annotated frame
    cv2.imshow("YOLOv8 Tracking", annotated_frame)

    # Break the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord("q"):
      break
  else:
    # Break the loop if the end of the video is reached
    break

# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()

Detection results of WA-YOLO and YOLOv8 on the custom-built dataset.

Moon Detection Dataset for YOLOv8

This dataset was developed as part of the CubeRT-02 project, a CubeSat mission aimed at testing AI-powered vision systems in aerospace contexts. It consists of 7500+ annotated images for object detection of the Moon, optimized for use with the YOLOv8 architecture.

  📸 Dataset Collection & Annotation

Initial set: ~400 web-sourced images used for theoretical model exploration. Main dataset: 7500+ images captured over 6 months using… See the full description on the dataset page: https://huggingface.co/datasets/LuisAdrian5519/Moon_Detection_Dataset_for_YOLOv8.

Abstract

This repository contains the YOLOv8 model weights and segmented images used for the manuscript submitted to APL: Machine Learning, titled "A Comparative Analysis of YOLOv8 and U-Net Image Segmentation Approaches for Transmission Electron Micrographs of Polycrystalline Thin Films". It is paired with data available at https://doi.org/10.57783/w6n3-5b02

Logo_Ghost_Print

## Overview

Logo_Ghost_Print is a dataset for instance segmentation tasks - it contains GP annotations for 429 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).