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).

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 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).

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.

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).

Yolov8_train

## Overview

Yolov8_train is a dataset for object detection tasks - it contains Hand annotations for 982 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 dataset is in YOLOv8 format. The dataset is divided into train, validation and test. Data replication processes were also applied. Download Dataset.

• Original dataset: https://github.com/ayoolaolafenwa/PixelLib/releases/tag/1.0.0
• This dataset contains annotations in YOLO format.
• One can train different segmentation models on this dataset.
• Some of the segmentation models are: YOLOv8, Pointrend, MaskRCNN

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

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.

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/.

📄 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

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).

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.

Train Data 10000_1

## Overview

Train Data 10000_1 is a dataset for instance segmentation tasks - it contains 1 annotations for 4,400 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 researchers collected a dataset of 3,500 images of Tilapia fish in a small bowl containing three fish per image. These images were manually annotated using Roboflow, with four keypoints labeled on each fish: mouth, peduncle, belly, and back. While only the mouth and peduncle keypoints were needed for length measurement, the additional keypoints were included to support potential future research using girth for weight determination. The dataset was used to train YOLOv8 models for both keypoint detection and fish counting tasks. For real-world validation, an additional test set of 100 frame pairs (200 images total) captured from two cameras at different angles in actual fish farm conditions was also used.

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.

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.

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).