Posts Tagged ‘Classification’
DeepFashion
DeepFashion
This dataset contains images of clothing items while each image is labeled with 50 categories and annotated with 1000 attributes, bounding box and clothing landmarks in different poses. Four datasets are developed according to the DeepFashion dataset including Attribute Prediction, Consumer-to-shop Clothes Retrieval, In-shop Clothes Retrieval and Landmark Detection in which only Attribute Prediction is available without password requests. All the other datasets mentioned need to request for a password to unzip the data files and the access would be available after signing an Agreement. All these datasets are available for academic research and any commercial use is prohibited. More details about the datasets and download instructions can be found on http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html. Attribute Prediction dataset which contains 289,222 number of images, can be downloaded from http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/AttributePrediction.html.
In-shop-Clothes Retrieval dataset which contains 7,982 images can be downloaded from http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/InShopRetrieval.html.
Consumer-to-shop Clothes dataset which contains 33,881 number of images, can be downloaded from http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/Consumer2ShopRetrieval.html.
Finally, Fashion Landmark Detection dataset which contains 123,016 number of images, can be downloaded from http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html.
Here is some information regarding the DeepFashion dataset:
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Number of images in the dataset: More than 800,000 (60,000 images for the training set and 10,000 images for the test set)
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Number of classes: 50 categories
If you use this dataset:
Make sure to follow the Terms of Use according to the Agreement about the datasets and use the data for academic research purposes only.
Make sure to cite the papers:
Z. Liu, P. Luo. S. Qiu, X. Wang, X. Tang, Powering Robust Clothes Recognition and Retrieval with Rich Anotations, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
Z. Liu, S. Yan, P. Luo, X. Wang, X. Tang, Fashion Landmark Detection In The Wild, European Conference on Computer Vision (ECCV), 2016.
Keywords: Vision, Image, Classification, Fashion, Clothes Recognition, Clothes Detection
Fashion MNIST
Fashion MNIST:
This dataset contains grayscale images for clothing generated by Zalando (https://jobs.zalando.com/tech/). The dataset is created to be a substitute for the original MNIST dataset for machine learning algorithms. This substitution seems necessary because achieving very high classification accuracies is easy by classical machine learning algorithms. Also, MNIST might have been overused. As a result, Fashion MNIST shares the same image size, training and test sizes and number of classes with original MNIST.
Here is some information regarding the Fashion MNIST dataset:
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Number of images in the dataset: 70,000 (60,000 images for the training set and 10,000 images for the test set)
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Image size: 28×28
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Number of classes: 10 (T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot)
4 data files are available for download from https://github.com/zalandoresearch/fashion-mnist which contain training set images, training set labels, test set images and test set labels. Instead of downloading the dataset, you might clone the GitHub repository in the same address provided above. More details and loading commands can be found in the same GitHub repository. You might also be interested to take a look at the Kaggle page https://www.kaggle.com/zalando-research/fashionmnist/home.
If you use this dataset, make sure to cite the paper:
Han Xiao, Kashif Rasul, Roland Vollgraf. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms, 2017.
keywords: Vision, Image, Classification, Fashion, Clothes Recognition, Clothes Detection
MNIST
MNIST:
This dataset contains grayscale images for handwritten digits in which half of the training set and half of the test set are collected among Census Bureau employees and the second half of each training and test sets are collected among high school students. The dataset is a subset of images from two parent datasets NIST’s Special Database 3 and Special Database 1.
Here is some information regarding the MNIST dataset:
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Number of images in the dataset: 70,000 (60,000 images for the training set: 30,000 from NIST’s Special Database 3 and 30,000 from NIST’s Special Database 1. 10,000 images for the test set: 5000 from Special Database 3 and 5000 from Special Database 1)
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Image size: 28×28
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Number of classes: 10 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
4 data files are available for download from http://yann.lecun.com/exdb/mnist/ which contain training set images, training set labels, test set images and test set labels. Please note that the images in this dataset do not have the image format and the user is supposed to write a short code to read them. The details about the file format is available on the mentioned address.
keywords: Vision, Image, Classification, Handwritten Digits
CIFAR-10 & CIFAR-100
CIFAR-10 & CIFAR-100:
These two datasets are labeled images from a parent dataset called Tiny Images Dataset (which is available on http://horatio.cs.nyu.edu/mit/tiny/data/index.html).
CIFAR-10:
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Number of images in the dataset: 60,000 (50,000 images for training divided into 5 batches and 10,000 images for test in one batch)
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Image size: 32×32
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Number of classes: 10 (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck)
3 different versions are available for this dataset each suitable for either Python, Matlab or C programming, and they can be downloaded from https://www.cs.toronto.edu/~kriz/cifar.html.
If you use this dataset, make sure to cite the following tech report.
Alex Krizhevsky, Learning multiple layers of features from tiny images, 2009.
CIFAR-100:
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Number of images in the dataset: 60,000 (50,000 images for training while 500 images belong to each class and 10,000 images for test while 100 images belong to each class for test)
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Image size: 32×32
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Number of classes: 100 , provided in detail in bellow (copied from http://www.cs.toronto.edu/~kriz/cifar.html)
The 100 classes belong to 20 Superclasses that determines the “coarse” label and the “fine” label refers to the class that image belongs to.
Superclass and Classes:
aquatic mammals: beaver, dolphin, otter, seal, whale
fish: aquarium fish, flatfish, ray, shark, trout
flowers: orchids, poppies, roses, sunflowers, tulips
food containers: bottles, bowls, cans, cups, plates
fruit and vegetables: apples, mushrooms, oranges, pears, sweet peppers
household electrical devices: clock, computer keyboard, lamp, telephone, television
household furniture: bed, chair, couch, table, wardrobe
insects: bee, beetle, butterfly, caterpillar, cockroach
large carnivores: bear, leopard, lion, tiger, wolf
large man-made outdoor things: bridge, castle, house, road, skyscraper
large natural outdoor scenes: cloud, forest, mountain, plain, sea
large omnivores and herbivores: camel, cattle, chimpanzee, elephant, kangaroo
medium-sized mammals: fox, porcupine, possum, raccoon, skunk
non-insect invertebrates: crab, lobster, snail, spider, worm
people: baby, boy, girl, man, woman
reptiles: crocodile, dinosaur, lizard, snake, turtle
small mammals: hamster, mouse, rabbit, shrew, squirrel
trees: maple, oak, palm, pine, willow
vehicles 1: bicycle, bus, motorcycle, pickup truck, train
vehicles 2: lawn-mower, rocket, streetcar, tank, tractor
3 different versions are available for this dataset each suitable for either Python, Matlab or C programming, and they can be downloaded from https://www.cs.toronto.edu/~kriz/cifar.html.
If you use this dataset, make sure to cite the following tech report.
Alex Krizhevsky, Learning multiple layers of features from tiny images, 2009.
Keywords: Vision, Image, Classification, Natural Images
ImageNet
ImageNet:
This dataset contains images which are organized according to the WordNet hierarchy (WorldNet 3.0) in which every node refers to up to thousands of images. Each concept in WorldNet is described by synonym sets (synsets) which are words and phrases. ImageNet aims to have 1000 images per synset on average.
Because the images are subject to copyright and ImageNet does not own the images, only thumbnails and URLs of the images are available by ImageNet. The original image dataset can only be provided to students and researchers under certain conditions. Also image data is available for download (only for educational purposes) through Visual Recognition Challenges by registration.
The following information is from http://image-net.org/about-stats
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Number of non-empty synsets: 21841
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Number of images: 14,197,122
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Number of images with bounding box annotations: 1,034,908
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Number of synsets with SIFT features: 1000
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Number of images with SIFT features: 1.2 million
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Number of categories: 22,000 with 500-1000 images per category
The ImageNet URLs can be downloaded from the following link:
http://image-net.org/download-imageurls
To access the WorldNet hierarchy and the WorldNet documentation, please refer to the following link:
http://image-net.org/download-API
The complete information about the dataset and contact link for download can be found at: http://image-net.org/about-overview and http://image-net.org/
Find information about the ImageNet Object Localization Challenge available on Kaggle:
https://www.kaggle.com/c/imagenet-object-localization-challenge
keywords: Vision, Image, Classification, Natural Images
WESAD: Wearable Stress and Affect Detection
WESAD: Wearable Stress and Affect Detection:
This dataset contains physiological and motion data of 15 subjects collected by using a wrist and a chest device worn. The chest-worn device records ECG, Electrodermal Activity, Electromyogram, Respiration, Body Temperature and Three-access Acceleration and the wrist-worn device records Blood Volume Pulse, Electrodermal activity, Body Temperature and Three-axis Acceleration. More details about the dataset and the links of download can be found on https://archive.ics.uci.edu/ml/datasets/WESAD+%28Wearable+Stress+and+Affect+Detection%29.
Here is some information regarding the dataset:
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Number of Instances: 63,000,000
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Number of Attributes: 12
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Number of Subjects: 15
If you use this dataset:
Make sure to use the data for academic research and non-commercial purposes only.
Make sure to cite the paper:
P. Schmidt, A. Reiss, R. Duerchen, C. Marberger, K. V. Laerhoven, Intorducing WESAD: a Multimodal Dataset for Wearable Stress and Affect Detection, International Conference on Multimodal Interaction (ICMI), 2018.
Keywords: Biology and Health, Classification, Regression, Stress Detection, Motion, Time Series
Indian Liver Patient Records
Indian Liver Patient Records:
This dataset contains the records regarding the liver conditions of people into two categories of liver patients and non-liver patients. The dataset was collected with the goal of providing a benchmark for prediction algorithms to help in diagnosing liver diseases. More information about the dataset and links of download can be found on Kaggle https://www.kaggle.com/uciml/indian-liver-patient-records/home or on UCI ML Repository on https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset).
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Number of patients: 583 (441 male and 142 female)
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Number of categories: 2 (liver patients: 416 and non-liver patients 167)
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Number of attributes: 10 (including Age, Gender, etc.)
If you use this dataset:
Make sure to provide acknowledgements and citation to the UCI Repository according to the Citation Policy https://archive.ics.uci.edu/ml/citation_policy.html.
Keywords: Biology and Health, Liver, Classification