An AI algorithm is only as good as the data you feed it.

It is neither a bold nor an unconventional statement. AI could have seemed rather far-fetched a couple of decades ago, but Artificial Intelligence and Machine Learning have come a really long way since then.

Computer vision helps computers understand and interpret labels and images. When you train your computer using the right kind of images datasets, it can gain the ability to detect, understand and identify various facial features, detect diseases, drive autonomous vehicles, and also save lives using multi-dimensional organ scanning.

The Computer Vision Market is predicted to reach $144.46 Billion by 2028 from a modest $7.04 Billion in 2020, growing at a CAGR of 45.64% between 2021 and 2028.

The image dataset you are feeding and training your Machine Learning and computer vision tasks are crucial to your AI project’s success. A quality dataset is quite hard to get. Depending on the complexity of your project, it could take anywhere between a few days to a few weeks to get reliable and relevant datasets for computer vision purposes.

Here, we provide you with a range (categorized for your ease) of open-source image datasets you can use right away.

Comprehensive List of Image Datasets to Train Your Computer Vision Model

General:

1. ImageNet

   ImageNet is a widely used dataset, and it comes with an astonishing 1.2 million images categorized into 1000 categories. This dataset is organized as per the WorldNet hierarchy and categorized into three parts – the training data, image labels, and validation data.

2. Kinetics 700

   Kinetics 700 is a huge high-quality dataset with more than 650,000 clips of 700 different human action classes. Each of the class actions has about 700 video clips. The clips in the dataset have human-object and human-human interactions, which are proving to be quite helpful when recognizing human actions in videos.

3. CIFAR-10

   CIFAR 10 is one of the largest computer-vision datasets boasting 60000 32 x 32 color images representing ten different classes. Each class has about 6000 images used to train computer vision algorithms and machine learning.

4. Oxford-IIIT Pet Images Dataset

   The pet image dataset comprises 37 categories with 200 images per class. These images vary in scale, pose, and lighting, and are accompanied by annotations for breed, head ROI, and pixel-level trimap segmentation.

5. Google’s Open Images

   With an impressive 9 million URLs, this is one of the largest image datasets on the list, containing millions of images labeled across 6,000 categories.

6. Plant Images

   This compilation includes multiple image datasets featuring an impressive 1 million plant images, covering approximately 11 species.

Facial Recognition:

1. Labeled Faces in the Wild

   Labeled Faced in the Wild is a huge dataset containing more than 13,230 images of nearly 5,750 people detected from the internet. This dataset of faces is designed to make it easier to study unconstrained face detection.

2. CASIA WebFace

   CASIA Web face is a well-designed dataset that helps machine learning and scientific research on unconstrained facial recognition. With more than 494,000 images of almost 10,000 real identities, it is ideal for face identification and verification tasks.

3. UMD Faces Dataset

   UMD faces a well-annotated dataset that contains two parts – still images and video frames. The dataset has more than 367,800 face annotations and 3.7 million annotated video frames of subjects.

4. Face Mask Detection

   This dataset includes 853 images categorized into three classes: “with mask,” “without mask,” and “mask worn incorrectly,” along with their bounding boxes in PASCAL VOC format.

5. FERET

   The FERET (Facial Recognition Technology Database) is a comprehensive image dataset containing over 14,000 annotated images of human faces.

Handwriting Recognition:

1. MNIST Database

   MNIST is a database containing samples of handwritten digits from 0 to 9, and it has 60,000 and 10,000 training and testing images. Released in 1999, MNIST makes it easier to test image processing systems in Deep Learning.

2. Artificial Characters Dataset

   Artificial Characters Dataset is, as the name suggests, artificially generated data that describes the English language structure in ten capital letters. It comes with more than 6000 images.