The most important part of any machine learning (ML) project is collecting good datasets, including good labels.
The datasets help train machines to perform techniques like image classification. When image classification datasets feed data to ML tools, they train them to emulate human cognitive abilities to extract meaning from images.
Image classification datasets work like nutrition for a machine learning tool. Without good datasets, ML tools will fail to give us the results.
But where do we get these datasets?
We either create new or use existing datasets for training models. To create new datasets, we need tons of labeled image data.
Since building a new image classification dataset is time-consuming, beginners tend to use existing datasets to get their tools up and running fast.
For all those beginners in machine learning, we have compiled a list of the top 8 image classification datasets that can help you start your project without training datasets.
But, before talking about those datasets, let’s take a look at a brief explanation of what an image classification dataset is.
What is an Image Classification Dataset?
An image classification dataset is the curated set of digital photos used for training, testing, and evaluating the performance of machine learning algorithms.
Since the algorithms learn from the example images in the datasets, the images need to be high-quality, diverse, and multi-dimensional.
In the turn of these high-quality images, we get a high-quality training dataset that can perform classification accurately and expedite the decision-making process.
This is why it is crucial to pick a reliable training dataset that enhances the classification results.
Top 8 Machine Learning Image Classification Datasets
We have grouped these eight datasets into two categories: agriculture and scene datasets and medicine datasets, based on the type of images they contain.
Image Classification Datasets for Agriculture and Scene
This image classification dataset is developed for indoor scene recognition. It features more than 15,000 images of indoor locations and 67 separate indoor categories.
Though the number of images across categories varies, at least 100 images are in each category. The format of all images is JPG.
Initially compiled and designed by Intel for a classification challenge, this image classification dataset has nearly 25,000 images of natural scenes worldwide.
Each image size is 150×150, available in categories such as mountain, glacier, sea, forest, buildings, and street.
The dataset also provides a collection of images for training, testing, and prediction in separate folders. Depending on the function you are preparing your model for, you can access relevant zip files of images.
These images can help you build a powerful neural network that can accurately classify images for any computer vision task.
By providing images for any weather recognition project, this dataset helps classify images based on weather. It features a sum of 1125 images under categories such as sunny, cloudy, and rainy.
Sun397 is an extensive Scene UNderstanding (SUN) dataset featuring 108,753 images classified into 397 categories.
These categories help evaluate numerous algorithms for scene recognition. While the categories contain a different number of images, they have a minimum of 100 images in each category.
There are also some other configurations of this dataset available with 76,000+ training images, more than 10,000 validation images, and a little over 21,000 test images.
Image Classification Datasets for Medicine
PatchCamelyon is a medical image classification dataset consisting of 327,000+ color images extracted from histopathological lymph node scans.
They are 96×96 pixel images annotated to indicate the presence of metastatic tissue.
Being a new and challenging dataset, PatchCamelyon creates a unique benchmark for machine learning models.
The dataset comprises 12,500 augmented images of blood cells alongside labels for four cell types. Each cell type has approximately 3,000 images gathered in 4 different folders based on cell type.
Another dataset consists of 410 images (pre-augmentation), two blood cell subtype labels: WBC and RBC, and bounding boxes for each cell in each image.
These datasets can help prepare models for automated diagnosis of blood-based diseases by detecting and classifying blood cell subtypes.
The data in this dataset is from the results of the Recursion Cellular Image Classification competition.
This cellular image classification dataset can help make better inferences on the state of body cells to help us discover treatments for a wide range of diseases.
ChestX-ray8 is a medical imaging dataset that contains 108,948 frontal-view X-ray images collected from 1992 to 2015.
The x-ray images belong to 32,717 unique patients. Images also contain text-mined labels of eight common diseases extracted from text radiological reports.
The eight image classification datasets listed above are great for any machine learning projects in relevant fields.
While you can use some datasets to train a model that predicts the weather for crops in agriculture, other datasets can help you train AI tools to extract some unattainable insights from medical images to discover possible cures for diseases.
To make sure these datasets suit your purpose, you must do your research and explore them more before jumping into training your model.