Image Classification Datasets

Chen Chen — Publications I am excited to join the ECE department at University of North Carolina at Charlotte as an Assistant Professor in Fall 2018. Face Recognition - Databases. Fashion-MNIST About three weeks ago the Fashion-MNIST dataset of Zalando's article images, which is a great replacement of classical MNIST dataset, was released. The images for each category were originally collected from Google, Bing and Flickr. I have searched a lot but most of the available images are in JPEG format which is not a suitable format due to its lossy compression. , the lane the vehicle is currently driving on (only available for category "um"). We’re going to write a function to classify a piece of fruit Image. However, the website goes down like all the time. Currently we have an average of over five hundred images per node. Any suggestions to sites for this purpose is welcome. A large number of image datasets, e. The images are black and white, and in different sizes and shapes, with width and heights ranges roughly between 30. Our training set contains 25,000 images, including 12,500 images of dogs and 12,500 images of cats, while the test dataset contains 12,500 images. Previously, I retrained the top layers of a VGG16 convolutional neural network architecture, and I was able to surpass an accuracy of 85% with 400 images. Build a Convolution Neural Network that can classify FashionMNIST with Pytorch on Google Colaboratory with LeNet-5 architecture trained on GPU. By the way, this repository is a wonderful source for machine learning data sets when you want to try out some algorithms. The NIH Clinical Center recently released over 100,000 anonymized chest x-ray images and their corresponding data to the scientific community. computer vision deep learning machine learning. uint8 array of grayscale image data with shape (num_samples, 28, 28). The MobileNet model we shared for the above demo was trained with 1,000 classes from ImageNet ILSVRC2012, which results in a model with very good feature extractors for a variety of image classification tasks. There are two types of classification algorithms e. png ├── label2 ├── c. A pretrained network is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. Image Parsing. , (i) disease or abnormality detection, (ii) region of interest segmentation (iii) disease classification from real medical image datasets. Note to self: Add some clean-shaven, Thanksgiving-appropriate pics to dataset. (Standardized image data for object class recognition. This page provides benchmark datasets and code that can be used for evaluating the performance of extreme multi-label algorithms. Brodatz Textures. GZ2 extends the original Galaxy Zoo classifications for a subsample of the brightest and largest galaxies in the Legacy release, measuring more detailed morphological features. There are two types of classification, supervised and unsupervised, which differ with respect to the interaction between the analyst and the computer during classification. We have created a 17 category flower dataset with 80 images for each class. edu/cchen62/. Classification with a few off-the-self classifiers. Each folder in the dataset, one for testing, training, and validation, has images that are organized by class labels. An image classification ResNet model for training on the CIFAR image dataset. For standard image inputs, the tool accepts multiple-band imagery with any bit depth, and it will perform the Random Trees classification on a pixel basis or. The dataset is the first in a series to provide document images and their ground truth as a contribution to Document image analysis and recognition (DAIR) community. An image classification model is trained to recognize various classes of images. Classes are typically at the level of Make, Model, Year, e. I would be very grateful if you could direct me to publicly available dataset for clustering and/or classification with/without known class membership. computer vision deep learning machine learning. Movie human actions dataset from Laptev et al. There are several interesting things to note about this plot: (1) performance increases when all testing examples are used (the red curve is higher than the blue curve) and the performance is not normalized over all categories. This data set includes labeled reviews from IMDb, Amazon, and Yelp. Figure 4: In this example, we insert an unknown image (highlighted as red) into the dataset and then use the distance between the unknown flower and dataset of flowers to make the classification. These methods are applied in many image classification tasks. metrics import roc_auc_score import numpy as. Concretely, it is possible to find benchmarks already formatted in KEEL format for classification (such as standard, multi instance or imbalanced data), semi-supervised classification, regression, time series and unsupervised learning. Only upload images to LabelMe with the goal of making them publicly available for research. CIFAR100 small image classification. Details of the MIO-TCD dataset. This repository contains a collection of many datasets used for various Optical Music Recognition tasks, including staff-line detection and removal, training of Convolutional Neuronal Networks (CNNs) or validating existing systems by comparing your system with a known ground-truth. The dataset is a product of a collaboration between Google, CMU and Cornell universities, and there are a number of research papers built on top of the Open Images dataset in the works. Recurrent Neural Network (LSTM). Affective Image Classification Using Features inspired by Psychology and Art Theory. Each image in the dataset is associated with metadata: author, view content (content), average vote of users (whether an image is good for classification or not), latitude and longitude of place where a photo has been taken and other. Unlike most other existing face datasets, these images are taken in completely uncontrolled situations with non-cooperative subjects. Each of the tiles in the mosaic is an arithmetic average of images relating to one of 53,464 nouns. Scene classification of such a huge volume of HSR-RS images is a big challenge for the efficiency of the feature learning and model training. We introduce a challenging set of 256 object categories containing a total of 30607 images. Each class has 20000images with a total of 40000 images with 227 x 227 pixels with RGB channels. In total, there are 50,000 training images and 10,000 test images. Dataset API become part of the core package; Some enhancements to the Estimator allow us to turn Keras model to TensorFlow estimator and leverage its Dataset API. It can be seen as similar in flavor to MNIST(e. In this example, images from a Flowers Dataset[5] are classified into categories using a multiclass linear SVM trained with CNN features extracted from the images. Step 1: The Image Classification Dataset Before you can start with the Image Classification retraining process, you’ll need a set of labeled images to retrain the existing model with new classes. Reuters News dataset: (Older) purely classification-based dataset with text from the. There are two types of classification algorithms e. Image Parsing. In this post, I will show you how to turn a Keras image classification model to TensorFlow estimator and train it using the Dataset API to create input pipelines. The image data set consists of 2,000 natural scene images, where a set of labels is artificially assigned to each image. Burges, Microsoft Research, Redmond The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. Running on a data set with 50,000 cases and 100 variables, it produced 100 trees in 11 minutes on a 800Mhz machine. In order to assess the difficulty of this task, we show some preliminary results obtained with state-of-the-art image classification systems. Why it is important to work with a balanced classification dataset. In our blog post we will use the pretrained model to classify, annotate and segment images into these 1000 classes. A method for object-oriented land cover classification combining Landsat TM data and aerial photographs. Launched by the U. Only upload images to LabelMe with the goal of making them publicly available for research. The data is organized in 2 different ways, one based on image content type (subcellular, cellular and tissue level data) and the other one is based on the image processing methodology (segmentation or classification or tracking). There are two types of classification, supervised and unsupervised, which differ with respect to the interaction between the analyst and the computer during classification. Next you could try to find more varied data sets to work with - perhaps identify traffic lights and determine their colour, or recognise different street signs. This version contains the depth sequences that only contains the human (some background can be cropped though). Creating a subset of bands for the classification. Affective Image Classification Using Features inspired by Psychology and Art Theory. The Comprehensive Cars (CompCars) dataset contains data from two scenarios, including images from web-nature and surveillance-nature. 15,851,536 boxes on 600 categories. The other variables have some explanatory power for the target column. Facebook is training some models on as many as 50 million images, and scaling up to billions of training images is unfeasible when all supervision is supplied by hand. Butterfly-200 - Butterfly-20 is a image dataset for fine-grained image classification, which contains 25,279 images and covers four levels categories of 200 species, 116 genera, 23 subfamilies, and 5 families. Back then, it was actually difficult to find datasets for data science and machine learning projects. Each image has been rated on 6 emotion adjectives by 60 Japanese subjects. Once you’re done, simply File>Save and you’ll original hand-dataset. The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) evaluates algorithms for object detection and image classification at large scale. The hyperspectral synthetic image collections are distributed in ZIP files containing five MAT files each. The solution builds an image classification system using a convolutional neural network with 50 hidden layers, pretrained on 350,000 images in an ImageNet dataset to generate visual features of the images by removing the last network layer. This dataset is another one for image classification. Course Description. Use deep convolutional generative adversarial networks (DCGAN) to generate digit images from a noise distribution. php/Data_Preprocessing". Also, please consult the dataset description page for a complete explanation of the dataset. The training data needs to be structured into 3 folders: training , validation and test with the following split:. Image classification results on PASCAL’07 train/val set Method: bag-of-features + χ2 -SVM classifier MSDense x SIFT 0. The random trees classifier is a powerful technique for image classification that is resistant to overfitting and can work with segmented images and other ancillary raster datasets. CNNs trained on Places365 (new Places2 data) are also released. There's an interesting target column to make predictions for. More about us. From a practical point of view, this means automatic processing of carefully captured images to produce a dataset of desired measurements from the images. The said results may be improved if data preprocessing techniques were employed on the datasets, and if. Introduction This is a publicly available benchmark dataset for testing and evaluating novel and state-of-the-art computer vision algorithms. py file and resized to 128 x 128 x 3 size. ndim – Number of dimensions of each image. Conduct field surveys and collect ground information and other ancillary data of the study area. Image classification - fast. xml and image_metadata_stylesheet. Natural Language Processing. Open Source Software in Computer Vision. Dataset API become part of the core package; Some enhancements to the Estimator allow us to turn Keras model to TensorFlow estimator and leverage its Dataset API. Classification, Clustering. This is the largest public dataset for age prediction to date. , types of land cover), they should appear as patterns in the characteristics of the phenomena. People in action classification dataset are additionally annotated with a reference point on the body. There’s an interesting target column to make predictions for. Image Classification in TensorFlow : Cats and Dogs dataset Learn DL Code TF. Datasets for image classification. In this paper, we address the challenge of land use and land cover classification using remote sensing satellite images. The dataset is divided into five training batches and one test batch, each with 10000 images. These 60,000 images are partitioned into a training. The author Sachdeva et al. There is additional unlabeled data for use as well. INRIA Holiday images dataset. The database contains 213 images of 7 facial expressions (6 basic facial expressions + 1 neutral) posed by 10 Japanese female models. To use all bands in an image dataset in the classification, add the image dataset to ArcMap and select the image layer on the Image Classification toolbar. We present a new dataset for India, consisting of 21,030 polygons from across the country that were manually classified as “built-up” or “not built-up,” which we use for supervised image classification and detection of urban areas. Benchmark datasets in computer vision. Details of the MIO-TCD dataset. The goal of the challenge is to provide an automatic classification of each input image. In the rest of this document, we list routines provided by the gluon. The mean-standard deviation method is particularly useful when our purpose is to show the deviation from the mean of our data array. This blog post is part two in our three-part series of building a Not Santa deep learning classifier (i. I am new in MATLAB,I have centers of training images, and centers of testing images stored in 2-D matrix ,I already extracted color histogram features,then find the centers using K-means clustering algorithm,now I want to classify them using using SVM classifier in two classes Normal and Abnormal,I know there is a builtin function in MATLAB but I don't know to adapt it to be used in this job. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Various other datasets from the Oxford Visual Geometry group. We will use a subset of the CalTech256 dataset to classify images of 10 different kinds of animals. Arial Verdana Times New Roman Wingdings Tahoma Profile MathType 4. While there are many databases in use currently, the choice of an appropriate database to be used should be made based on the task given (aging, expressions,. This comes mostly in the form of intense colors and sometimes wrong labels. As we know machine learning is all about learning from past data, we need huge dataset of flower images to perform real-time flower species recognition. , we create four different dataset from this original dataset such that each dataset is only associated with a specific category. In the following sections we will introduce some datasets that you might find useful if you want to use machine learning for image classification. If this original dataset is large enough and general enough, then the spatial hierarchy of. Flexible Data Ingestion. Each image is labeled with one of 10 classes (for example "airplane, automobile, bird, etc"). Movie human actions dataset from Laptev et al. I would be very grateful if you could direct me to publicly available dataset for clustering and/or classification with/without known class membership. classification, for example the one presented by Rieck et al. Image classification is the following task: You have an image and you want to assign it one label. scan service described later. Data Sets & Images AVA dataset. Some of the most important datasets for image classification research, including CIFAR 10 and 100, Caltech 101, MNIST, Food-101, Oxford-102-Flowers, Oxford-IIIT-Pets, and Stanford-Cars. This post is a comparison between R & Python for applying the pretrained imagenet VGG19 model shipped with keras. This dataset presents the age-adjusted death rates for the 10 leading causes of death in the United States beginning in 1999. In this tutorial, we show how to use a pre-trained Inception-BatchNorm network to predict the class of an image. ai datasets. Generally, to avoid confusion, in this bibliography, the word database is used for database systems or research and would apply to image database query techniques rather than a database containing images for use in specific applications. This method is useful to understand how classification regions have changed over time (Figure 3). The dataset is divided into five training batches and one test batch, each with 10000 images. Each image is labeled with one of 10 classes (for example “airplane, automobile, bird, etc”). Classes are typically at the level of Make, Model, Year, e. Text Datasets. This serves as typically the first dataset to practice image recognition. I am doing some project on medical image processing and I need some uncompressed medical images especially magnetic resonance angiography, vessel and so on. We are in the process of updating all the results for the new datasets. Three NASA NEX data sets are now available to all via Amazon S3. Interesting links. medical images to eventually develop “knowledgeable ” computer systems[11][12]. The whole process is identical to the standard data mining process. For this program, we shall pass images in the batch of 16 i. org with any questions. The MNIST dataset is one of the most common datasets used for image classification and accessible from many different sources. A polygon feature class or a shapefile. I just have images and need to make a dataset of some features. , HeLa (Boland & Murphy, 2001), Mivia (Foggia, Percannella, Soda, & Vento, 2010), and MondialMarmi (Fernández, Ghita, González, Bianconi, & Whelan, 2010), to name a few, have been used to benchmark LBP and LBP-based descriptors. Datasets for classification, detection and person layout are the same as VOC2011. Publications. There are 50000 training images and 10000 test images. One of the interesting things that a deep learning algorithm can do is classify real world images. UC Merced Land Use Dataset Download the dataset. Thus, the objective of this paper presents an appraisal of the existing and conventional methods for the classification of medical images and based on these observations; propose a new framework for medical image classification. I just have images and need to make a dataset of some features. This dataset is another one for image classification. Figure 4: In this example, we insert an unknown image (highlighted as red) into the dataset and then use the distance between the unknown flower and dataset of flowers to make the classification. image dataset called NICO, which makes use of contexts to create Non-IIDness consciously. The original Caltech-101 [1] was collected by choosing a set of object categories, downloading examples from Google Images and then manually screening out all images that did not fit the category. Facial recognition. Prerequisite: Image Classifier using CNN. xsl files into the /images folder and run the following python file to generate the final detector. We randomly sub-sampled these datasets D2 and D3, in order to balance sample size among all the DCs. The dogs dataset comes with about 150 images for each breed, which is good enough for this tutorial. In multi-class classification, a balanced dataset has target labels that are evenly distributed. Introduction This is a publicly available benchmark dataset for testing and evaluating novel and state-of-the-art computer vision algorithms. The PubFig database is a large, real-world face dataset consisting of 58,797 images of 200 people collected from the internet. CIFAR100 small image classification. That's why we've created a home. The toolbox will allow you to customize the portion of the database that you want to download, (2) Using the images online via the LabelMe Matlab toolbox. The deep convolutional neural network (CNN), a typical deep learning model, is an efficient end-to-end deep hierarchical feature learning model that can capture the intrinsic features of input HSR-RS images. USGS Land Cover US Land Cover CONUS Descriptions Global Land Cover North American Land Cover. Note that the image source is camera. The purpose of this markup is to improve. In this post, we will look into one such image classification problem namely Flower Species Recognition, which is a hard problem because there are millions of flower species around the world. The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. scan service described later. I've a set of images that have a single classification of OPEN (they show something that is open). Inside Science column. The image dataset comes with annotations that mark out the bounding boxes. The size of each image is 32 by 32 pixels. 127,915 CAD Models 662 Object Categories 10 Categories with Annotated Orientation. While supervised learning techniques require knowledgeable operators to train large image data sets, such experts are often unavailable to perform this task. We make use of a bag-of-visual-words method (cf Csurka et al 2004). Deep Learning - Image Classification and Similar Image Retrieval In this tutorial i will show you how to build a deep learning network for image recognition CIFAR-10 data set. This is because, the set is neither too big to make beginners overwhelmed, nor too small so as to discard it altogether. 01/19/2018; 14 minutes to read +7; In this article. Classification Datasets. We make use of a bag-of-visual-words method (cf Csurka et al 2004). Sample experiment that uses multiclass classification to predict the letter category as one of the 26 capital letters in the English alphabet. To our best knowledge, this is the first time, image gradient based features have been used for hyperspectral image classification. Available here. The categories can be seen in the figure below. Next, a few sklearn models are trained on this flattened data. Google Cloud Vision API is a popular service that allows users to classify images into categories, appropriate for multiple common use cases across several industries. In particular, each class has fewer labeled training examples than in CIFAR-10, but a very large set of unlabeled. Classification. However, if you prefer to pre-train the base MobileNet model with your own dataset, you can do so as follows. Thanks and regards in advance. 4%) and CIFAR-10 data (to approx. The following table gives the detailed description of the number of images associated with different label sets, where all the possible class labels are desert, mountains, sea, sunset and trees. This dataset contains a total number of 2025 low-resolution gray-scale faces of 45 celebrities. With everything in place, let’s get started with the training process. It is planned to provide more data and ground-truth information in the fture. We make use of a bag-of-visual-words method (cf Csurka et al 2004). Three days, 5,400 images per day, 30,087 bounding boxes. It consists of 28 x 28 pixels grayscale images of 70,000 fashion products, and it has 10 categories with 7,000 images per category. The lab is aimed at applying a full learning pipeline on a real dataset, namely images of handwritten digits. Figure 4: In this example, we insert an unknown image (highlighted as red) into the dataset and then use the distance between the unknown flower and dataset of flowers to make the classification. Creating a subset of bands for the classification. Movie human actions dataset from Laptev et al. The task tumor classification is performed on two image dataset, namely the breast B-mode. datasets import make_classification from sklearn. Here, we have found the "nearest neighbor" to our test flower, indicated by k=1. The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) evaluates algorithms for object detection and image classification at large scale. ImageNet 2012 Classification Dataset. Data sets for Regression Short Course The first few data sets from the class notes are listed below. Available here. Datasets CIFAR10 small image classification. Image classification. Discriminative Spatial Saliency for Image Classification, Gaurav Sharma, Frederic Jurie and Cordelia Schmid, CVPR, 2012. We have created two flower datasets by gathering images from various websites, with some supplementary images from our own photographs. The EMNIST Digits a nd EMNIST MNIST dataset provide balanced handwritten digit datasets directly compatible with the original MNIST dataset. Multivariate, Text, Domain-Theory. Flexible Data Ingestion. Tiny ImageNet Challenge is the default course project for Stanford CS231N. The first image of each group is the query image and the correct retrieval results are the other images of the group. Download image-seg. Specifically, we propose to extend. This dataset helps for finding which image belongs to which part of house. datasets [32, 53, 24] but may be completely nonviable for the time being. The included leafsnap-dataset-images. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. dataset_cifar100. Transfer learning lets you take a small dataset and produce an accurate model. AutoML Vision needs at least 100 images for each label. This blog post is part two in our three-part series of building a Not Santa deep learning classifier (i. Thanks and regards in advance. See this page for download and more information on the benchmark dataset. The group should be used for discussions about the dataset and the starter code. A model is specified by its name. In order to make use of the multitude of digital data available from satellite imagery, it must be processed in a manner that is suitable for the end user. Image classification of the MNIST and CIFAR-10 data using KernelKnn and HOG (histogram of oriented gradients) Lampros Mouselimis 2019-04-14. used an artificial neural network and PCA-ANN for the multiclass brain tumor MRI images classification, segmentation with dataset of 428 MRI images and an accuracy of 75-90% was achieved. Benchmark Data Sets for Highly Imbalanced Binary Classification. Face Recognition - Databases. So we divide our dataset of 4750 images by keeping 80 percent images as training dataset and 20 percent as validation set. Dataset class is used to provide an interface for accessing all the training or testing samples in your dataset. The dataset is generated from 458 high-resolution images (4032x3024 pixel) with the method proposed by Zhang et al (2016). Go ahead and download the data set from the Sentiment Labelled Sentences Data Set from the UCI Machine Learning Repository. The geometric resolution is 1. Thanks and regards in advance. classification algorithm assigns pixels in the image to categories or classes of interest. datasets package embeds some small toy datasets as introduced in the Getting Started section. The included leafsnap-dataset-images. There are a total of 120 classes of dogs, with 20580 images in total, partitioned into 8580 test images, and 12000 training images. The original dataset is a multi-label classification problem with 6 different labels: {Beach,…. We will use a subset of the CalTech256 dataset to classify images of 10 different kinds of animals. For a general overview of the Repository, please visit our About page. withlabel – If True, it returns datasets with labels. The mean-standard deviation method is particularly useful when our purpose is to show the deviation from the mean of our data array. Benchmark Data Sets for Highly Imbalanced Binary Classification. /dir/train ├── label1 ├── a. We make use of a bag-of-visual-words method (cf Csurka et al 2004). Apply a bi-directional LSTM to IMDB sentiment dataset classification task. Dataset API become part of the core package; Some enhancements to the Estimator allow us to turn Keras model to TensorFlow estimator and leverage its Dataset API. One of this MAT files corresponds to the free of noise hyperspectral synthetic image, and in the other four additive noise has been added to the synthetic image given a Signal to Noise Ratio (SNR) of 20, 40, 60 and 80db respectively. I am looking for some relatively simple data sets for testing and comparing different training methods for artificial neural networks. MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges Those are in bytestream format, you should extract and convert to de. Bird experts searched for and annotated the images of birds, and thus, even birds that appeared to be very small in the whole image could be specified in detail. The PubFig database is a large, real-world face dataset consisting of 58,797 images of 200 people collected from the internet. The set of classes is very diverse. A collection of. This method uses large networks that were trained for a long time on huge datasets, transferring that knowledge into. Based on hyperspectral imaging of inoculated and mock-inoculated stem images, our 3D DCNN has a classification accuracy of 95. In this example, we will be working on one of the most extensively used datasets in image comprehension, one which is used as a simple but general benchmark. Present classification approaches concentrates on spectral features of the image. , Périlleux C. 1 was proposed. Here, we have found the "nearest neighbor" to our test flower, indicated by k=1. This data set includes labeled reviews from IMDb, Amazon, and Yelp. Back then, it was actually difficult to find datasets for data science and machine learning projects. I have searched a lot but most of the available images are in JPEG format which is not a suitable format due to its lossy compression. Check out our brand new website! Check out the ICDAR2017 Robust Reading Challenge on COCO-Text! COCO-Text is a new large scale dataset for text detection and recognition in natural images. Each image in the dataset is associated with metadata: author, view content (content), average vote of users (whether an image is good for classification or not), latitude and longitude of place where a photo has been taken and other. The dataset includes the polygons outlining all building footprints in each image, as Figure 2 shows. Examples of machine learning projects for beginners you could try include… Anomaly detection… Map the distribution of emails sent and received by hour and try to detect abnormal behavior leading up to the public scandal. Datasets for classification, detection and person layout are the same as VOC2011. This image is the equivalent of a false color infrared photograph. The data set isn’t too messy — if it is, we’ll spend all of our time cleaning the data. The whole process is identical to the standard data mining process. The other variables have some explanatory power for the target column. Upload pictures: Image names will be made lower case and spaces will be removed. Number of categories: 200. When your AI needs to differentiate between many like items - we're the ones who provide you the dataset images to train it. Load and return the digits dataset (classification). Pew Research Center makes its data available to the public for secondary analysis after a period of time. Now make a copy of the hand-dataset. The MNIST dataset is one of the most common datasets used for image classification and accessible from many different sources. Learning from Massive Noisy Labeled Data for Image Classification Tong Xiao1, Tian Xia2, Yi Yang2, Chang Huang2, and Xiaogang Wang1 1The Chinese University of Hong Kong 2Baidu Research Abstract Large-scale supervised datasets are crucial to train con-volutionalneuralnetworks(CNNs)forvariouscomputervi-sion problems. If you want to stay up-to-date about this dataset, please subscribe to our Google Group: audioset-users. I am new in MATLAB,I have centers of training images, and centers of testing images stored in 2-D matrix ,I already extracted color histogram features,then find the centers using K-means clustering algorithm,now I want to classify them using using SVM classifier in two classes Normal and Abnormal,I know there is a builtin function in MATLAB but I don't know to adapt it to be used in this job. For more information please contact: Standard Reference Data Program National Institute of Standards and Technology. A pretrained network is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. A key reason is the lacking of a well-designed dataset to support related research. Image classification datasets. Description In order to facilitate the study of age and gender recognition, we provide a data set and benchmark of face photos. The goal is to minimize or remove the need for human intervention. Also please suggest the general size of images to act as a database. The annotations cover 600 classes of objects, grouped hierarchically. , Périlleux C. ai students.