A large set of images of cats and dogs. There are 1738 corrupted images that are dropped.
To use this dataset:
import tensorflow_datasets as tfds
ds = tfds.load('cats_vs_dogs', 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/cats_vs_dogs-4.0.1.png" alt="Visualization" width="500px">
A dataset consisting of stereo thermal, stereo color, and cross-modality image pairs with high accuracy ground truth (< 2mm) generated from a LiDAR. The authors scanned 100 cluttered indoor and 80 outdoor scenes featuring challenging environments and conditions. CATS contains approximately 1400 images of pedestrians, vehicles, electronics, and other thermally interesting objects in different environmental conditions, including nighttime, daytime, and foggy scenes.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Images were gathered from Google searches and downloaded using app 'download all images' . I highly recommend this app as it is very fast and returns a zip file with the images which you can then unzip to a specific directory. I have developed a custom set of tools to create datasets. The first tool used creates a dataset framework in a specified directory I call Datasets. It inputs the name of the new dataset and creates a directory with that name and within that directory creates 4 subdirectories train, test, valid and storage. The storage directory is where the unzipped downloaded images are placed. Downloaded images can be a crazy mix of ungodly file names and image formats. I wrote a python program called order_by_size. It operates on the downloaded images, within the storage directory, It removes files with extensions that are not jpg, png, or bmp and deletes files that are below a user specified image size. Then it renames the files sequentially using "zeros" padding and converts them to jpg format, and orders the files so that the first file is the largest image size, 2nd file is the next largest and so on. For the images in your dataset you want to start with images that are large. Later these images will be cropped to a region of interest and you want these cropped images to be large and have sufficient pixel count so that features can be extracted by your classification model. Now that the files are sequentially ordered and have jpg extensions I use another program called duplicate delete. This program uses file hashing to detect duplicate images and deletes any duplicates. This prevents having images in common between the train, test and validation images when the files are partitioned. Now when you do a Google search you will get a lot of what you want and also a lot of junk. I wrote another python program called review_images that sequentially shows each of the images in the storage directory and you can elect to delete or keep the image if it is the correct type of image you want. This then eliminates unwanted images from the storage directory. Then comes the hard part. If you want to build a high quality dataset you should crop your images so that the resulting image has a high ratio of pixels in the region of interest to the total number of pixels. For that I use paint shop pro version 9. If you examine the dataset images you will see that in most cases the image of the cat takes up at least 50% of the pixels in the image. After all that is done I use the order_by_size program again with different parameters which converts all the images to a specified size. For this dataset I used 224 X 224 X3 as the image size. Now we have a uniform ordered and properly pruned set of images for a specific class like tigers for example. I wrote another python program called make_class, it inputs the new class name (tiger for example) and creates a new class sub directory in the train, test and valid directories. Then it partitions the images in the storage directory into train images, test images and validation images and stores them in the class directory of the train, test and valid directories. Finally I wrote another python program that creates a dataset csv file. To make a high quality dataset takes a lot of work but the tools I have generated helps to reduce the work load.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Small sample of the Kaggle Cats and Dogs dataset (https://www.kaggle.com/c/dogs-vs-cats/data).
Contains 1000 images for the train set (500 cats and 500 dogs), and 400 images for the test set (200 each).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
Dogs Cats is a dataset for object detection tasks - it contains Dogs And Cats annotations for 1,994 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).
## Overview
Cats is a dataset for object detection tasks - it contains Cats annotations for 1,330 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.
A large set of images of cats and dogs.
Homepage: https://www.microsoft.com/en-us/download/details.aspx?id=54765
Source code: tfds.image_classification.CatsVsDogs
Versions:
4.0.0 (default): New split API (https://tensorflow.org/datasets/splits) Download size: 786.68 MiB
Source: https://www.tensorflow.org/datasets/catalog/cats_vs_dogs
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset was created by exporting the Oxford Pets dataset from Roboflow Universe, generating a version with Modify Classes to drop all of the classes for the labeled dog breeds and consolidating all cat breeds under the label, "cat." The bounding boxes were also modified to incude the entirety of the cats within the images, rather than only their faces/heads.
https://i.imgur.com/3IEzlCf.png" alt="Annotated image of a cat from the dataset">
The Oxford Pets dataset (also known as the "dogs vs cats" dataset) is a collection of images and annotations labeling various breeds of dogs and cats. There are approximately 100 examples of each of the 37 breeds. This dataset contains the object detection portion of the original dataset with bounding boxes around the animals' heads.
Origin: This dataset was collected by the Visual Geometry Group (VGG) at the University of Oxford.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This dataset, contains a curated collection of images featuring four distinct big cat species: lions, tigers, leopards, and cheetahs. The images were sourced using the DuckDuckGo search engine and are organized into separate directories for each animal. This dataset is ideal for machine learning and computer vision projects focused on image classification and species recognition. With this dataset, you can train and validate your models to accurately differentiate between these majestic big cats.
cat dataset used for AIHW5 diffusion model.
## Overview
COCO 2017 Cats is a dataset for object detection tasks - it contains Cats annotations for 4,112 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).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
Yolov5 Using Cats is a dataset for object detection tasks - it contains Cats annotations for 458 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).
huggan/cats dataset hosted on Hugging Face and contributed by the HF Datasets community
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
This dataset was created by Sarim Javed
Released under MIT
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The Controlled Anomalies Time Series (CATS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system with 200 injected anomalies.
The CATS Dataset exhibits a set of desirable properties that make it very suitable for benchmarking Anomaly Detection Algorithms in Multivariate Time Series [1]:
Change Log
Version 2
[1] Example Benchmark of Anomaly Detection in Time Series: “Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock. Anomaly Detection in Time Series: A Comprehensive Evaluation. PVLDB, 15(9): 1779 - 1797, 2022. doi:10.14778/3538598.3538602”
About Solenix
Solenix is an international company providing software engineering, consulting services and software products for the space market. Solenix is a dynamic company that brings innovative technologies and concepts to the aerospace market, keeping up to date with technical advancements and actively promoting spin-in and spin-out technology activities. We combine modern solutions which complement conventional practices. We aspire to achieve maximum customer satisfaction by fostering collaboration, constructivism, and flexibility.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Dogs vs. Cats is a common binary classification task in the field of computer vision and machine learning. It involves distinguishing between images of dogs and images of cats..
tayamaken/cats dataset hosted on Hugging Face and contributed by the HF Datasets community
## Overview
Cats is a dataset for instance segmentation tasks - it contains Roads annotations for 828 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.
CATS-ISS_L2O_D-M7.2-V3-01_05kmLay is the Cloud-Aerosol Transport System (CATS) International Space Station (ISS) Level 2 Operational Day Mode 7.2 Version 3-01 5 km Layer data product. This collection spans from March 25, 2015 to October 29, 2017. CATS, which was launched on January 10, 2015, was a lidar remote sensing instrument that provided range-resolved profile measurements of atmospheric aerosols and clouds from the ISS. CATS was intended to operate on-orbit for up to three years. CATS provides vertical profiles at three wavelengths, orbiting between ~230 and ~270 miles above the Earth's surface at a 51-degree inclination with nearly a three-day repeat cycle. For the first time, scientists were able to study diurnal (day-to-night) changes in cloud and aerosol effects from space by observing the same spot on Earth at different times each day. CATS Level 2 Layer data products contain geophysical parameters and are derived from Level 1 data, at 60m vertical and 5km horizontal resolution.
According to a national pet owners survey, there was a total of approximately 95.6 million cats living in households in the United States in 2017. In the same year, some 68 percent of all U.S. households owned at least one pet.
Increasing pet expenditure
Whilst the number of households owning cats, and pets in general, has remained relatively consistent over the last few years, pet industry expenditure has steadily grown. Consumers are expected to spend a record breaking 75.38 billion U.S. dollars on their pets in 2019. The majority of pet market revenue comes from food sales, followed by veterinary care costs.
Shopping location preferences
When it comes to shopping locations, most consumers still purchase their pet products in physical retail stores. However, the number of consumers buying pet products online is on the rise. Dry cat food was the number one pet product bought online by cat owners in the United States in 2018.
A large set of images of cats and dogs. There are 1738 corrupted images that are dropped.
To use this dataset:
import tensorflow_datasets as tfds
ds = tfds.load('cats_vs_dogs', 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/cats_vs_dogs-4.0.1.png" alt="Visualization" width="500px">