{"id":71775,"date":"2023-01-31T13:06:04","date_gmt":"2023-01-31T12:06:04","guid":{"rendered":"https:\/\/www.clickworker.com\/?page_id=71775"},"modified":"2023-03-02T16:26:41","modified_gmt":"2023-03-02T15:26:41","slug":"image-classification-dataset","status":"publish","type":"page","link":"https:\/\/www.clickworker.com\/ai-glossary\/image-classification-dataset\/","title":{"rendered":"Image Classification Dataset"},"content":{"rendered":"<style>\r\n.faq-snippet.collapsable div h2, .faq-snippet {color: #1d1d1f;} .faq-snippet p { color: #212529;}\r\n<\/style>\r\n\r\n<section class=\"white section-space top_grey_separation_sm\">\r\n<div class=\"container pb-5\">\r\n<div class=\"row align-items-center justify-content-center justify-content-lg-between\">\r\n<div class=\"col-lg-6 pb-2\">\r\n\r\n<span class=\"gl_letter_offcanvas gl_single\">I<\/span>\r\n<h1 class=\"section-headline pt-4 mt-1 mb-4\"><span class=\"gl_single_headline\">Image Classification Dataset <\/span><\/h1>\r\n<ul>\r\n<li><a href=\"#what\">Image Classification Dataset<\/a><\/li>\t\r\n\t\t\t\t\t<li><a href=\"#forms\">What is an image classification dataset?<\/a><\/li>\t\t\t\t\t\r\n\t\t\t\t\t<li><a href=\"#forms\">What forms an image classification dataset?<\/a><\/li>\t\t\t\t\t\r\n\t\t\t\t\t<li><a href=\"#labels\">What are image classification labels?<\/a><\/li>\t\r\n\t\t\t\t\t<li><a href=\"#features\">What are image classification features?<\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#quality\">What is image classification quality?<\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#howmuch\">How much of image classification datasets would you require?<\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#use\">How to use image classification datasets?<\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#challenges\">Challenges when training with image classification datasets<\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#practices\">Best practices when dealing with image classification datasets<\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#popular\">Popular image classification datasets<\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#conclusion \">Conclusion <\/a><\/li>\r\n\t\t\t\t\t<li><a href=\"#faq\">FAQs<\/a><\/li>\r\n\t\t\t\t\t<\/ul>\t\r\n<\/div>\r\n<div class=\"col-auto\">\r\n\t<img decoding=\"async\" src=\"https:\/\/www.clickworker.com\/wp-content\/uploads\/2023\/01\/image-classification-2.png\"alt=\"image classification\" class=\"gl_image max-width-100\"><\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n\r\n\r\n<section class=\"gray section-space\">\r\n<div class=\"container\">\r\n<div class=\"row\">\r\n<div class=\"col-lg-12\">\r\n<h2 class=\"section-headline mb-4\" id=\"what\">Image Classification Dataset<\/h2>\r\n<p>Image classification is an AI or <a href=\"https:\/\/www.clickworker.com\/ai-glossary\/machine-learning\/\">machine learning-based<\/a> task that helps identify distinct objects from an image input. It is a complex operation that requires several components, technology, and algorithmic design that work together to let a machine recognize objects within an image. To do so, machine learning models are constructed and trained with huge amounts of labeled image data called image classification datasets.<br>\r\nImage classification datasets are an integral part of image classification AI systems as they act as the reference point against which the input images are analyzed to get the final output. These datasets present images with proper labels and structured information to train an image classification model. Thus, they are essential for developing <a href=\"https:\/\/www.clickworker.com\/ai-glossary\/computer-vision\/\">computer vision<\/a> systems and other image classification-based applications such as medical diagnostic systems and more.<\/p>\r\n<p><strong>Video on Image Annotation Types<\/strong><\/p>\r\n<div class=\"row\">\r\n<div class=\"col-12 col-lg-8 mx-auto\">\r\n<div class=\"lyte-wrapper\" title=\"5 Main Types of Image Annotation\" style=\"width:420px;max-width:100%;margin:5px;\"><div class=\"lyMe\" id=\"WYL_YJ5HpEILiDE\" itemprop=\"video\" itemscope itemtype=\"https:\/\/schema.org\/VideoObject\"><div><meta itemprop=\"thumbnailUrl\" content=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FYJ5HpEILiDE%2Fhqdefault.jpg\" \/><meta itemprop=\"embedURL\" content=\"https:\/\/www.youtube.com\/embed\/YJ5HpEILiDE\" \/><meta itemprop=\"duration\" content=\"PT5M1S\" \/><meta itemprop=\"uploadDate\" content=\"2020-08-26T02:44:53Z\" \/><\/div><div id=\"lyte_YJ5HpEILiDE\" data-src=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FYJ5HpEILiDE%2Fhqdefault.jpg\" class=\"pL\"><div class=\"tC\"><div class=\"tT\" itemprop=\"name\">5 Main Types of Image Annotation<\/div><\/div><div class=\"play\"><\/div><div class=\"ctrl\"><div class=\"Lctrl\"><\/div><div class=\"Rctrl\"><\/div><\/div><\/div><noscript><a href=\"https:\/\/youtu.be\/YJ5HpEILiDE\" rel=\"nofollow\"><img decoding=\"async\" src=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FYJ5HpEILiDE%2F0.jpg\" alt=\"5 Main Types of Image Annotation\" width=\"420\" height=\"216\" \/><br \/>Watch this video on YouTube<\/a><\/noscript><meta itemprop=\"description\" content=\"Image annotation is an integral part of the machine learning process for numerous computer vision models. This video will introduce five of the most common types of image annotation: bounding boxes, cuboids, polygons, lines, and semantic segmentation. Based on the article: https:\/\/lionbridge.ai\/articles\/an-introduction-to-5-types-of-image-annotation\/ If you liked the video, please don&#039;t forget to like and subscribe. It helps the channel out a ton!\"><\/div><\/div><div class=\"lL\" style=\"max-width:100%;width:420px;margin:5px;\"><\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n<section class=\"white section-space bottom_grey_separation_sm\">\r\n<div class=\"container\">\r\n<div class=\"row\">\r\n<div class=\"col-lg-12\">\r\n<h2 class=\"section-headline mb-4\" id=\"forms\">What forms an image classification dataset? <\/h2>\r\n<p>An image classification dataset can be considered to be the fuel that runs an image classification system. A model is trained with an image classification dataset; thus, having a high-quality training dataset is imperative to get accurate and speedy results. Using a good-quality dataset also ensures optimal resource utilization. But unreliable data can drastically affect the efficacy of the image classification model.<br>\r\nImage classification applications are used in many applications, from traffic control, disaster recovery, drone operation, medical diagnoses, and more. Hence the image classification datasets too are varied and can be collected from multiple varied domains and industries:<\/p>\r\n<ul><li>Art<\/li>\r\n<li>Agriculture <\/li>\r\n<li>Automobile and Advanced Driver Assistance Systems<\/li> \r\n<li>Fashion<\/li> \r\n<li>Food and Groceries <\/li>\r\n<li>Wildlife <\/li>\r\n<li>Sports<\/li> \r\n<li>Satellite Imaging <\/li>\r\n<li>Medical Imaging and Healthcare <\/li>\r\n<li>Security and Surveillance <\/li>\r\n<li>Scene type understanding<\/li><\/ul> \r\n<p>And many more <\/p>\r\n\r\n<blockquote class=rounded><p class=\"pb-3\"><strong>Tip:<\/strong><\/p>\r\n<p class=\"pb-3\">Train your Image Classification algorithms efficiently by using high quality data that can be provided by clickworker&#8217;s<\/p><a href=\"https:\/\/www.clickworker.com\/photo-datasets-image-datasets-for-machine-learning\/\" class=\"btn btn-primary\" role=\"button\">Image Datasets and Photo Datasets<\/a><\/blockquote>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"labels\">What are image classification labels?<\/h2>\r\n<p>Image classification datasets heavily rely on the concept of labels. Determining the right labels that fit your purpose is the first step to preparing your image classification datasets. The labels are picked depending on the classification goals.<br>\r\nFor instance, if you want to identify balls and bats from your input images, your labels should also be centered around these objects. Three key aspects need to be  considered when choosing the labels for your image classification dataset, namely:<\/p>\r\n<ul><li>The <a href=\"https:\/\/www.talon.one\/glossary\/granularity\" target=\"_blank\" rel=\"noopener\">granularity<\/a> of the label &#8211; The level of detail you seek<\/li> \r\n<li>The number of labels &#8211; The higher the number of labels, the more complex the model could be<\/li> \r\n<li>The parts of the image that correspond to the chosen labels.<\/li><\/ul>\r\n<p>Here are some ways to ensure gathering a good image classification dataset.\r\n<ul><li>Determine the granularity of the label early on. This decision depends on how detailed you want your results to be. For instance, you could design a model to detect cars from a picture. A more advanced model can also be designed to detect cars along with the model or brand type of the car. The advanced model would require a higher level of granularity, of course.<\/li>  \r\n<li>Keeping the same example as identifying cars from a picture, the number of labels would help you determine the number of brands you want to identify. The subclassifications and the number of other things you might want to classify would all depend on the number of labels you want to include in your dataset.<\/li> \r\n<li>The third thing to consider is the parts of the image you want to be recognized. Considering the same example as that of a car, would you like to identify only if the entire car is present in the picture or if just a part of it, say, the rear of the car alone, is visible?<\/li><\/ul>\r\n<p>When you finalize these considerations, you should be able to define your labels to fit the purpose of your mode exactly.<br>\r\nSo as you can see, an image classification dataset will have to include images that can be labeled to the extent of granularity, detail, and parts you want to classify. Your datasets should be able to support these labeling considerations and have huge amounts of relevant data.<br>\r\nA rich dataset helps your model perform better. It should also ensure consistent data points across the various classes. It should be devoid of noise and corrupted data. Outliers should be handled properly, and all given images should have good quality and be easy to label.<\/p>\r\n\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"features\">What are image classification features?<\/h2>\r\n<p>Besides labels, you should also consider the features you intend to extract from your images. The following are some of the common features your image dataset should offer you:<\/p>\r\n<ul><li>Color features like <a href=\"https:\/\/en.wikipedia.org\/wiki\/Color_histogram\" target=\"_blank\" rel=\"noopener\">color histograms<\/a><\/li>\r\n<li><a href=\"https:\/\/towardsdatascience.com\/hog-histogram-of-oriented-gradients-67ecd887675f\" target=\"_blank\" rel=\"noopener\">Histogram of oriented gradients<\/a><\/li>\r\n<li>Texture features like Tamura\u2019s or haralick\u2019s<\/li>\r\n<li><a href=\"https:\/\/www.educative.io\/answers\/what-is-sift\" target=\"_blank\" rel=\"noopener\">SIFT Features<\/a><\/li>\r\n<li><a href=\"https:\/\/medium.com\/data-breach\/introduction-to-surf-speeded-up-robust-features-c7396d6e7c4e\" target=\"_blank\" rel=\"noopener\">SURF features<\/a><\/li><\/ul>\r\n<p>There are many more image descriptors that can be used as features to classify an image. Depending on the problem you are trying to solve, your dataset should be able to support a smooth and optimal feature extraction for these features.<\/p>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"quality\">What is image classification quality?<\/h2>\r\n<p>As already mentioned, good-quality images help improve the model\u2019s performance and can help you reach accurate results faster. Here are some of the quality parameters to consider when gathering your image classification datasets:<\/p>\r\n<ul><li>Image contrast<\/li>\r\n<li>Contrast sensitivity<\/li>\r\n<li>Blur and detail visibility<\/li>\r\n<li>Noise<\/li>\r\n<li>Artifacts<\/li>\r\n<li>Distortion<\/li>\r\n<li>Compromises and so on<\/li><\/ul>\r\n<p>The quality of any image is usually determined depending on its imaging method, the equipment used, and the various imaging variables such as contrast, blur, noise, distortion, and so on. Based on the problem, the image classification dataset should provide the adequate quality required for the models to work optimally.<\/p>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"howmuch\">How much of image classification datasets would you require?<\/h2>\r\n<p>The amount of training data you need to train an image classification model largely depends on the classification goals of your model. The more items you want to detect and recognize, the more volumes of data you should be using. Here are some minimum requirements when deciding on your image classification dataset size.<br> \r\nAs a rule of thumb, it is best to have at least 100 images per particular class of item you want to detect. For example, if you want to detect sunflowers from a picture, you should have at least 100 images of sunflowers in your training data. The more flowers or labels you want to detect, the more images you need.<br> \r\nIf you want greater detail, that is, high granularity, the number of images used should also be higher. As a recommendation, it is considered best to use at least 100 images for each sub-label.<br> \r\nThe same applies to the number of parts you want to identify. You will need at least a minimum of 100 images per item that you want to identify.<br> \r\nWhile the arbitrary count of 100 images per label may sound like a good benchmark, one cannot be assured of the accuracy with just this figure. Depending on the complexity of the image classification problem, you might need more images, or sometimes for similar shape identification, a lower number of images could also suffice.<\/p>\r\n\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"use\">How to use image classification datasets?<\/h2>\r\n<p>Depending on the machine learning model you use, you would be using the image datasets to train and validate the model.<br>\r\nCreate your image classification datasets and specify the associated attributes and parameters.<br>\r\nInput this dataset in some form via file storage or upload it to the machine learning model system for training purposes. While uploading, you should specify a certain percentage of the dataset to be used for validation and the rest for testing. For this, you could make use of a split algorithm. Specify where the results of the split algorithm should be stored and the workflows on how the model should consume these datasets.<br>\r\nThe steps can be summarized as follows:<\/p>\r\n<ul><li>Collecting the image dataset<\/li>\r\n<li>Split the images into training and validation groups and test groups. The percentage of this split can be anything from 60%-40% to 80%-20%, depending on the model.<\/li>\r\n<li>Pre-process the images by labeling them<\/li>\r\n<li>Use the image datasets to train the model, build the model and fine-tune it<\/li>\r\n<li>Evaluate the model by testing it against the test dataset<\/li><\/ul>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"challenges\">Challenges when training with image classification datasets<\/h2>\r\n<p>Getting high-quality data with consistent view angles and sizes can be a challenge. But to ensure accurate results, you will need sample data of the same item in differing angles, lighting, and other quality concerns. Here are some common challenges you might encounter when trying to finalize your dataset:<\/p>\r\n<ul><li>Differing orientations<br> \r\nA single object might look a lot different when viewed from different viewpoints with respect to the camera. This could increase the complexity of the training involved.<\/li>\r\n<li>Scales and sizes<br>\r\nImages can sometimes cause optical illusions where the size and scalings of the item could differ a lot. For instance, a car pictured near a tanker lorry could appear much smaller than it really is.<\/li>\r\n<li>Visibility<br>\r\nLow visibility and low light conditions can hamper the data quality down to the pixel level, and sometimes the entire object may not be visible in the image.<\/li><\/ul>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"practices\">Best practices when dealing with image classification datasets<\/h2> \r\n<p>You must follow best practices to get high-quality images and ensure your dataset is diversified enough to support accurate results. Here are some practices that can help:<\/p>\r\n<ul><li>Ensure your image dataset contains images taken from varying angles and perspectives.<\/li>\r\n<li>Collect images in different lighting conditions. <\/li> \r\n<li>Try to include as many images with varying sizes and scalings relevant to other objects, so you can include greater variance in your dataset.<\/li>  \r\n<li>Make sure your images have high visibility. <\/li> \r\n<li>In case of limited visibility, try using low-visibility data points when training your dataset.<\/li>  \r\n<li>Try to limit the data size of your images, as very big images can impact your model\u2019s performance. Most AI models work with images of sizes up to 224 x 224 pixels. So making use of huge file sizes will bear no extra benefit and could only slow down processing.<\/li>  \r\n<li>It is also a good practice to use high-definition pictures to get better results.<\/li><\/ul>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"popular\">Popular image classification datasets<\/h2>\r\n<p>While you can use your own custom datasets for your image classification algorithms, using existing datasets is often considered a best practice. This is because these datasets usually provide you with well-prepared high-quality images and come with easy licensing options to use in your models. You should also remember that sometimes images on the internet could carry copyright implications, and collecting huge amounts of relevant image classification datasets can be quite challenging.<br>\r\nHere are some popular image classification datasets that you could make use of:<\/p>\r\n<ul><li>Image Classification: People and Food<\/li>\r\n<li>Images of Crack in Concrete for Classification<\/li>\r\n<li>Architectural Heritage Elements<\/li><\/ul>\r\n\r\n<p>Uses in the field of medicine:<\/p>\r\n<ul><li><a href=\"https:\/\/www.tensorflow.org\/datasets\/catalog\/patch_camelyon\" target=\"_blank\" rel=\"noopener\">TensorFlow patch_camelyon<\/a> Medical Images<\/li>\r\n<li>Recursion Cellular Image Classification<\/li>\r\n<li>Blood Cell Images<\/li>\r\n<li>ChestX-ray8<\/li><\/ul>\r\n\r\n<p>Agriculture-based image classification datasets:<\/p>\r\n<ul><li>Indoor Scenes Images<\/li>\r\n<li>Images for Weather Recognition  <\/li>\r\n<li>Intel Image Classification<\/li> \r\n<li>TensorFlow Sun397 Image Classification Dataset <\/li> \r\n<li>Coastset Image Classification Dataset  <\/li><\/ul>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"conclusion\">Conclusion<\/h2> \r\n<p>Image classification is a growing and commonly used machine learning-based task that finds applications in various industries. Every computer vision system uses them, from surveillance applications to medical diagnosis systems. And such advanced AI-based image classification systems would not be possible without the huge volumes of training data used to train, model, and evaluate these systems. Thus, image classification datasets play an integral part in the ongoing development of AI technologies, and researchers worldwide need many good-quality image classification datasets. Understanding their utilization, fair usage, and quality data collection is necessary to create better machine learning models and AI systems.<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n\r\n<section class=\"gray section-space\">\r\n\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t<div class=\"container\">\t\t\t\r\n\t\t<h2 id=\"faq\">FAQs on Image Classification Dataset<\/h2>\t\r\n\t\t<!--<div class=\"h2 d-block faq-snippet-heading cw-dark-gray\" data-toggle=\"collapse\" data-target=\"#faqCrowdsourcingSnippet\" role=\"button\" aria-expanded=\"false\" aria-controls=\"faqCrowdsourcingSnippet\">\r\n\t\t\t\tCrowdsourcing - Goolge FAQ Snippets\r\n\t\t\t<\/div>-->\r\n\t\t<div id=\"faqDataAnnotationSnippet\">\r\n\t\t\t\t<div class=\"row\">\r\n\t\t\t\t\t\t<div class=\"col-lg-12\">\r\n\t\t\t\t\t\t\t\t\t<section class=\"sc_fs_faq sc_card card faq-snippet collapsable\">\n\t\t\t<div>\n\t\t\t\t<h2>What is image classification?<\/h2>\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t<p>\n\t\t\t\t\t\tImage classification is an AI or machine learning-based task that helps identify distinct objects from an image input.\t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"sc_fs_faq sc_card card faq-snippet collapsable\">\n\t\t\t<div>\n\t\t\t\t<h2>What are the benefits of using existing datasets for image classification?<\/h2>\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t<p>\n\t\t\t\t\t\tThese datasets usually provide you with well-prepared high-quality images and come with easy licensing options to use in your models. \t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"sc_fs_faq sc_card card faq-snippet collapsable\">\n\t\t\t<div>\n\t\t\t\t<h2>What is the limitation of using existing datasets for image classification?<\/h2>\t\t\t\t<div>\n\t\t\t\t\t\t\t\t\t\t<p>\n\t\t\t\t\t\t Images on the internet could carry copyright implications, and collecting huge amounts of relevant image classification datasets can be quite challenging\t\t\t\t\t<\/p>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\n<script type=\"application\/ld+json\">\n\t{\n\t\t\"@context\": \"https:\/\/schema.org\",\n\t\t\"@type\": \"FAQPage\",\n\t\t\"mainEntity\": [\n\t\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"What is image classification?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"Image classification is an AI or machine learning-based task that helps identify distinct objects from an image input.\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t,\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"What are the benefits of using existing datasets for image classification?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"These datasets usually provide you with well-prepared high-quality images and come with easy licensing options to use in your models.\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t,\t\t\t\t{\n\t\t\t\t\"@type\": \"Question\",\n\t\t\t\t\"name\": \"What is the limitation of using existing datasets for image classification?\",\n\t\t\t\t\"acceptedAnswer\": {\n\t\t\t\t\t\"@type\": \"Answer\",\n\t\t\t\t\t\"text\": \"Images on the internet could carry copyright implications, and collecting huge amounts of relevant image classification datasets can be quite challenging\"\n\t\t\t\t\t\t\t\t\t}\n\t\t\t}\n\t\t\t\t\t\t]\n\t}\n<\/script>\n\r\n\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t<\/div>\r\n\t\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t\t<div class=\"mini-section-spacer\"><\/div>\r\n\t\t<\/div>\r\n\r\n\t<\/section>\r\n\r\n ","protected":false},"excerpt":{"rendered":"<p>I Image Classification Dataset Image Classification Dataset What is an image classification dataset? What forms an image classification dataset? What are image classification labels? What are image classification features? What is image classification quality? How much of image classification datasets would you require? How to use image classification datasets? Challenges when training with image classification [&hellip;]<\/p>\n","protected":false},"author":65,"featured_media":0,"parent":54483,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"blank_with_breadcrumb.php","meta":{"footnotes":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Image Classification Dataset - AI glossary \/ term explanation<\/title>\n<meta name=\"description\" content=\"An image classification dataset can be considered to be the fuel that runs an image classification system.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.clickworker.com\/ai-glossary\/image-classification-dataset\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Image Classification Dataset - 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