How to approach an image classification dataset: Thinking per "label" The label structure you choose for your training dataset is like the skeletal system of your classifier. You can access the Fashion MNIST directly from Keras. dog … rat. As you will be the Scikit-Learn library, it is best to use its helper functions to download the data set. The categories are: altar, apse, bell tower, column, dome (inner), dome (outer), flying buttress, gargoyle, stained glass, and vault. CoastSat Image Classification Dataset – Used for an open-source shoreline mapping tool, this dataset includes aerial images taken from satellites. We begin by preparing the dataset, as it is the first step to solve any machine learning problem you should do it correctly. Text . We hope that the datasets above helped you get the training data you need. File . It can be used for object segmentation, recognition in context, and many other use cases. Copy to Drive Connect RAM. Insert . Much work have been done in the domain of handwritten and printed text separation albeit work related to doctor's handwriting. They’re good starting points to test and debug code. Can automatically help identify animals in the wild taken by wildlife conservatories. A pretrained network is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. 8. CIFAR-10 is a very popular computer vision dataset. from keras.datasets import cifar10 . Human annotators classified the images by gender and age. Sign in. Let’s therefore do the same only let’s make it even more exciting by using a dataset curated on Kaggle called LEGO Minifigures classification. SAT-6 has six broad land cover classes, includes barren land, trees, grassland, roads, buildings and water bodies. Images Classification 2015 S. Basu et al. The database features detailed visual knowledge base with captioning of 108,077 images. The Oxford-IIIT Pet Dataset is good for fine-grained cat and dog classification. Both datasets are relatively small and are used to verify that an algorithm works as expected. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. total their are 400 images in the training dataset Test Data : Test data contains 50 images of each cars and planes i.e. Image dataset with Contexts). 2500 . Visual Genome: Visual Genome is a dataset and knowledge base created in an effort to connect structured image concepts to language. SAT-6 Airborne Dataset Images were extracted from the National Agriculture Imagery Program (NAIP) dataset. Step 5: Second Convolutional Layer and Pooling Layer . Without worrying too much on real-time flower recognition, we will learn how to perform a simple image classification task using computer vision and machine learning algorithms with the help of Python. Download image-seg.tar.gz Predict the object class of a 3x3 patch from an image of an outdoor scence. How to report confusion matrix. © 2020 Lionbridge Technologies, Inc. All rights reserved. Still can’t find the right image data? How to approach an image classification dataset: Thinking per "label" The label structure you choose for your training dataset is like the skeletal system of your classifier. Is organized according to the WordNet hierarchy, in which each node of the hierarchy is depicted by hundreds and thousands of images. In this guide, we will build an image classification model from start to finish, beginning with exploratory data analysis (EDA), which will help you understand the shape of an image and the distribution of classes. Additional connection options Editing. The exact amount of images in each category varies. Tools . Image classification from scratch. 2011 The dataset was originally built to tackle the problem of indoor scene recognition. Check out our services for image classification, or contact our team to learn more about how we can help. ; Standardized: Data is pre-processed into same format, which requires no background knowledge for users. Returns. Today, MNIST serves as more of sanity checks than as a benchmark. Download letter.tar.gz Classify an image as one of 26 upper case letters. Edit . Classification Datasets. Youtube-8M: a large-scale labeled dataset that consists of millions of YouTube video IDs, with annotations of over 3,800+ visual entities. This dataset is a collection of 1,125 images divided into four categories such as cloudy, rain, shine, and sunrise. With 20 years of experience, we’ll ensure that getting tagged image data is quick, cost-effective and accurate. We then navigate to Data to download the dataset using the Kaggle API. View . It consists of 60,000 images of 10 classes (each class is represented as a row in the above image). ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. Can choose from 11 species of plants. 2,785,498 instance segmentations on 350 categories. CelebFaces: Face dataset with more than 200,000 celebrity images, each with 40 attribute annotations. Let’s take an example to better understand. This will take you from a directory of images on disk to a tf.data.Dataset in just a couple lines of code. This dataset is well studied in many types of deep learning research for object recognition. ImageNet: The de-facto image dataset for new algorithms. Share. Image Classification: People and Food – This dataset comes in CSV format and consists of images of people eating food. The dataset is divided into five training batches and one test batch, each containing 10,000 images. Indoor Scenes Images – From MIT, this dataset contains over 15,000 images of indoor locations. This goal of the competition was to use biological microscopy data to develop a model that identifies replicates. GID consists of two parts: a large-scale classification set and a fine land-cover classification set. Plot some images from the dataset to visualize the dataset. About Image Classification Dataset. Educational: Our multi-modal data, from multiple open medical image datasets with Creative Commons (CC) Licenses, is easy to use for educational purpose. We will use the MNIST dataset for image classification. We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images. Image Classification is the task of assigning an input image, one label from a fixed set of categories. That's where Roboflow comes in. Image Classification using own google Dataset "Musk Vs. Jobs" Image Classification using own Dataset (Google Images). what are the ideal requiremnets for data which should be kept in mind when data is collected/ extracted for Image classification. Therefore, one of the emerging techniques that overcomes this barrier is the concept of transfer learning. Add text cell. Thus, the first thing to do is to clearly determine the labels you'll need based on your classification goals. Each image is going to be with a shape as (3, 200, 200) Also I have something like 40 images on each folder (train and test) How dose it look my data folders? test. Intel Image Classification – Created by Intel for an image classification contest, this expansive image dataset contains approximately 25,000 images. Steps for Image Classification on CIFAR-10: 1. The inputs are simple statistical features derived from the pixels in the image. Receive the latest training data updates from Lionbridge, direct to your inbox! 2 hypothesis between training and testing data is the basis of numerous image classification methods. cat. The dataset is divided into 6 parts – 5 training batches and 1 test batch. To help your autonomous vehicle become a key player in the industry, Lionbridge offers the outsourcing and scalability of image annotation, so that you can focus on the bigger picture. In this paper, we systematically study the effect of variations in the training data by evaluating deep features trained on different image sets in a few-shot classification setting. This goal of the competition was to use biological microscopy data to develop a model that identifies replicates. Therefore, one of the emerging techniques that overcomes this barrier is the concept of transfer learning. This tutorial shows how to classify images of flowers. MS COCO: COCO is a large-scale object detection, segmentation, and captioning dataset containing over 200,000 labeled images. TensorFlow Sun397 Image Classification Dataset – Another dataset from Tensorflow, this dataset contains over 108,000 images used in the Scene Understanding (SUN) benchmark. Image classification using Xgboost: An example in Python using CIFAR10 Dataset. A polygon feature class or a shapefile. In literature, however, the Non-I.I.D. Requirements for Images(dataset) for an image classification problem? The Open Image dataset provides a widespread and large scale ground truth for computer vision research. While it had a good run as a benchmark dataset, even simple models by today’s standards achieve classification accuracy over 95%, making it unsuitable for distinguishing between stronger models and weaker ones. This dataset is a collection of 1,125 images divided into four categories such as cloudy, rain, shine, and sunrise. Document image classification is not as well studied as natural image classification. total their are 100 images in the test dataset. This is one of the core problems in Computer Vision that, despite its simplicity, has a large variety of practical applications. © 2020 Lionbridge Technologies, Inc. All rights reserved. Updated on April 19, 2020. CIFAR-10: A large image dataset of 60,000 32×32 colour images split into 10 classes. I.I.D. 9. The dataset contains a vast amount of data spanning image classification, object detection, and visual relationship detection across millions of images and bounding box annotations. Computer vision enables computers to understand the content of images and videos. For our module 4 project, my partner Vicente and I wanted to create an image classifier using deep learning.. Purpose: Classify species of animals based on pictures. letter. total their are 100 images in the test dataset. Real . Ctrl+M B. SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. The results of your image classification will be compared with your reference data for accuracy assessment. Recursion Cellular Image Classification – This data comes from the Recursion 2019 challenge. Create a dataset This is because, the set is neither too big to make beginners overwhelmed, nor too small so as to discard it altogether. The MNIST data set contains 70000 images of handwritten digits. In this article, we introduce five types of image annotation and some of their applications. 7. This new dataset, which is named as Gaofen Image Dataset (GID), has superiorities over the existing land-cover dataset because of its large coverage, wide distribution, and high spatial resolution. The full information regarding the competition can be found here. image_classification_part1.ipynb_ Rename. The images are histopathological lymph node scans which contain metastatic tissue. Train Data : Train data contains the 200 images of each cars and planes i.e. VGG-16 paper was released by researchers at the University of Oxford in 2015. First, you will use high-level Keras preprocessing utilities and layers to read a directory of images on disk. Image classification is a method to classify the images into their respective category classes using some method like : ... cars and planes. The Snapshot Serengeti dataset covers 40 mammals from the African Savannah. Image Classification with MNIST Dataset. Flowers: Dataset of images of flowers commonly found in the UK consisting of 102 different categories. My images. 2011 We hope ImageNet will become a useful resource for researchers, educators, students and all of you who share our passion for pictures. The Train, Test and Prediction data is separated in each zip files. Stanford Dogs Dataset: The dataset made by Stanford University contains more than 20 thousand annotated images and 120 different dog breed categories. keras. Stanford Dogs Dataset: Contains 20,580 images and 120 different dog breed categories, with about 150 images per class. Next, you will write your own input pipeline from scratch using tf.data.Finally, you will download a dataset from the large catalog available in TensorFlow Datasets. Plant Image Analysis: A collection of datasets spanning over 1 million images of plants. The basic idea is to label images with both main concept and contexts. GID consists of two parts: a large-scale classification set and a fine land-cover classification set. Each flower class consists of between 40 and 258 images with different pose and light variations. For example, we find the Shopee-IET Machine Learning Competition under the InClass tab in Competitions. Help . what are the ideal requiremnets for data which should be kept in mind when data is collected/ extracted for Image classification. Then, you can craft your image dataset accordingly. Furthermore, the images are divided into the following categories: buildings, forest, glacier, mountain, sea, and street. Thus, the first thing to do is to clearly determine the labels you'll need based on your classification goals. The image categories are sunrise, shine, rain, and cloudy. Image Tools helps you form machine learning datasets for image classification. Insert code cell below. Building a model to do image classification (MNIST digit recognition) marks the start of the deep learning journey for many beginners. It contains just over 327,000 color images, each 96 x 96 pixels. If you like, you can also write your own data loading code from scratch by visiting the load images tutorial. CIFAR-10: A large image dataset of 60,000 32×32 colour images split into 10 classes. Open settings. 2500 . Key Features. The quality and generality of deep image features is crucially determined by the data they have been trained on, but little is known about this often overlooked effect. The dataset also includes meta data pertaining to the labels. image-seg. All geographic information systems rely on a large foundation of structured geospatial data. And while they're consistently getting better, the ease of loading your own dataset seems to stay the same. In fact, even Tensorflow and Keras allow us to import and download the MNIST dataset directly from their API. Freelance writer working at Lionbridge; AI enthusiast. Finally, the prediction folder includes around 7,000 images. If you supplied labels, the images will be grouped into sub-folders with the label name. 4. TensorFlow patch_camelyon Medical Images– This medical image classification dataset comes from the TensorFlow website. Images for Weather Recognition – Used for multi-class weather recognition, this dataset is a collection of 1125 images divided into four categories. For those looking to build predictive models, this article will introduce 10 stock market datasets and cryptocurrency datasets for machine learning. Lionbridge brings you interviews with industry experts, dataset collections and more. I will be providing you complete code and other required files used in this article so you can do hands-on with this. MedMNIST is standardized to perform classification tasks on lightweight 28 * 28 images, which requires no background knowledge. train. Architectural Heritage Elements – This dataset was created to train models that could classify architectural images, based on cultural heritage. 10000 . Before feeding such image to the OCR engine, the classification of printed and handwritten texts is a necessity as doctor's prescription contains both handwritten and printed texts which are to be processed separately. A curated list of image datasets for computer vision. Even researchers who come up with any new classification technique also … The MNIST handwrttien digit data set has become the go-to guide for anyone starting out with classification in machine learning. Lionbridge brings you interviews with industry experts, dataset collections and more. In this article, we will explain the basics of CNNs and how to use it for image classification task. 10. Note: The following codes are based on Jupyter Notebook. We experimented with different neural network architectures on document image dataset. There are a few basic things about an Image Classification problem that you must know before you deep dive in building the convolutional neural network. There are around 14k images in Train, 3k in Test and 7k in Prediction. Labelled Faces in the Wild: 13,000 labeled images of human faces, for use in developing applications that involve facial recognition. Flexible Data Ingestion. The goal in computer vision is to automate tasks that the human visual system can do. dog … rat. CIFAR10 small images classification dataset load_data function. Images of Cracks in Concrete for Classification – From Mendeley, this dataset includes 40,000 images of concrete. Acknowledgements To help you build object recognition models, scene recognition models, and more, we’ve compiled a list of the best image classification datasets. The Street View House Numbers (SVHN) Dataset. Furthermore, the datasets have been divided into the following categories: medical imaging, agriculture & scene recognition, and others. However, due to limited computation resources and training data, many companies found it difficult to train a good image classification model. The data preparation is the same as the previous tutorial. cifar10. Disk. Receive the latest training data updates from Lionbridge, direct to your inbox! To download the complete dataset, click here. Classification, Clustering . 12 Best Cryptocurrency Datasets for Machine Learning, 20 Best German Language Datasets for Machine Learning, Top 12 Free Demographics Datasets for Machine Learning Projects, Top 10 Stock Market Datasets for Machine Learning, 15 Free Geographic Datasets for Machine Learning, 20 Image Datasets for Computer Vision: Bounding Box Image and Video Data, 14 Best Text Classification Datasets for Machine Learning, 12 Best Turkish Language Datasets for Machine Learning, 20 Free Sports Datasets for Machine Learning, 15 Free Datasets and Corpora for Named Entity Recognition (NER), Top 10 Vietnamese Text and Language Datasets, Top 10 Reddit Datasets for Machine Learning, 14 Best Russian Language Datasets for Machine Learning, 15 Best Chatbot Datasets for Machine Learning. Author: fchollet Date created: 2020/04/27 Last modified: 2020/04/28 Description: Training an image classifier from scratch on the Kaggle Cats vs Dogs dataset. Title: Impact of base dataset design on few-shot image classification. However, there are at least 100 images in each of the various scene and object categories. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. 1. Size: 170 MB Recursion Cellular Image Classification – This data comes from the Recursion 2019 challenge. Image classification models discern what a given image contains based on the entirety of an image's content. It is inspired by the CIFAR-10 dataset … It's also a chance to classify something other than cats and dogs. Click to connect. Authors: Othman Sbai, Camille Couprie, Mathieu Aubry. CompCars : Contains 163 car makes with 1,716 car models, with each car model labeled with five attributes, including maximum speed, displacement, number of doors, number of seats, and type of car. This release also adds localized narratives, a completely new form of multimodal annotations that consist of synchronized voice, text, and mouse traces over the objects being described. Indoor Scene Recognition: A very specific dataset, useful as most scene recognition models are better ‘outside’. Wondering which image annotation types best suit your project? A pretrained network is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. Let's load these images off disk using the helpful image_dataset_from_directory utility. The full information regarding the competition can be found here. Click here to download the aerial cactus dataset from an ongoing Kaggle competition. This Tutorial Is Aimed At Beginners Who Want To Work With AI and Keras: Prerequisites: Basic knowledge of Python ; Basic understanding of classification problems; What Is Image Classification. Labelme: A large dataset created by the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) containing 187,240 images, 62,197 annotated images, and 658,992 labeled objects. This data was initially published on https://datahack.analyticsvidhya.com by Intel to host a Image classification Challenge. load_data Loads CIFAR10 dataset. What is Image Classification? Animal Image Classification using CNN. In fact, it is only numbers that machines see in an image. TensorFlow patch_camelyon Medical Images – This medical image classification dataset comes from the TensorFlow website. We at Lionbridge AI have prepared a list of the best public sources for demographic datasets. Each batch has 10,000 images. cat. To help, we at Lionbridge have curated a list of the 15 best publicly available geographic data sources for machine learning. 3. If you’re looking for annotated image or video data, the datasets on this list include images and videos tagged with bounding boxes for a variety of use cases. Real . datasets. Looking for open source demographic data for machine learning? You'll learn to prepare data for optimum modeling results and then build a convolutional neural network (CNN) that will classify images according to whether they contain a … Although the dataset is effectively solved, it can be used as the basis for learning and practicing how to develop, evaluate, and use convolutional deep learning neural networks for image classification from scratch. We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset. From the UCI repository of machine learning databases. Image Classification is one of the most common problems where AI is applied to solve. LSUN: Scene understanding with many ancillary tasks (room layout estimation, saliency prediction, etc.). Lionbridge have curated a list of the competition was to use biological microscopy data to download the cactus. Some images from the pixels in the UK consisting of 102 different categories a list of the areas of learning! Agriculture Imagery Program ( NAIP ) dataset than as a benchmark and highly effective approach to deep research. Suit your project found it difficult to train a good image classification dataset – used multi-class. University of Oxford in 2015 and download the aerial cactus dataset from an ongoing Kaggle competition for! Experimented with different neural network architectures on document image classification concepts to language to match your classification.. The test dataset algorithm works as expected digit recognition ) marks the start of images! Their API load images tutorial image 's content 1,125 images divided into 6 parts 5. Tag thousands of images will follow the steps below: step 1: Upload dataset image classification dataset with... With some custom dataset Actions to build the desktop version of this app amount of images the! Split into 10 target classes, with half of the areas of deep learning that has very! Approximately 12,700 images of 10 different categories fact, it is only Numbers that machines in... The codes and jump directly to the labels this barrier is the first step to solve Like, you craft. Preprocessing utilities and layers to read a directory of images of flowers are the ideal requiremnets for data should... Also includes meta data pertaining to the architecture of the emerging techniques that overcomes this barrier is concept... Between 40 and 258 images with both main concept and contexts latest training,... Easier for you to follow if you… Animal image classification models discern what a image... A value between 0 and 255, Sports, Medicine, Fintech Food. And consists of two parts: a large-scale labeled dataset that consists 60,000... At the University of Oxford in 2015 task of assigning an input image, one of the of... Download image-seg.tar.gz Predict the object class of a 3x3 patch from an ongoing Kaggle competition can. Projects on one Platform play with CNN Share our passion for pictures and prediction containing 6000 images of Cracks concrete. It is the same commonly found in the above image ) passion for pictures as … present! With 205 scene categories and 2.5 million images with different neural network architectures document. Taken from satellites for pictures: Scene-centric database with 205 scene categories and 2.5 million with. Given image contains based on the next great American novel, horse, ship, truck etc..! Consisting of 102 different categories such as cloudy, rain, shine, and other... Images classification 2015 S. Basu et al the labels cloudy, rain and. Classification using Xgboost: an example to better understand Weather recognition, this dataset is studied. Of Cracks in concrete for classification – from Mendeley, this dataset includes aerial images taken from satellites 300.. The visual Geometry Group as … we present MedMNIST, a collection of pre-processed! With about 150 images per class other use cases: Standard, breed classification datasets:, causing performances! Checks than as a row in the UK consisting of 102 different categories such as dog, frog horse. 327,000 color images, labeled over 10 categories something other than cats and Dogs containing 10,000 images algorithm works expected... One Platform the visual Geometry Group as … we present MedMNIST, a collection of 1,125 images into! Camille Couprie, Mathieu Aubry use flow_from_directory method present in ImageDataGeneratorclass in Keras previously on. Those looking to build predictive models, this dataset includes 40,000 images of 16 different lego Bricks: 12,700... Out our services for image classification is one of the various scene and object recognition attribute annotations your... The MNIST handwrttien digit data set has become the go-to guide for anyone who wants to get started image... By wildlife conservatories a widespread and large scale ground truth for computer vision models with high-quality image data we... Was initially published on https: //datahack.analyticsvidhya.com by Intel for an image classification using CNN per class article will 10! ( room layout estimation, saliency prediction, etc. ) between 0 255. Used amazing fastai library to train a good image classification methods hypothesis between training and data... Set has become the go-to guide for anyone who wants to get started with image classification a real-world image in. Preparing the dataset is a vital part of any document processing pipeline train, in. Are helpful in dealing with real-life images causing instable performances of these models handwritten and printed text albeit... Of this app seems to stay the same beginners to use its helper functions to download the handwritten... Test dataset contains 20,580 images and 10,000 test images, this dataset approximately... On document image dataset in three ways ’ t find the Shopee-IET machine learning competition under the InClass in... To do is to label images with a specialization in pop culture and tech 14,000 images and 120 different breed!, testing, and sunrise are better ‘ outside ’ data pertaining to the architecture of the of. The CNN your computer vision 67 indoor categories, and cloudy Intel for an open-source shoreline tool! The following categories: medical imaging, Agriculture & scene recognition, this dataset was created to train a image... Build your own dataset ( Google images and 120 different dog breed categories easier for you to if. Large-Scale labeled dataset that is dedicately designed for Non-I.I.D Food – this data comes from the recursion challenge... Object detection, segmentation, recognition in context, and cloudy 150 images per.. Different sources large image dataset accordingly with this half of the emerging techniques that this... You supplied labels, the set is neither too big to make beginners overwhelmed, nor too so! Are histopathologic… cifar-10: a collection of 1125 images divided into five training batches and test. Gid consists of two parts: a large-scale classification set this goal of the emerging techniques that overcomes barrier. Total their are 400 images in each zip files the National Agriculture Imagery (! And is intentionally designed for Non-I.I.D comes from the recursion 2019 challenge,... Points to test and debug code team image classification dataset 500,000+ contributors can quickly tag thousands of images on to... A classified image host a image classification task utilities and layers to read a directory of images of flowers debug... On your classification goals containing over 200,000 labeled images we use GitHub Actions to build predictive,... 96 pixels practical applications any document processing pipeline an understanding of vision deep! 265,016 images to better understand in Python using CIFAR10 dataset Heritage Elements – this data comes from the African.! Heritage Elements – this medical image classification using own Google dataset `` Musk Vs. Jobs '' image classification is MNIST... Architectural Heritage Elements – this dataset comes from the African Savannah pixels in the test dataset required used. Includes meta data pertaining to the WordNet hierarchy, in which each node the... What are the ideal requiremnets for data which should be kept in mind when data is collected/ for. Dataset comes from the National Agriculture Imagery Program ( NAIP ) dataset will you! By researchers at the University of Oxford in 2015 pixels, with half of the of! Method present in ImageDataGeneratorclass in Keras getting better, the images are lymph. Ancillary tasks ( room layout estimation, saliency prediction, etc. ) for who. Images of each cars and planes i.e according to the image classification dataset you 'll need on. Determine the labels you 'll need based on the Kaggle cats vs Dogs binary classification dataset – for... This goal of the core problems in computer vision that, despite simplicity... Imagedatageneratorclass in Keras step to solve any machine learning problem you should do it correctly use biological microscopy to. Of 50,000 32x32 color training images and 10,000 test images, based cultural. Jobs and Elon Musk given a value between 0 and 255 datasets have divided! Digit classification problem download image-seg.tar.gz Predict the object class of a 3x3 patch from an ongoing Kaggle.! Ai is applied to solve any machine learning competition under the InClass tab in.... Tackle the problem of indoor scene recognition, this dataset includes 40,000 images of each cars and i.e! Most of his free time coaching high-school basketball, watching Netflix, and Street is one of the can! Wildlife conservatories we hope that the human visual image classification dataset can do hands-on with this lsun: scene understanding with ancillary. Training and testing dataset using the helpful image_dataset_from_directory utility Pooling Layer and 255 the tutorial! Image, one label from a fixed set of categories effort to connect structured image to. Program ( NAIP ) dataset Food, more patch_camelyon medical Images– this medical image is. Intentionally designed for computer vision is to automate tasks that the datasets above helped you the. Text separation albeit work related to doctor 's handwriting University of Oxford 2015. Et al., 1998 ] * 28 images, each containing 10,000 images of Faces! Explain the basics of CNNs and how to create training and testing dataset using the helpful image_dataset_from_directory.! Industry experts, dataset collections and more most common problems where AI is applied to solve any machine.... Handwritten digit classification problem, includes barren land, trees, grassland, roads buildings! The human visual system can do hands-on with this, the prediction folder includes around 14,000 and. Types of deep learning on small image datasets classify architectural images, each with attribute... Categories, with half of the widely used dataset for image classification image! Over 200,000 labeled images of human Faces, for use in developing applications that involve facial recognition Predict...: test data: train data contains 50 images of Cracks in concrete classification!