If you’ve ever worked with deep learning frameworks you know they require a good amount of time, knowledge and, most of all, commitment just to get them up and running on your machine. And, that’s before you’ve even built your model or trained your data.
This assumes you have a GPU-enabled server that you don’t mind provisioning and maintaining. Algorithmia is excited to introduce three deep learning frameworks that are now supported on our platform: TensorFlow, Theano and Caffe!
To showcase these commonly used frameworks, we’ve added 16 open source deep learning models to the platform for you to try out, or use in your applications. Here’s a roundup of the deep learning algorithms available to run as microservices on Algorithmia:
Places 365 Classifier
An image classifier using a pre-trained CNN and is based on Places: An Image Database for Deep Scene Understanding B. Zhou, et al., 2016. This algorithm is trained using the deep learning framework Caffe, and it classifies images as a particular location, such as a courtyard, drugstore, hotel room or even a landscape tag such as a glacier or hot spring. This algorithm is particularly useful in travel, real estate or map applications. Try out the place recognition demo we created here.
Deep Face Recognition
A classifier trained to recognize celebrities using Caffe based on the work of Parkhi, O. M., et al., 2015.This classifier takes an image of a person’s face, and returns the likely celebrity names, along with confidence values. Of course, this algorithm can be used to detect celebrities in a newsfeed or other source, but it might also be fun to use it to see what celebrity you look most like. Try building an application around that.
Real Estate Classifier
A Caffe algorithm we trained in-house. It was built specifically for real estate applications, and includes image classification for features such as pools, a walk-in-closets, and house types, like a beach house or colonial house.
Colorful Image Colorization
An algorithm originally developed by Richard Zhang, Phillip Isola, Alexei A. Efros, which takes black and white pictures, and returns the image in color. The algorithm uses the deep learning caffe framework to classify objects/regions within the image and colors them accordingly. So go ahead and take those old pictures from your photo albums and see what they look like in color! Try the colorize photo demo we created here.
An image tagging algorithm using Caffe based on Illustration2Vec: A Semantic Vector Representation of Illustrations by Saito, Masaki and Matsui, Yusuke. This algorithm takes an image and calculates the similarity between the image and available tags. It returns four categories of predicted tags when available: NSFW rating, character, copyright, and general (tags your image belongs to such as “male,” or “black hair”). This algorithm can help with grouping alike images into categories or detect images that aren’t appropriate for some settings.
A direct implementation of Google’s InceptionNet using Tensorflow. It takes an image (such as a car), and returns the top 5 classes the model predicts are relevant to the image. This could include “race car,” “sports car,” or ‘”convertible” classes for a car, while an image of a dog could return the breed of dog such as “husky.” With this algorithm you can bring object recognition to your app easily via the Algorithmia API.
A popular language parser that Google recently open-sourced using Tensorflow. The Parsey McParseface neural model is an incredibly accurate sentence parser and Parts-of-Speech tagger that can be used for computational linguistic problems such as sentiment analysis and comparative opinions. Use it to build intelligent chatbots.
A fun algorithm from ArtsyNetworks GitHub using the deep learning framework Theano. The algorithm takes an image and stylizes it according to various artists or art forms. Users can find an artists style they like such as Alphonse Mucha’s Art Nouveau, and then submit an image of an example of that style (such as Dance by Mucha) along with their own image including metadata and the algorithm returns a stylized image.
Here’s a few more of our deep learning models:
- Introduction to Emotion Recognition
- SalNet: Deep Convolutional Network Prediction: Automatically detects the most relevant parts in an image.
- CaffeNet: An image classifier built on the AlexNet architecture.
- Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns: Autodetects emotions in the given image.
- Large-scale Image Memorability: Returns how memorable the given image is.
- Age Classification: Classifies the age range of a person in a given image.
- Gender Classification: Classifies the gender of a person in a given image.
- Subreddit Classifier: Determine which subreddit an image came from.
- Nudity Detection Ensemble: Detect nudity in pictures.
The above examples are already available on Algorithmia. We’re working to add more, and since we’re an open platform, we encourage everyone from academics to developers to data scientists to add their work and make it available to others.
Ready to host your deep learning models on Algorithmia? Get started with our model guides.
To learn more, visit our Introduction to Microservices blog post.