Best Use of the Clarifai API
Clarifai’s powerful image and video recognition technology is built on the most advanced machine learning systems and made easily accessible by a clean API, empowering developers all over the world to build a new generation of intelligent applications. Clarifai would like to see what developers can come up with that shows the power of Clarifai and how they can improve their applications with machine learning.
Each member on the winning team for the Best Use of the Clarifai API will receive a Clarifai skateboard and DSLR camera!
Projects solving this challenge
Real time map to improve the world. Anybody can post a news, events, crime, party, anything you can image. All based on hashtags i.e. #event, #crime, #news, #party. Messages older than 5 minutes will be removed. There's stream button that user can press to see the events posted. For "Event", there should be a "uber" button For "Products", there should be a "buy" and "contact" button using Flowroute
An app to play Assassin https://en.wikipedia.org/wiki/Assassin_(game) Players "kill" their target by taking a photo of their opponent without being caught. The photo automatically uploaded and analyzed with the Clarifai API to determine similarity to the base photo taken at the start of the game. The app immediately judges the kill and notifies the would-be killer if their photo was good enough.
zxq is an instagram app that whenever a user tags a photo with #zxq, the image is processed and a comment is added with the top four hashtags of the photo's contents.
Same Page is an application made to help caretakers help people with Autism. The problem that caretakers and behavior therapists face is that it's hard for everyone to be on the same page. This is because meeting up together happens infrequently and information is on the paper trail. Same Page is an iOS application that solves this is problem by making behavior tracking digital, real time and collaborative.
See Food uses Clarifai's image recognition API to display photos of food from local restaurants. A user can add their favorite foods to a list to be reviewed later, or eliminate foods they have no interest in. It provides a quick and visual way to answer the age-old question of "What do I want to eat right now?".
On The Go
Using Clarifai, implement an Amazon Go-like experience. A low-cost open-source solution for small businesses to allow consumers to purchase products, on the go.
audifai brings the communication with a sign language speaker to the next level through innovative sign language recognition technology. auidifai is powered by Clarifai's machine learning engine to read words from photos. audifai aims to close the gaps between sign language speakers and verbal language speakers.
A bot that monitors your pet, and try to guess what your pet is doing. It is done by trying reinforce learning with fast feedback, to build model for custom data. Python would be the main stack.
Face/Image detection usage for smart apps
Face detection APIs can be used to make smart locks for offices, households, security systems. Machine learning can further enhance the features by making smarter decisions about face recognitions. Image detection can be used for smart shopping online, but searching items on different retail sites by just clicking a picture of a product. If you are looking for a team, come join me!
Organizations hosting challenge
Clarifai is an artificial intelligence company that excels in visual recognition, solving real-world problems for businesses and developers alike. Clarifai’s powerful image and video recognition technology is built on the most advanced machine learning systems and made easily accessible by a clean API, empowering developers all over the world to build a new generation of intelligent applications.
Events specific to challenge
Technologies specific to this challenge
Whether you have one image or billions, you are only steps away from using artificial intelligence to recognize your visual content. The API is built around a simple idea. You send inputs (images) to the service and it returns predictions. The type of prediction is based on what model you run the input through. For example, if you run your input through the 'food' model, the predictions it returns will contain concepts that the 'food' model knows about.