Flower Segmentation

## Overview

Flower Segmentation is a dataset for instance segmentation tasks - it contains Flowers annotations for 6,053 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 consists of four sets of flower images, from three different species: apple, peach, and pear, and accompanying ground truth images. The images were acquired under a range of imaging conditions. These datasets support work in an accompanying paper that demonstrates a flower identification algorithm that is robust to uncontrolled environments and applicable to different flower species. While this data is primarily provided to support that paper, other researchers interested in flower detection may also use the dataset to develop new algorithms. Flower detection is a problem of interest in orchard crops because it is related to management of fruit load. Funding provided through ARS Integrated Orchard Management and Automation for Deciduous Tree Fruit Crops. Resources in this dataset:Resource Title: AppleA images. File Name: AppleA.zipResource Description: 147 images of an apple tree in bloom acquired with a Canon EOS 60D.Resource Title: Training image names from Apple A dataset. File Name: train.txtResource Description: This is a list of filenames used in training; see related paper for details.Resource Title: AppleA labels. File Name: AppleA_Labels.zipResource Description: Binary images for the Apple A set, where white represents flower pixels and black, non-flower pixels. June 25, 2018: 5 files added: 275.png, 316.png, 328.png, 336.png, 369.png.Resource Title: Validation image names from Apple A dataset. File Name: val.txtResource Description: This is a list of filenames used in testing; see related paper for details. June 25, 2018: 5 filenames added. IMG_0275.JPG IMG_0316.JPG IMG_0328.JPG IMG_0336.JPG IMG_0369.JPGResource Title: AppleB images. File Name: AppleB.zipResource Description: 15 images of an apple tree in bloom acquired with a GoPro HERO 5. June 25, 2018: 3 files added. 23.bmp 28.bmp 42.bmpResource Title: AppleB labels. File Name: AppleB_Labels.zipResource Description: Binary images for the Apple B set, where white represents flower pixels and black, non-flower pixels. June 25, 2018: 3 files added. 23.bmp 28.bmp 42.bmpResource Title: Peach. File Name: Peach.zipResource Description: 20 images of an peach tree in bloom acquired with a GoPro HERO 5. June 25, 2018: 4 files added. 14.bmp 34.bmp 40.bmp 41.bmpResource Title: Peach labels. File Name: PeachLabels.zipResource Description: Binary images for the Peach set, where white represents flower pixels and black, non-flower pixels. June 25, 2018: 4 files added. 14.bmp 34.bmp 40.bmp 41.bmpResource Title: Pear. File Name: Pear.zipResource Description: 15 images of a free-standing pear tree in bloom, acquired with a GoPro HERO5. June 25, 2018: 3 files added. 1_25.bmp 1_62.bmp 2_28.bmpResource Title: Pear labels. File Name: PearLabels.zipResource Description: Binary images for the pear set, where white represents flower pixels and black, non-flower pixels. June 25, 2018: 3 files added. 1_25.bmp 1_62.bmp 2_28.bmpResource Title: Apple A Labeled images from training set . File Name: AppleALabels_Train.zipResource Description: Binary images for the Apple A set, where white represents flower pixels and black, non-flower pixels. These images form the training set. Resource added August 20, 2018. User noted that this resource was missing.

A comprehensive dataset of 2.7M+ flower images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification, and segmentation.

Flower Recognition Final

## Overview

Flower Recognition Final is a dataset for instance segmentation tasks - it contains Flowers annotations for 296 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).

Resource Title: Validation image names from Apple A dataset. File Name: val.txtResource Description: This is a list of filenames used in testing; see related paper for details. June 25, 2018: 5 filenames added. IMG_0275.JPG IMG_0316.JPG IMG_0328.JPG IMG_0336.JPG IMG_0369.JPG

flowersdataset #segmentation #VGG

  Dataset Card for Flowers Dataset





  Dataset Summary

VGG have created a 17 category flower dataset with 80 images for each class. The flowers chosen are some common flowers in the UK. The images have large scale, pose and light variations and there are also classes with large varations of images within the class and close similarity to other classes. The categories can be seen in the figure below. We randomly split the dataset into 3… See the full description on the dataset page: https://huggingface.co/datasets/Guldeniz/flower_dataset.

This dataset features over 2,700,000 high-quality images of flowers sourced from photographers worldwide. Designed to support AI and machine learning applications, it provides a diverse and richly annotated collection of flower imagery.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data, detailing camera settings such as aperture, ISO, shutter speed, and focal length. Additionally, each image is pre-annotated with object and scene detection metadata, making it ideal for tasks like classification, detection, and segmentation. Popularity metrics, derived from engagement on our proprietary platform, are also included.

1. Unique Sourcing Capabilities: the images are collected through a proprietary gamified platform for photographers. Competitions focused on flower photography ensure fresh, relevant, and high-quality submissions. Custom datasets can be sourced on-demand within 72 hours, allowing for specific requirements such as particular flower species or geographic regions to be met efficiently.

2. Global Diversity: photographs have been sourced from contributors in over 100 countries, ensuring a vast array of flower species, colors, and environmental settings. The images feature varied contexts, including natural habitats, gardens, bouquets, and urban landscapes, providing an unparalleled level of diversity.

3. High-Quality Imagery: the dataset includes images with resolutions ranging from standard to high-definition to meet the needs of various projects. Both professional and amateur photography styles are represented, offering a mix of artistic and practical perspectives suitable for a variety of applications.

4. Popularity Scores Each image is assigned a popularity score based on its performance in GuruShots competitions. This unique metric reflects how well the image resonates with a global audience, offering an additional layer of insight for AI models focused on user preferences or engagement trends.

5. I-Ready Design: this dataset is optimized for AI applications, making it ideal for training models in tasks such as image recognition, classification, and segmentation. It is compatible with a wide range of machine learning frameworks and workflows, ensuring seamless integration into your projects.

6. Licensing & Compliance: the dataset complies fully with data privacy regulations and offers transparent licensing for both commercial and academic use.

Use Cases 1. Training AI systems for plant recognition and classification. 2. Enhancing agricultural AI models for plant health assessment and species identification. 3. Building datasets for educational tools and augmented reality applications. 4. Supporting biodiversity and conservation research through AI-powered analysis.

This dataset offers a comprehensive, diverse, and high-quality resource for training AI and ML models, tailored to deliver exceptional performance for your projects. Customizations are available to suit specific project needs. Contact us to learn more!

Final_flower_segmentation

## Overview

Final_flower_segmentation is a dataset for instance segmentation tasks - it contains Floflow annotations for 4,572 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).

FLOWER MEASUREMENT

## Overview

FLOWER MEASUREMENT is a dataset for instance segmentation tasks - it contains FLOWER annotations for 201 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 depth of the network model.

The dataset was annotated using the labelme tool, and it was trained using the pixellib.

Flower Interpreter

## Overview

Flower Interpreter is a dataset for instance segmentation tasks - it contains Polygon annotations for 2,534 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).

Flower Detailed

## Overview

Flower Detailed is a dataset for instance segmentation tasks - it contains Drawing Of Flower annotations for 257 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).

We have created a 17 category flower dataset with 80 images for each class. The flowers chosen are some common flowers in the UK. The images have large scale, pose and light variations and there are also classes with large varations of images within the class and close similarity to other classes. The categories can be seen in the figure below. We randomly split the dataset into 3 different training, validation and test sets. A subset of the images have been groundtruth labelled for segmentation.

Accuracy of method 1 by including and excluding inconsistent results.

Setting of deep learning architecture.

Effect on the accuracy of the classifier after label modification and counting correct predictions in terms of set-membership.

Leaves from genetically unique Juglans regia plants were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA). Soil samples were collected in Fall of 2017 from the riparian oak forest located at the Russell Ranch Sustainable Agricultural Institute at the University of California Davis. The soil was sieved through a 2 mm mesh and was air dried before imaging. A single soil aggregate was scanned at 23 keV using the 10x objective lens with a pixel resolution of 650 nanometers on beamline 8.3.2 at the ALS. Additionally, a drought stressed almond flower bud (Prunus dulcis) from a plant housed at the University of California, Davis, was scanned using a 4x lens with a pixel resolution of 1.72 µm on beamline 8.3.2 at the ALS Raw tomographic image data was reconstructed using TomoPy. Reconstructions were converted to 8-bit tif or png format using ImageJ or the PIL package in Python before further processing. Images were annotated using Intel’s Computer Vision Annotation Tool (CVAT) and ImageJ. Both CVAT and ImageJ are free to use and open source. Leaf images were annotated in following Théroux-Rancourt et al. (2020). Specifically, Hand labeling was done directly in ImageJ by drawing around each tissue; with 5 images annotated per leaf. Care was taken to cover a range of anatomical variation to help improve the generalizability of the models to other leaves. All slices were labeled by Dr. Mina Momayyezi and Fiona Duong.To annotate the flower bud and soil aggregate, images were imported into CVAT. The exterior border of the bud (i.e. bud scales) and flower were annotated in CVAT and exported as masks. Similarly, the exterior of the soil aggregate and particulate organic matter identified by eye were annotated in CVAT and exported as masks. To annotate air spaces in both the bud and soil aggregate, images were imported into ImageJ. A gaussian blur was applied to the image to decrease noise and then the air space was segmented using thresholding. After applying the threshold, the selected air space region was converted to a binary image with white representing the air space and black representing everything else. This binary image was overlaid upon the original image and the air space within the flower bud and aggregate was selected using the “free hand” tool. Air space outside of the region of interest for both image sets was eliminated. The quality of the air space annotation was then visually inspected for accuracy against the underlying original image; incomplete annotations were corrected using the brush or pencil tool to paint missing air space white and incorrectly identified air space black. Once the annotation was satisfactorily corrected, the binary image of the air space was saved. Finally, the annotations of the bud and flower or aggregate and organic matter were opened in ImageJ and the associated air space mask was overlaid on top of them forming a three-layer mask suitable for training the fully convolutional network. All labeling of the soil aggregate and soil aggregate images was done by Dr. Devin Rippner. These images and annotations are for training deep learning models to identify different constituents in leaves, almond buds, and soil aggregates Limitations: For the walnut leaves, some tissues (stomata, etc.) are not labeled and only represent a small portion of a full leaf. Similarly, both the almond bud and the aggregate represent just one single sample of each. The bud tissues are only divided up into buds scales, flower, and air space. Many other tissues remain unlabeled. For the soil aggregate annotated labels are done by eye with no actual chemical information. Therefore particulate organic matter identification may be incorrect. Resources in this dataset:Resource Title: Annotated X-ray CT images and masks of a Forest Soil Aggregate. File Name: forest_soil_images_masks_for_testing_training.zipResource Description: This aggregate was collected from the riparian oak forest at the Russell Ranch Sustainable Agricultural Facility. The aggreagate was scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 0,0,0; pores spaces have a value of 250,250, 250; mineral solids have a value= 128,0,0; and particulate organic matter has a value of = 000,128,000. These files were used for training a model to segment the forest soil aggregate and for testing the accuracy, precision, recall, and f1 score of the model.Resource Title: Annotated X-ray CT images and masks of an Almond bud (P. Dulcis). File Name: Almond_bud_tube_D_P6_training_testing_images_and_masks.zipResource Description: Drought stressed almond flower bud (Prunis dulcis) from a plant housed at the University of California, Davis, was scanned by X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 4x lens with a pixel resolution of 1.72 µm using. For masks, the background has a value of 0,0,0; air spaces have a value of 255,255, 255; bud scales have a value= 128,0,0; and flower tissues have a value of = 000,128,000. These files were used for training a model to segment the almond bud and for testing the accuracy, precision, recall, and f1 score of the model.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads Resource Title: Annotated X-ray CT images and masks of Walnut leaves (J. Regia) . File Name: 6_leaf_training_testing_images_and_masks_for_paper.zipResource Description: Stems were collected from genetically unique J. regia accessions at the 117 USDA-ARS-NCGR in Wolfskill Experimental Orchard, Winters, California USA to use as scion, and were grafted by Sierra Gold Nursery onto a commonly used commercial rootstock, RX1 (J. microcarpa × J. regia). We used a common rootstock to eliminate any own-root effects and to simulate conditions for a commercial walnut orchard setting, where rootstocks are commonly used. The grafted saplings were repotted and transferred to the Armstrong lathe house facility at the University of California, Davis in June 2019, and kept under natural light and temperature. Leaves from each accession and treatment were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 170,170,170; Epidermis value= 85,85,85; Mesophyll value= 0,0,0; Bundle Sheath Extension value= 152,152,152; Vein value= 220,220,220; Air value = 255,255,255.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads

The data set was produced by MetaVision team using original content.

Several segmentation techniques were used to generate accurate masks from the original video streams, preserving natural variance and original shapes.

This set is just a "building block". Feel free to augment it or combine it with other sets.

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Hand_segmentation

## Overview

Hand_segmentation is a dataset for instance segmentation tasks - it contains Hand Gestures Seg annotations for 284 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 [ODbL v1.0 license](https://creativecommons.org/licenses/ODbL v1.0).