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Comprehensive Source Of Data For Computer Vision

Comprehensive Source Of Data For Computer Vision
A functional AI system is built upon solid reliable, stable, and dynamic data sources. Without a rich and comprehensive AI information for training available it's impossible to construct an efficient and effective AI solution. We know that the task's complexity is the main factor that will determine the quality required of the data. We aren't what amount of AI Training Dataset we will need to create the customized model.
There's no easy answer to what quantity of training data to use for machine-learning is required. Instead of using an approximate figure we believe that a myriad of techniques can provide you with an accurate estimate of the size of data you may require. Before we get into the details, let's look at the importance of training data to the successful implementation for an AI project.
An AI algorithm can only be as effective as the data it is fed.
It's neither an outrageous or unorthodox statement. AI might have been a bit out of the realm a few years ago however, Artificial Intelligence and Machine Learning have come a long way since then.
The computer vision aids computers in understanding and interpret images and labels. If the computer is trained with the correct type of images, it will be able to recognize the various facial features, diagnose illnesses, operate self-driving vehicles and save lives by using multiple-dimensional scans of the organ.
It is estimated that the Computer Vision Market is predicted to be $144.46 billion by 2028 , from just $7.04 billion in the year 2020. expanding at a CAGR 45.64 percent between 2021 to 2028.

Complete Listing of Computer Vision Datasets


  • ImageNet (Link)

ImageNet is a well-known data set, and has an impressive 1.2 million images that are classified into categories of 1000. The data is organized as in accordance with the WorldNet hierarchy and is classified into three components namely, the images, the training data and validation data.
  • Kinetics 700 (Link)
Kinetics 700 is an enormous quality dataset that includes more than 650,000 videos of 700 human actions classes. Each class action includes around 700 videos. The videos within the data set include human-human and human-object interactions that are showing to be extremely useful when recognising human-like actions in video.
  • CIFAR-10 (Link)
CIFAR 10 is among the largest computer vision datasets, with 60000 32 colors x 32 pixels representing 10 distinct classes. Each class contains around 6000 images that are used to teach computer vision


  • The Cityscape data (Link)
Cityscape is the data source to look at when searching for video sequences that were recorded from a variety of cites' street scenes. The images were recorded over a period of time, as well as in various lighting and weather conditions. The annotations pertain to 30 different classes of images that are split into eight distinct categories.
  • Barkley Deep Drive (Link)
Barkley DeepDrive is specially designed for training autonomous vehicles and has over 100000 video sequences that have been annotated. It's among the most useful training materials for autonomous vehicles, due to the constantly changing road and driving conditions.
  • Mapillary (Link)
Mapillary is home to more than the 750 million streets and traffic signs around the world it is very helpful in the training of visual perception models for Machine Learning and AI algorithms. It lets you develop autonomous vehicles that adapt to varying conditions of weather and lighting, as well as perspectives.

3.Medical Imaging:

  • Covid-19 Open Research Dataset (Link)
The original dataset contains approximately 6500 lung segmentations pixel-polygonal in chest xrays from AP/PA. In addition 517 images of Covid-19 patient xrays that have tags that include the name, address admission details, results and much more are available.
  • NIH Database of 100,000 Chest X-Rays (Link)
The NIH database is among the largest publicly accessible databases that contains 100,000 chest x-rays images as well as other data that are that are useful to researchers and scientists. There are even images of patients suffering from severe lung diseases.
  • Atlas of Digital Pathology (Link)
Atlas of Digital Pathology offers numerous histopathological images with more than 17,000 images in all, ranging from close to 100 slides with annotations of various organs. This database is beneficial in the design of computer


  • IMDB Wiki Dataset (Link)
IMDB Wiki is among the most well-known databases of faces that are properly labeled by gender, age and names. There are also around 20000 faces of famous faces and 62 thousand faces from Wikipedia.
  • Celeb Faces (Link)
Celeb Faces is a large-scale database with 200,000 annotations of famous people. The pictures are accompanied by backgrounds and poses that vary which makes them useful for testing sets of training for the field of computer vision. It's highly beneficial to improving the accuracy of facial recognition editing, facial parts localization, and much more.

How much data is enough?

It depends.
The amount of information needed depends on a number of variables Some of them are:
  1. The complex nature that is involved in the machine learning task you're working on
  2. The scope of your project as well as the budget will also affect the type of training strategy you're using.
  3. The annotation and labeling requirements for the particular project.
  4. The diversity and dynamics of the data sets needed to train an AI-based program with precision.
  5. The quality of data is a requirement for the project.

What do you do if you require more datasets

1.Open Dataset

Open datasets are generally regarded as an excellent source of data that is free. Although this may be the case however, open datasets don't provide what a project requires generally. There are a variety of sources where data can be obtained including the government, EU Open data portals, Google Public data explorers and many more. There are a number of drawbacks of making use of open datasets for large projects.
When you utilize such datasets there is a risk of conducting training and testing of your model using wrong or insufficient data. The methods for collecting data are not generally known and can affect the final outcome of the project. Privacy as well as consent as well as identity theft, are major disadvantages of accessible data sources.

2.Augmented Dataset

If you have a certain quantity of data for training but it is not enough to meet the requirements of your project, you must apply methods for data enhancement. The data is reused to satisfy the requirements that the algorithm.
The data samples undergo different transformations, making the data diverse, rich and fluid. An easy illustration of this can be observed when working with images. Images can be enhanced by a variety of methods - it is cut or resized, or mirrored and rotated into different angles and colors are able to be changed.

3.Synthetic Data

If there's not enough data, we may turn towards synthetic generators. Synthetic data is useful in the context of transfer learning because the model can be developed using synthetic data before being able to apply it to the real-world data. For instance an autonomous vehicle that is based on AI is first trained to identify and analyse things that are in computers' vision game videos.
Synthetic data can be beneficial in situations where there is a shortage of actual data to create as well as test developed models. Additionally, it can be utilized when dealing with privacy issues and data sensitivity.

4.Custom Data Collection

Custom Speech Data Collection might be an ideal method to create datasets in cases where other forms don't provide the desired results. High-quality datasets can be produced by using web scraping software sensors, cameras as well as other tools. If you require custom-designed data that improve the efficiency of your model, purchasing custom-designed datasets may be the best option. A variety of third-party providers offer their knowledge.

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