Plantations Segmentation Object Detection dataset

The dataset consist of aerial photography of agricultural plantations with crops such as cabbage and zucchini. The dataset addresses agricultural tasks such as plant detection and counting, health assessment, and irrigation planning.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F5fa7e8e62e793dac70dc9e1db6f60a18%2F66666.png?generation=1685972525147537&alt=media" alt="">

OTHER DATASETS WITH SEGMENTATION:

• Food Images Segmentation
• Vehicle Images & Segmentation
• Bald People Segmentation Dataset
• Weapons - Gun Detection & Segmentation
• UFC/MMA Fights Images Segmentation Dataset

Dataset structure

• Plantations_Segmentation - contains of original plantation images (folder img) and file with annotations (.xml)
• Object_Segmentation - includes object segmentation masks for the original images
• Class_Segmentation - includes class segmentation masks for the original images

Types of segmentation

The dataset includes two types of segmentation: - Class Segmentation - objects corresponding to one class are identified - Object Segmentation - all objects are identified separately

Data Format

Each image from img folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the polygons. For each point, the x and y coordinates are provided.

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F4107d573b14b40ee2c9c67727ab9ec87%2Fcarbon%20(6).png?generation=1686129907313187&alt=media" alt="">

Plantation segmentation might be made in accordance with your requirements.

🧩 This is just an example of the data. Leave a request here to learn more

🚀 You can learn more about our high-quality unique datasets here

keywords: agricultural tasks dataset, image segmentation dataset, plantations images dataset, plantations segmentation dataset, land cover dataset, agricultural products dataset, semantic segmentation dataset, agriculture dataset, agricultural data, object detection dataset, plants segmentation dataset, plant detection, plant recognition

stevenbucaille/image-segmentation-models-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

The dataset includes 458 hi-res images together with their alpha maps (BW) indicating the crack presence. The ground truth for semantic segmentation has two classes to conduct binary pixelwise classification. The photos are captured in various buildings located in Middle East Technical University.

You can access a larger dataset containing images with 227x227 px dimensions for classification which are produced from this dataset from http://dx.doi.org/10.17632/5y9wdsg2zt.1 .

The dataset is an extension of the Semantic Drone Dataset of Institute of Computer Graphics and Vision at the Graz University of Technology.

Binary and 5-class Extension

The extension proposes two different preprocessed datasets in order to perform binary segmentation and multi-class segmentation with 5 macro-groups instead of the original 24 labels and a resolution of 960x736px instead of 6000x4000px.

Colormaps and Re-labeling

All the information relative to the colors assigned to each class are contained in the colormaps.xlsx file and in addition to it there are also the conversion dictionaries used to convert the labels in classes_dict.txt.

Original Dataset

The original dataset with 24 different classes and 24Mpx of resolution is contained in the folder semantic drone dataset

Questions and Comments

Leave an up-vote if you are going to use this dataset or leave a comment/suggestion on how I could improve the documentation, if you have questions feel free to ask

The Multiclass Weeds Dataset for Image Segmentation comprises two species of weeds: Soliva Sessilis (Field Burrweed) and Thlaspi Arvense L. (Field Pennycress). Weed images were acquired during the early growth stage under field conditions in a brinjal farm located in Gorakhpur, Uttar Pradesh, India. The dataset contains 7872 augmented images and corresponding masks. Images were captured using various smartphone cameras and stored in RGB color format in JPEG format. The captured images were labeled using the labelme tool to generate segmented masks. Subsequently, the dataset was augmented to generate the final dataset.

Unet++: A nested U-Net architecture for medical image segmentation.

This dataset was created to support the training and evaluation of semantic segmentation models for plant region extraction in digitised herbarium specimens. It was created in the context of the e-col+ project (ANR‐21‐ESRE‐005).ContentSegmentation dataset:This dataset consists of digitised herbarium specimen images and their corresponding segmented versions, where only plant regions are preserved and background elements are removed.The archive segmentation_dataset.zip contains two main components:train/: 2,952 images.unsegmented_images/: 1,476 original RGB herbarium images.segmented_images/: 1,476 segmented versions of the same images (plant-only regions).test/: 333 additional herbarium images, used for evaluating segmentation models on unseen data.Training and validation images were selected from the Herbarium Segmentation Dataset (https://doi.org/10.6084/m9.figshare.27685914.v1), while the test set consists of newly and manually annotated images.Out-of-Distribution evaluation dataset:The Out Of Distribution (OOD) dataset comprises 171 unannotated images, selected to represent visually challenging conditions, including:noisy or highly textured backgrounds,colored backdrops (e.g., yellow, pink, dark grey),intricate plant morphologies,and common digitisation artifacts like pins, overlapping components, and mosaic-like patterns.

nielsr/image-segmentation-toy-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Plant Village (Image Segmentation)

## Overview

Plant Village (Image Segmentation) is a dataset for semantic segmentation tasks - it contains Objects XKHQ annotations for 1,256 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 Herbarium Image Segmentation Dataset originates from the MNHN (Muséum National d’Histoire Naturelle) in Paris and includes 11 diverse plant families and genera, offering a rich variety within dicotyledons. The dataset comprises 2,277 RGB images, each paired with a corresponding segmentation mask. These images cover a range of genera: Amborella (91 images), Castanea (161), Desmodium (164), Ulmus (352), Rubus (184), Litsea (199), Eugenia (219), Laurus (250), Convolvulaceae (177), Magnolia (162), and Monimiaceae (318), showcasing significant morphological diversity.This dataset was generated by removing non-plant backgrounds to enhance the clarity of plant features. It is suitable for segmentation tasks in botanical research and supports studies on plant morphology, biodiversity, and conservation. The segmented images can improve accuracy in classification tasks, particularly in identifying plant morphological traits, and are intended to facilitate research in plant science, biodiversity, and conservation.

Dataset

This dataset was created by Umer Majeed

Released under MIT

Contents

The dataset is comprised of 38 chemically stained Whole slide image samples along with their corresponding ground truth annotated by histopathologists for 12 classes indicating skin layers (Epidermis, Reticular dermis, Papillary dermis, Dermis, Keratin), Skin tissues (Inflammation, Hair follicles, Glands), skin cancer (Basal cell carcinoma, Squamous cell carcinoma, Intraepidermal carcinoma) and background (BKG).

The dataset consists of six collections of SEM images, three trained U-net AI models, and CSV files with image quality metrics and trained AI model accuracy metrics. Each SEM image collection contains images augmented with Poisson noise and contrast.This work was performed with funding from the CHIPS Metrology Program, part of CHIPS for America, National Institute of Standards and Technology, U.S. Department of Commerce.

Image Segmentation

## Overview

Image Segmentation is a dataset for instance segmentation tasks - it contains Chicken annotations for 426 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 U-net is a deep convolutional neural network for biomedical image segmentation.

View on bioimage.io # HPA Cell Image Segmentation Dataset

This dataset includes annotated cell images obtained from the Human Protein Atlas (http://www.proteinatlas.org), each image contains 4 channels (Microtubules, ER, Nuclei and Protein of Interest). The cells in each image are annotated with polygons and saved into GeoJSON format produced with Kaibu(https://kaibu.org) annotation tool.

hpa_cell_segmentation_dataset_v2_512x512_4train_159test.zip is an example dataset for running a deep learning-based interactive annotation tools in ImJoy (https://github.com/imjoy-team/imjoy-interactive-segmentation).

hpa_dataset_v2.zip is a full annotate image segmentation dataset

Utility functions in Python for reading the GeoJSON annotation can be found here: https://github.com/imjoy-team/kaibu-utils/blob/main/kaibu_utils/init.py

Bone Tumor Image Segmentation

## Overview

Bone Tumor Image Segmentation is a dataset for object detection tasks - it contains Tumor annotations for 582 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).

Road Segmentation Dataset

This dataset comprises a collection of images captured through DVRs (Digital Video Recorders) showcasing roads. Each image is accompanied by segmentation masks demarcating different entities (road surface, cars, road signs, marking and background) within the scene.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

The dataset can be utilized for enhancing computer vision algorithms involved in road surveillance, navigation, and intelligent transportation systemsand and in autonomous driving systems.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fb0789a0ec8075d9c7abdb0aa9faced59%2FFrame%2012.png?generation=1694606364403023&alt=media" alt="">

DATASETS WITH VEHICLES :

• License Plates - 1 209 438 OCR Plates
• Car Object Detection & Tracking
• Vehicle Images & Segmentation
• Parking Space Detection & Classification Dataset
• Videos around cars

Dataset structure

• images - contains of original images of roads
• masks - includes segmentation masks created for the original images
• annotations.xml - contains coordinates of the polygons, created for the original photo

Data Format

Each image from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the polygons and labels . For each point, the x and y coordinates are provided.

Сlasses:

• road_surface: surface of the road,
• marking: white and yellow marking on the road,
• road_sign: road signs,
• car: cars on the road,
• background: side of the road and surronding objects

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fa74a4214f4dd89a35527ef008abfc151%2Fcarbon.png?generation=1694608637609153&alt=media" alt="">

Roads Segmentation might be made in accordance with your requirements.

🧩 This is just an example of the data. Leave a request here to learn more

🚀 You can learn more about our high-quality unique datasets here

keywords: road surface, road scene, off-road, vehicle segmentation dataset, semantic segmentation for self driving cars, self driving cars dataset, semantic segmentation for autonomous driving, car segmentation dataset, car dataset, car images, car parts segmentation, self-driving cars deep learning, cctv, image dataset, image classification, semantic segmentation

Road Image Segmentation

## Overview

Road Image Segmentation is a dataset for object detection tasks - it contains Roads annotations for 433 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).

1. Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications, and are particularly suited for image data. Image segmentation (the classification of all pixels in images) is one such application and can for example be used to assess forest structural metrics. While CNN-based image segmentation methods for such applications have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists.
2. Here, we present R package imageseg which implements a CNN-based workflow for general-purpose image segmentation using the U-Net and U-Net++ architectures in R. The workflow covers data (pre)processing, model training, and predictions. We illustrate the utility of the package with image recognition models for two forest structural metrics: tree canopy density and understory vegetation density. We trained the models using large and dive...