10 Best Image Classification Datasets for ML Projects

To help you build object recognition models, scene recognition models, and more, we’ve compiled a list of the best image classification datasets. These datasets vary in scope and magnitude and can suit a variety of use cases. Furthermore, the datasets have been divided into the following categories: , agriculture & scene recognition, and others. medical imaging Medical Image Classification Datasets 1. – This data comes from the Recursion 2019 challenge. This goal of the competition was to use biological microscopy data to develop a model that identifies replicates. The full information regarding the competition can be found . Recursion Cellular Image Classification here 2. – This medical image classification dataset comes from the TensorFlow website. It contains just over 327,000 color images, each 96 x 96 pixels. The images are histopathological lymph node scans which contain metastatic tissue. TensorFlow patch_camelyon Medical Images Agriculture and Scene Datasets 3. – Used for an open-source shoreline mapping tool, this dataset includes aerial images taken from satellites. The dataset also includes meta data pertaining to the labels. CoastSat Image Classification Dataset 4. Used for multi-class weather recognition, this dataset is a collection of 1125 images divided into four categories. The image categories are sunrise, shine, rain, and cloudy. Images for Weather Recognition – 5. – From MIT, this dataset contains over 15,000 images of indoor locations. The dataset was originally built to tackle the problem of indoor scene recognition. All images are in JPEG format and have been divided into 67 categories. The number of images per category vary. However, there are at least 100 images for each category. Indoor Scenes Images 6. – Created by Intel for an image classification contest, this expansive image dataset contains approximately 25,000 images. Furthermore, the images are divided into the following categories: buildings, forest, glacier, mountain, sea, and street. The dataset has been divided into folders for training, testing, and prediction. The training folder includes around 14,000 images and the testing folder has around 3,000 images. Finally, the prediction folder includes around 7,000 images. Intel Image Classification 7. – Another dataset from Tensorflow, this dataset contains over 108,000 images used in the Scene Understanding (SUN) benchmark. Furthermore, the images have been divided into 397 categories. The exact amount of images in each category varies. However, there are at least 100 images in each of the various scene and object categories. TensorFlow Sun397 Image Classification Dataset Other Image Classification Datasets 8. – This dataset was created to train models that could classify architectural images, based on cultural heritage. It contains over 10,000 images divided into 10 categories. The categories are: altar, apse, bell tower, column, dome (inner), dome (outer), flying buttress, gargoyle, stained glass, and vault. Architectural Heritage Elements 9. – This dataset comes in CSV format and consists of images of people eating food. Human annotators classified the images by gender and age. The CSV file includes 587 rows of data with URLs linking to each image. Image Classification: People and Food 10. – From Mendeley, this dataset includes 40,000 images of concrete. Each image is 227 x 227 pixels, with half of the images including concrete with cracks and half without. Images of Cracks in Concrete for Classification Also published on: https://lionbridge.ai/datasets/top-10-image-classification-datasets-for-machine-learning/