The response depends on a number of factors like the diversity of production data, the availability of open-source datasets, the expected performance of the system, and the list can go on for quite a while.
With the rapid adoption of deep learning in computer vision, there are more and more diverse tasks needed to be solved with the help of machines. Object detection is a branch of computer vision that deals with identifying and locating objects in a photo or video. The goal of object detection is to find objects with certain characteristics in a digital image or video with the help of machine learning. Often, object detection is a preliminary step for item recognition: first, we have to identify objects and then apply recognition models to identify certain elements.
Object detection is a core task of AI-powered solutions for visual inspection, warehouse automation, inventory management, security, and more.
Below are some object detection use cases that are successfully implemented across industries. Quality assurance, inventory management, sorting and assembly line — object detection plays an important role in automation of many manufacturing processes.
Machine learning algorithms allow to quickly detect any defects, automatically count and locate objects. This allows them to improve inventory accuracy by minimizing human error and the time spent. Machine learning is used in self-driving cars, pedestrian detection and optimizing traffic flows in cities.
Object detection is used to perceive vehicles and obstacles surrounding the driver. In transportation, object recognition is used to detect and count vehicles. Applying object detection and recognition to assist with medical examinations for telehealth is a new trend set to change the way healthcare is delivered to patients. Safety and surveillance. Among the applications of object detection are video surveillance systems capable of people detection and face recognition.
Using machine learning algorithms , such systems are designed for biometric authentication and remote surveillance. This technology has even been used for suicide prevention. Logistics and warehouse automation. Object detection models are capable of visual inspection for defect detection , inventory management, quality control, and automation of supply chain management.
AI-powered logistics solutions use object detection models instead of barcode detection, thus replacing manual scanning. Developing an object detection system to be used for tasks similar to the ones we mentioned above is no different to any other ML project. It usually starts with building a hypothesis to be checked during several rounds of experiments with data.
Such a hypothesis is a part of the PoC approach in software development. It aligns with machine learning, as in this case the delivery is not an end product. Experienced ML practitioners such as Andrew Ng co-founder of Google Brain, ex Chief Scientist at Baidu recommend building the first iteration of the system with machine learning functionality quickly, then deploying it and iterating from there.
This approach allows us to create a functional and scalable prototype system that can be upgraded with the data and feedback from the production team. This solution is far more efficient compared to the situation where you would try to build the final system from the get-go.
A prototype like this does not necessarily require large amounts of data. The only way to find out out is to set a hypothesis and to test it on a real case.
Our goal was to create a system capable of detecting objects for logistics. Goods transportation from production to warehouse or from warehouse to facilities often requires intermediate control and coordination of the actual quantity with invoices and a database.
When done manually, this task would require hours of human work and involves high risks. You can browse the data sets on Data. You can browse by topic area, or search for a specific data set. The World Bank is a global development organization that offers loans and advice to developing countries. The World Bank regularly funds programs in developing countries, then gathers data to monitor the success of these programs. You can browse World Bank data sets directly, without registering.
The data sets have many missing values, and sometimes take several clicks to actually get to data. Reddit , a popular community discussion site, has a section devoted to sharing interesting data sets.
You can browse the subreddit here. You can also see the most highly upvoted data sets here. Academic Torrents is a new site that is geared around sharing the data sets from scientific papers. For now, it has tons of interesting data sets that lack context. You can browse the data sets directly on the site. Deluge is a good free option.
However, as online services generate more and more data, an increasing amount is generated in real-time, and not available in data set form. Some examples of this include data on tweets from Twitter , and stock price data. Twitter has a good streaming API, and makes it relatively straightforward to filter and stream tweets. You can get started here. There are tons of options here — you could figure out what states are the happiest, or which countries use the most complex language.
We also recently wrote an article to get you started with the Twitter API here. Github has an API that allows you to access repository activity and code. You can get started with the API here.
The options are endless — you could build a system to automatically score code quality, or figure out how code evolves over time in large projects. Quantopian is a site where you can develop, test, and operationalize stock trading algorithms. In order to help you do that, they give you access to free minute by minute stock price data. You could build a stock price prediction algorithm. You could use these calls to build up a set of historical weather data, and make predictions about the weather tomorrow.
In this post, we covered good places to find data sets for any type of data science project. We hope that you find something interesting that you want to sink your teeth into!
Please let us know! At Dataquest , our interactive guided projects are designed to help you start building a data science portfolio to demonstrate your skills to employers and get a job in data. Emergency Room Visits - 20 years of select emergency room visit data, by sex and age.
Has a lot of data that you can limit your query to with a SQL-like query builder, but can be cumbersome to build a query. Available via R package here. The national Pigeon Racing Database is here for you.
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