The "Iris Flower Visualization using Python" project is a data science project that focuses on exploring and visualizing the famous Iris flower dataset. The Iris dataset is a well-known dataset in the field of machine learning and data science, containing measurements of four features (sepal length, sepal width, petal length, and petal width) for three different species of Iris flowers (Setosa, Versicolor, and Virginica).

In this project, Python is used as the primary programming language along with popular libraries such as pandas, matplotlib, seaborn, and plotly. The project aims to provide a comprehensive visual analysis of the Iris dataset, allowing users to gain insights into the relationships between the different features and the distinct characteristics of each Iris species.

The project begins by loading the Iris dataset into a pandas DataFrame, followed by data preprocessing and cleaning if necessary. Various visualization techniques are then applied to showcase the dataset's characteristics and patterns. The project includes the following visualizations:

1. Scatter Plot: Visualizes the relationship between two features, such as sepal length and sepal width, using points on a 2D plane. Different species are represented by different colors or markers, allowing for easy differentiation.

2. Pair Plot: Displays pairwise relationships between all features in the dataset. This matrix of scatter plots provides a quick overview of the relationships and distributions of the features.

3. Andrews Curves: Represents each sample as a curve, with the shape of the curve representing the corresponding Iris species. This visualization technique allows for the identification of distinct patterns and separability between species.

4. Parallel Coordinates: Plots each feature on a separate vertical axis and connects the values for each data sample using lines. This visualization technique helps in understanding the relative importance and range of each feature for different species.

5. 3D Scatter Plot: Creates a 3D plot with three features represented on the x, y, and z axes. This visualization allows for a more comprehensive understanding of the relationships between multiple features simultaneously.

Throughout the project, appropriate labels, titles, and color schemes are used to enhance the visualizations' interpretability. The interactive nature of some visualizations, such as the 3D Scatter Plot, allows users to rotate and zoom in on the plot for a more detailed examination.

The "Iris Flower Visualization using Python" project serves as an excellent example of how data visualization techniques can be applied to gain insights and understand the characteristics of a dataset. It provides a foundation for further analysis and exploration of the Iris dataset or similar datasets in the field of data science and machine learning.

A large set of images of flowers

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('tf_flowers', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/tf_flowers-3.0.1.png" alt="Visualization" width="500px">

We have created a 102 category dataset, consisting of 102 flower categories. The flowers chosen to be flower commonly occuring in the United Kingdom. Each class consists of between 40 and 258 images. The details of the categories and the number of images for each class can be found on this category statistics page.

Dataset

This dataset was created by Hamza Tanç

Contents

Open source flower images available in Python distribution. Raw images converted to TFRecord format in offline process.

This dataset contains both the artificial and real flower images of bramble flowers. The real images were taken with a realsense D435 camera inside the West Virginia University greenhouse. All the flowers are annotated in YOLO format with bounding box and class name. The trained weights after training also have been provided. They can be used with the python script provided to detect the bramble flowers. Also the classifier can classify whether the flowers center is visible or hidden which will be helpful in precision pollination projects.

Dataset

This dataset was created by sadaf koondhar

Contents

The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.

It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.

The columns in this dataset are:

• Id
• SepalLengthCm
• SepalWidthCm
• PetalLengthCm
• PetalWidthCm
• Species

This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('iris', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

We present infrastructure for developing large-scale and long-term phenological datasets across multiple herbaria, as well as a sample dataset that has been acquired from the digital archives of 440 distinct herbaria across North America and further processed to evaluate phenological status. This dataset contains 2,319,672 specimen records of plants collected while reproductively active. These data have been modified to explicitly codify the observed phenological status of each specimen at the time of collection, and to remove specimens for which information essential to assessing their phenology or the corresponding climate conditions in the year and location of collection were missing. As different collectors have used distinct taxonomic schema over space and time in documenting the specimens being collected, these data were also rectified into a single unified taxonomic schema to ensure that consistent taxon names were used throughout the dataset. Further, this data has been united..., Phenological data pertaining to flowering times in this dataset consist of 2,319,672 specimen records of plant species collected in flower, while strobilating, or while fertile (this last category primarily applied to graminoids). These data were derived from the digital archives of 440 herbaria (see Readme for full listing), and subsequently cleaned and modified using several criteria described below to facilitate their use in phenological assessment. To ensure the quality of the data used in this study, specimens were included in the dataset analyzed here only if, at the time of digitization, herbarium personnel had: verified that the specimens were collected when in flower, strobilating, or fertile; recorded GPS coordinates of the location from which the specimen was collected; and provided the precise date of collection (including month, date, and year). Only those specimens that were explicitly recorded reproductive status within either the DarwinCore “reproductivecondition†or “li..., Code was written in python 3.7. Multiple python packages are required to run these packages (see attached .yml file for full list). We recommend the usage of Anaconda for constructing the python environment and installing the python packages required to produce this dataset, including the PhenoColl package that was developed for this project (https://doi.org/10.5281/zenodo.8323153) , # North American Herbarium Data for Phenological Assessment

Author Information

Principal Investigator Contact Information

• Name: Isaac W. Park
• Institution: University of California - Santa Barbara
• Address: 4117 Life Sciences Building, University of California Santa Barbara 93116
• Email: isaac_park@ucsb.edu

Date of data collection

07-01-2021 through 08-30-2021

Geographic location of data collection:

North America

Information about funding sources that supported the collection of the data

Munging of this data from raw herbarium data was supported by the National Science Foundation (NSF) through:

• NSF DEB-1556768 (to S.J.M., I.W.P.)
• NSF DEB-2105932 (to S.J.M., I.W.P)
• NSF DEB-2105907 (to S.R.)
• NSF DEB-2105903 (to C.C.D.)

SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: Creative Commons Zero (CC0 1.0)(note that supplementary data deposited in Zenodo, which includes the rectified phenological data developed using these data a...

This dataset was downloaded from Udacity's AI with Python Programming Nanodegree. It consists of 102 classes of flowers and their labels are present in the cat_to_name.json. It consists about 8200 photos of different flowers and their labels are mentioned in the file names.

Automated Flower Identification Using Convolutional Neural Networks

This project aims to develop a model for identifying five different flower species (rose, tulip, sunflower, dandelion, daisy) using Convolutional Neural Networks (CNNs).

Description

The dataset consists of 5,000 images (1,000 images per class) collected from various online sources. The model achieved an accuracy of 98.58% on the test set. Usage

This project requires Python 3.x and the following libraries:

TensorFlow: For making Neural Networks numpy: For numerical computing and array operations. pandas: For data manipulation and analysis. matplotlib: For creating visualizations such as line plots, bar plots, and histograms. seaborn: For advanced data visualization and creating statistically-informed graphics. scikit-learn: For machine learning algorithms and model training. To run the project:

Clone this repository.

Install the required libraries. Run the Jupyter Notebook: jupyter notebook flower_classification.ipynb Additional Information Link to code: https://github.com/Harshjaglan01/flower-classification-cnn License: MIT License

Context

I am working on a Bachelor's degree in Computer Science, and I choose Machine Learning and more specifically image recognition as my work theme, This is the dataset I created to facilitate experimenting and training models.

Content

This data was obtained with a python crawler on Google Image Search, resized to occupy less memory, and manually filtered from failed results. The filtering was done by me, manually, so there may be some images that do not fit properly.

Usage

Feel free to use images from this dataset to increase your own training materials, or even test your models.

General Information

The total amount of images: 115944 Average width for all images: 271 px Average height for all images: 242 px Average number of images per label: 387 Least images per label: 222 Most images per label: 483

List of Flowers

'Abutilon', 'Acacia', 'Aconite', 'AfricanDaisy', 'Agapanthus', 'Ageratum', 'Alchemilla', 'Allium', 'Alstroemeria', 'Alyssum', 'Amaranthus', 'Amaryllis', 'Anemone', 'AniseHyssop', 'ArmeriaMaritima', 'Aster', 'Azalea', 'Baby’sBreath', 'Bachelor’sButton', 'BalloonFlower', 'Ballota', 'BeeBalm', 'Begonia', 'Bellflower', 'Bergamot', 'Bergenia', 'Billbergia', 'Black-eyedSusan', 'BlanketFlower', 'BlazingStar', 'BleedingHeart', 'Bletilla', 'Blue-eyedGrass', 'Bluebonnets', 'BluestarFlower', 'Borage', 'Bottlebrush', 'Bouvardia', 'Brachyscome', 'Brassica', 'Broom', 'Buttercup', 'ButterflyBush', 'Calceolaria', 'Calendula', 'CaliforniaPoppy', 'CallaLily', 'Camellia', 'Candytuft', 'CannaLily', 'CapeLeadwort', 'CapePrimrose', 'CardinalFlower', 'Carnation', 'Catharanthus', 'Catmint', 'Celosia', 'CerastiumTomentosum', 'Chicory', 'Chionodoxa', 'Chrysanthemum', 'Clarkia', 'Clematis', 'Clover', 'Columbine', 'Coneflower', 'CoralBells', 'CoralVine', 'Coreopsis', 'Cornflower', 'Corydalis', 'Cosmos', 'Cotoneaster', 'Crocosmia', 'Crocus', 'CrownImperial', 'CuckooFlower', 'Cyclamen', 'Daffodil', 'Dahlia', 'Daisy', 'Dandelion', 'Daphne', 'Daylily', 'Decumaria', 'Delphinium', 'DesertRose', 'Deutzia', 'Dianella', 'Dianthusbarbatus', 'Diascia', 'Dietes', 'Dill', 'Disa', 'DutchIris', 'Echinops', 'Echium', 'Elder', 'EnglishBluebell', 'Epimedium', 'Eremurus', 'Erica', 'Erigeron', 'Euphorbia', 'Eustoma', 'EveningPrimrose', 'FallCrocus', 'Feverfew', 'Firethorn', 'FlamingKaty', 'FlannelFlower', 'FlaxFlower', 'FloweringDogwood', 'ForgetMeNot', 'Forsythia', 'FourO’clock', 'Foxglove', 'FrangipaniFlower', 'Freesia', 'FrenchMarigold', 'Fuchsia', 'Gaillardia', 'Gardenia', 'Gazania', 'Geranium', 'GerberaFlower', 'Gladiolas', 'Goldenrod', 'GrapeHyacinth', 'Guzmania', 'Hawthorn', 'Heather', 'Hebe', 'Helenium', 'Helichrysum', 'Heliotrope', 'Hellebore', 'Hibiscus', 'Holly', 'Hollyhock', 'Honeysuckle', 'Hosta', 'Hyacinth', 'Hydrangea', 'Hyssop', 'IcelandPoppy', 'IcePlant', 'Impatiens', 'IpomoeaLobata', 'Iris', 'Ixia', 'Ixora', 'Jacob’sLadder', 'Jasmine', 'JohnnyJumpUp', 'KaffirLily', 'Kalmia', 'KangarooPaw', 'Knautia', 'Kniphofia', 'Lady’sSlipper', 'Laelia', 'Lantana', 'Larkspur', 'Lavatera', 'Lavender', 'LemonVerbena', 'Lewesia', 'Lilac', 'Lily', 'LilyoftheValley', 'Linaria', 'Lotus', 'LoveintheMist', 'Lunaria', 'Lupin', 'Magnolia', 'MalteseCross', 'Mandevilla', 'MargueriteDaisy', 'Marigold', 'Matthiola', 'Mayflower', 'Meconopsis', 'Mimosa', 'Monk’sHood', 'MoonflowerVine', 'Moraea', 'MorningGlory', 'MossRose', 'Narcissus', 'Nasturtium', 'Nemesia', 'Nemophila', 'Neoregelia', 'Nerine', 'NewZealandTeaTree', 'Nierembergia', 'Nolana', 'Oleander', 'Olearia', 'Orchid', 'OrientalLily', 'OrientalPoppy', 'OrnamentalCherry', 'Ornithogalum', 'Osteospermum', 'Oxalis', 'OxeyeDaisy', 'OysterPlant', 'PaintedDaisy', 'Pansy', 'Parodia', 'PassionFlower', 'PeaceLily', 'Pelargonium', 'Penstemon', 'Peony', 'Periwinkle', 'PersianButtercup', 'Petunia', 'Phlox', 'Photinia', 'Physostegia', 'PincushionFlower', 'Pinks', 'Poinsettia', 'Polyanthus', 'Poppy', 'Potentilla', 'PowderPuff', 'QuakerLadies', 'QueenoftheMeadow', 'Queen’sCup', 'Quince', 'RainLily', 'RockRose', 'Rondeletia', 'RoseofSharon', 'Roses', 'Sage', 'SaintJohn’sWort', 'Scaevola', 'ScentedGeranium', 'Scilla', 'Sedum', 'ShastaDaisy', 'Silene', 'Snapdragon', 'Snowdrop', 'Snowflake', 'Soapwort', 'Speedwell', 'Starflower', 'Statice', 'Sunflower', 'SweetPea', 'TeaRose', 'TigerFlower', 'Tithonia', 'TobaccoPlant', 'Trachelium', 'Trillium', 'Triteleia', 'Tritoniacrocata', 'Trollius', 'TrumpetVine', 'Tuberose', 'Tulip', 'UrnPlant', 'Ursinia', 'UvaUrsi', 'Valerian', 'Verbena', 'Viburnum', 'Viola', 'VirginiaCreeper', 'Wallflower', 'Wandflower', 'Waterlilies', 'Watsonia', 'WaxPlant', 'Wedelia', 'Weigela', 'WhirlingButterflies', 'Winterberry', 'WinterJasmine', 'WishboneFlower', 'WoollyViolet', 'Xanthocerassorbifolium', 'Xerophyllum', 'Xylobium', 'Xylosma', 'Yarrow', 'Yellow-eyedGrass', 'YellowArchangel', 'YellowBell', 'Zenobia', 'Zinnia'

Description: - This dataset includes all 22 built-in datasets from the Seaborn library, a widely used Python data visualization tool. Seaborn's built-in datasets are essential resources for anyone interested in practicing data analysis, visualization, and machine learning. They span a wide range of topics, from classic datasets like the Iris flower classification to real-world data such as Titanic survival records and diamond characteristics.

• Included Datasets:
  • Anagrams: Analysis of word anagram patterns.
  • Anscombe: Anscombe's quartet demonstrating the importance of data visualization.
  • Attention: Data on attention span variations in different scenarios.
  • Brain Networks: Connectivity data within brain networks.
  • Car Crashes: US car crash statistics.
  • Diamonds: Data on diamond properties including price, cut, and clarity.
  • Dots: Randomly generated data for scatter plot visualization.
  • Dow Jones: Historical records of the Dow Jones Industrial Average.
  • Exercise: The relationship between exercise and health metrics.
  • Flights: Monthly passenger numbers on flights.
  • FMRI: Functional MRI data capturing brain activity.
  • Geyser: Eruption times of the Old Faithful geyser.
  • Glue: Strength of glue under different conditions.
  • Health Expenditure: Health expenditure statistics across countries.
  • Iris: Famous dataset for classifying Iris species.
  • MPG: Miles per gallon for various vehicles.
  • Penguins: Data on penguin species and their features.
  • Planets: Characteristics of discovered exoplanets.
  • Sea Ice: Measurements of sea ice extent.
  • Taxis: Taxi trips data in a city.
  • Tips: Tipping data collected from a restaurant.
  • Titanic: Survival data from the Titanic disaster.

This complete collection serves as an excellent starting point for anyone looking to improve their data science skills, offering a wide array of datasets suitable for both beginners and advanced users.

This contains the 10 datasets used in the Visual Domain Decathlon, part of the PASCAL in Detail Workshop Challenge (CVPR 2017). The goal of this challenge is to solve simultaneously ten image classification problems representative of very different visual domains.

Some of the datasets included here are also available as separate datasets in TFDS. However, notice that images were preprocessed for the Visual Domain Decathlon (resized isotropically to have a shorter size of 72 pixels) and might have different train/validation/test splits. Here we use the official splits for the competition.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('visual_domain_decathlon', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/visual_domain_decathlon-aircraft-1.2.0.png" alt="Visualization" width="500px">

This dataset contains images of an artifical flower platform with different insects sitting on it or flying above it. All images were automatically recorded with the Insect Detect DIY camera trap, a hardware combination of the Luxonis OAK-1, Raspberry Pi Zero 2 W and PiJuice Zero pHAT for automated insect monitoring (bioRxiv preprint).

Classes

The following object classes were annotated in this dataset:

• wasp (mostly Vespula sp.)
• hbee (Apis mellifera)
• fly (mostly Brachycera)
• hovfly (various Syrphidae, e.g. Episyrphus balteatus)
• other (all Arthropods with insufficient occurences, e.g. various Hymenoptera, true bugs, beetles)
• shadow (shadows of the recorded insects)

View the Health Check for more info on class balance.

Versions

• v4 insect_detect_416_1class
  • squashed to square (aspect ratio 1:1)
  • downscaled to 416x416 pixel
  • all classes merged into one class ("insect")
• v5 insect_detect_raw_4K
  • original images in 4K resolution (3840x2160 pixel)
• v7 insect_detect_320_1class
  • squashed to square (aspect ratio 1:1)
  • downscaled to 320x320 pixel
  • all classes merged into one class ("insect")

Deployment

You can use this dataset as starting point to train your own insect detection models. Check the model training instructions for more information.

Open source Python scripts to deploy the trained models can be found at the insect-detect GitHub repo.