There are many types of video datasets available that can be used to train machine learning algorithms. YouTube-8M segments, CIFAR100, CODAHCODAH or TACO are just a few examples. Below is a listing of each. You can find out more at our website. Tell us what you think! Comment below to let us know your thoughts! Also, don't miss our list of the most popular video datasets.
CIFAR-100
Images in the CIFAR-100 video datasets are categorized according to WordNet. These images contain hyperlinks which describe each pixel. These datasets were created in order to fulfill two basic requirements of computer vision as well as to support other machine learning techniques. In addition to CIFAR-100, the BDD-100K is a driving dataset for independent multitask learning, which consists of ten tasks and 100K videos. This dataset is being used for estimating progress in developing image recognition algorithms to autonomous vehicles.
YouTube-8M Segments
YouTube-8M is large labeled and contains millions of YouTube videos IDs. This dataset can be used for your machine learning project. These videos have been labeled in high-quality, machine-generated annotations. Each segment of the data point is 5 seconds long. It is very easy to use the dataset: All you have to do to create a CloudFormation template and AWS Glue Catalog entries is a matter of minutes.
CODAHCODAH
Machine learning applications that require the analysis of video content need specific types of data to train their models. The majority of public video datasets don't meet these requirements, either because there is not enough diversity or low amounts, or because it is difficult to train algorithms. Here are some tips for selecting the best datasets for machine learning applications. Identify where your datasets came from. YouTube videos include many different content, including news and sports.
TACO
This paper proposes a new machine learning approach to recognize natural sentences using TACO video datasets. This framework makes use of contextual evidence to locate video segments corresponding to a given natural language sentence. This method is superior to state-of the-art. It can be used for machine learning and speech recognition. This article will describe the main features of this algorithm and demonstrate its effectiveness with the TACO data datasets.
CMU-MOSEI
Multimodal Corpus of Sentiment Intensity, or CMU-MOSI, is a large dataset of 2199 opinion videos that have been annotated and annotated using subjectivity. It also includes various audio and visual features. This dataset has a lot of statistical information and is great for machine learning studies. Each frame is annotated. The largest data set of its kind, it contains an extensive range of emotion labels.
Facebook BISON
Facebook's BISON Video dataset focuses more on fine-grained visual grounding than the COCO Captions dataset. This dataset supplements the COCO Captions dataset and measures the ability to connect the linguistic and visual content. BISON is used for evaluations of caption-based retrieval system and captioning systems. It demonstrates that visual grounding systems can outperform people.
FAQ
What is AI and why is it important?
It is estimated that within 30 years, we will have trillions of devices connected to the internet. These devices will cover everything from fridges to cars. Internet of Things (IoT), which is the result of the interaction of billions of devices and internet, is what it all looks like. IoT devices will be able to communicate and share information with each other. They will also be capable of making their own decisions. A fridge may decide to order more milk depending on past consumption patterns.
According to some estimates, there will be 50 million IoT devices by 2025. This represents a huge opportunity for businesses. However, it also raises many concerns about security and privacy.
What is the most recent AI invention
Deep Learning is the most recent AI invention. Deep learning, a form of artificial intelligence, uses neural networks (a type machine learning) for tasks like image recognition, speech recognition and language translation. Google created it in 2012.
Google was the latest to use deep learning to create a computer program that can write its own codes. This was done using a neural network called "Google Brain," which was trained on a massive amount of data from YouTube videos.
This enabled it to learn how programs could be written for itself.
IBM announced in 2015 they had created a computer program that could create music. Another method of creating music is using neural networks. These are known as NNFM, or "neural music networks".
Which countries are currently leading the AI market, and why?
China is the world's largest Artificial Intelligence market, with over $2 billion in revenue in 2018. China's AI market is led by Baidu. Tencent Holdings Ltd. Tencent Holdings Ltd. Huawei Technologies Co. Ltd. Xiaomi Technology Inc.
The Chinese government has invested heavily in AI development. The Chinese government has set up several research centers dedicated to improving AI capabilities. These centers include the National Laboratory of Pattern Recognition and the State Key Lab of Virtual Reality Technology and Systems.
Some of the largest companies in China include Baidu, Tencent and Tencent. These companies are all actively developing their own AI solutions.
India is another country making progress in the field of AI and related technologies. India's government is currently working to develop an AI ecosystem.
Is Alexa an Artificial Intelligence?
Yes. But not quite yet.
Amazon created Alexa, a cloud based voice service. It allows users use their voice to interact directly with devices.
The Echo smart speaker first introduced Alexa's technology. Since then, many companies have created their own versions using similar technologies.
These include Google Home and Microsoft's Cortana.
Statistics
- In 2019, AI adoption among large companies increased by 47% compared to 2018, according to the latest Artificial IntelligenceIndex report. (marsner.com)
- That's as many of us that have been in that AI space would say, it's about 70 or 80 percent of the work. (finra.org)
- In the first half of 2017, the company discovered and banned 300,000 terrorist-linked accounts, 95 percent of which were found by non-human, artificially intelligent machines. (builtin.com)
- More than 70 percent of users claim they book trips on their phones, review travel tips, and research local landmarks and restaurants. (builtin.com)
- Additionally, keeping in mind the current crisis, the AI is designed in a manner where it reduces the carbon footprint by 20-40%. (analyticsinsight.net)
External Links
How To
How do I start using AI?
You can use artificial intelligence by creating algorithms that learn from past mistakes. You can then use this learning to improve on future decisions.
If you want to add a feature where it suggests words that will complete a sentence, this could be done, for instance, when you write a text message. It would analyze your past messages to suggest similar phrases that you could choose from.
To make sure that the system understands what you want it to write, you will need to first train it.
Chatbots can also be created for answering your questions. So, for example, you might want to know "What time is my flight?" The bot will answer, "The next one leaves at 8:30 am."
If you want to know how to get started with machine learning, take a look at our guide.