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.

Auto Label

## Overview

Auto Label is a dataset for instance segmentation tasks - it contains Buildings 4jY2 annotations for 7,839 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 [MIT license](https://creativecommons.org/licenses/MIT).

Label Data

## Overview

Label Data is a dataset for instance segmentation tasks - it contains Objects annotations for 1,955 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.

Label Real Data

## Overview

Label Real Data is a dataset for instance segmentation tasks - it contains Bags annotations for 318 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).

Dataset

This dataset was created by ZhenDDOS

Released under MIT

Contents

White Blood Cell (WBC) Detection in Microscopic Blood Cell Images

Overview

This dataset consists of microscopic images of blood cells specifically designed for the detection of White Blood Cells (WBC). It is intended for object detection tasks where the goal is to accurately locate and identify WBCs within blood smear images. Researchers and developers can utilize this data to train machine learning models for medical applications such as automated blood cell analysis.

Dataset Content

Images: The dataset contains high-resolution microscopic images of blood smears, where WBCs are scattered among Red Blood Cells (RBCs) and platelets. Each image is annotated with bounding boxes around the WBCs.

Annotations: The annotations are provided in YOLO format, where each bounding box is associated with a label for WBC.

File Structure:

images/: Contains the blood cell images in .jpg or .png format. labels/: Contains the annotation files in .txt format (YOLO format), with each file corresponding to an image. Image Size: Varies, but all images are in high resolution suitable for detection tasks.

Applications

Medical Image Analysis: This dataset can be used to build models for the automated detection of WBCs, which is a crucial step in diagnosing various blood-related disorders. Object Detection: Ideal for testing object detection algorithms like YOLO, Faster R-CNN, or SSD. Acknowledgments This dataset is created using publicly available microscopic blood cell images, annotated for educational and research purposes. It can be used for developing machine learning models for academic research, prototyping medical applications, or object detection benchmarking.

Multi Instance 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… See the full description on the dataset page: https://huggingface.co/datasets/duality-robotics/YOLOv8-Multi-Instance-Object-Detection-Dataset.

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.

Heart Label

## Overview

Heart Label is a dataset for instance segmentation tasks - it contains Heart annotations for 1,278 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 dataset contains RGB images and corresponding YOLOv8-format annotation files for the detection and classification of cattle behaviors in pasture-based environments. It was collected as part of the European Horizon 2020 XGain project and is intended for training and validating object detection models using the YOLOv8 framework. The dataset includes high-resolution images captured with overhead cameras and manual bounding box annotations indicating behaviors such as grazing, lying, and standing. Each image-label pair is UUID-matched and organized into a structured folder format. This resource supports research in computer vision, animal welfare, and precision livestock farming.

The dataset contains sample images from the Open Images Dataset v7. This dataset only contains images for the category 'ducks' and is arranged to fine-tune the YOLOv8 image segmentation models.

Directories:

The dataset contains two main directors, i.e., images and labels. These directories further contain 'train' and 'val' directories. As the names suggest, these directories contain images and labels for the training and validation of image segmentation models.

Dataset Description:

Training Images: 400 Validation Images: 50

Class/es: Duck

config.yaml

The dataset also contains a config.yaml file. This file contains paths for relevant directories that YOLOv8 needs to load datasets

Label In This

## Overview

Label In This is a dataset for instance segmentation tasks - it contains Aerial Img KmdI annotations for 603 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

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).

My Labels

## Overview

My Labels is a dataset for instance segmentation tasks - it contains . annotations for 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).

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.

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.

This dataset contains 11027 labeled images for the detection of fire and smoke instances in diverse real-world scenarios. The annotations are provided in YOLO format with bounding boxes and class labels for two classes: fire and smoke. The dataset is divided into an 80% training set with 10,090 fire instances and 9724 smoke instances, a 10% Validation set with 1,255 fire and 1,241 smoke instances, and a 10% Test set with 1,255 fire and 1,241 smoke instances. This dataset is suitable for training and evaluating fire and smoke detection models, such as YOLOv8, YOLOv9, and similar deep learning-based frameworks in the context of emergency response, wildfire monitoring, and smart surveillance.

The provided dataset consists of:

• 300 synthetic images featuring water-sensitive paper ()
• 2 real water-sensitive paper images manually annotated
• 127 real water-sensitive paper images, divided into squares, pre-annotated

The synthetic dataset was generated using an automated algorithm that creates individual droplets and positions them against a yellow artificial background. Annotations for instance segmentation each droplet are stored in a text file formatted according to YOLOv8 annotation format.

Some key features include:

• Distribution of number of droplets per image: the number of droplets per image is determined
  based on the configuration values to follow a normal distribution.
• Size distribution of droplets: the algorithm calculates the size of each droplet based on the
  Rosin-Rammler distribution.
• Image Resolution: images were created with three different resolutions
• Yellow Background: the background of each image is composed by a yellow radial gradient generated for each water-sensitive paperimage. The gradient transitions between two randomly chosen tones of yellow from a list of shades of yellow taken from real images of water-sensitive paper.
• Droplet Color: the colors of the droplets are taken from two distinct real datasets of water-sensitive paper.
• Droplet Shape: the shapes of the droplets are selected from a list containing 25 404 shapes, which are taken from real water-sensitive paper images.

Each set of images of this dataset is organized into two folder:

1. image: contains the water-sensitive paper images
2. label: contains the labels in YOLOv8 polygon format of each one of the droplets in the image

The dataset has been prepared for use in machine vision-based mango fruit and branch localisation for detection of fruit-branch occlusion. Images are from Honey Gold and Keitt mango varieties. The dataset contains: - 250 RGB images (200 training + 50 test images) of mango tree canopies acquired using Azure Kinect Camera under artificial lighting condition. - COCO JSON format label files with multi class (mango+branch), single classes (mango only and branch only) polygon annotations. - Labels converted to txt format to use for YOLOv8-seg + other models training. Annotation: The annotation tool - VGG Image Annotator (VIA) was used for ground truth labeling of images using polygon labelling tool.