tensorflow

Repo for this project here! A seasoned gardener can diagnose plant stress by visual inspection. For our entry to the Tensorflow Microcontroller Challenge, we chose to highlight the issue of water conservation while pushing the limits of computer vision applications. Our submission, dubbed “Droop, There It Is” builds on previous work to identify droopy, wilted plants. Drought stress in plants typically manifests as visually discernible drooping and wilting, also known as plasmolysis, indicating low turgidity or water pressure....

Intro In a previous post, we discussed scraping a movie poster image corpus with genre labels from imdb and learning image similarity models using tensorflow. In this post, we extend this idea to recommend movie trailers based on audio-visual similarity. Data We started by scraping IMDB for movie trailers and their genre tags as labels. Using Scrapy, it is easy to build a text file of video links to then download with youtube-dl....

Scrapy is a powerful web scraping framework and essential tool for building machine learning datasets. For sites with simple structure, scrapy makes it easy to curate a dataset after launching a spider. Check out the tutorials in scrapy’s documentation. To train a poster similarity model, we first gathered hundreds of thousands of movie posters. More concretely, when scraping IMDb.com, we may be interested in gathering posters from <img> tags under <div> tags with the class "poster"....

The use of streaming services has sharply increased over this past year. Many video streaming platforms prominently feature theatrical posters in content representation. As movie posters are designed to signal theme, genre and era, this representation strongly influences a user’s propensity to watch the title. Domain experts have remarked on how poster elements can convey an emotion or capture attention. Exploring this thesis, Netflix conducted a UX study, using eye tracking to find that 91% of titles are rejected after roughly 1 second of view time....

Introducing TF-Recommenders Recently, Google open sourced a Keras API for building recommender systems called TF-Recommenders. TF-Recommenders is flexible, making it easy to integrate heterogeneous signals like implicit ratings from user interactions, content embeddings, or real-time context info. This module also introduces losses specialized for ranking and retrieval which can be combined to benefit from multi-task learning. The developers emphasize the ease-of-use in research, as well as the robustness for deployment in web-scale applications....

Check out our repo for all the code referenced in this blog! Recommender systems are used by many groups to maximize the presentation of products to users. There is a variety of implementations for building recommender systems, but at their core, these systems are designed to sort a universe of items by their relevance to a user based on user information, item information, or both. One well known algorithm for solving the sorting problem is the Learn-to-Rank model, where the objective is to rank a list of examples by each item’s relevance to a particular user....

We have seen applications in industries like retail, telemedicine, and robotics enabled by video analytics with machine learning. ML practitioners often leverage transfer learning with pretrained models to expedite development. Computer vision applications can benefit from using video analytics frameworks to facilitate faster iteration and experimentation. NVIDIA’s TLT toolkit and the Deepstream SDK 5.0 have made it easy to experiment with various network architectures and quickly deploy them on a NVIDIA powered device for optimized inference....

Check out and contribute to our collection of data privacy resources! AI researchers developed models to identify image pixels featuring people. We apply this to promote privacy by helping you redact personally identifiable info in images. This demo is powered by Tensorflow.js! Drop an image and retrieve the redacted output without ever sending data over the internet. Click on your redacted image when it’s done to save. Consider another use case of delivery robots roaming the streets....

Check out the repo and the video! “Everybody Dance Now” offers a sensational demonstration in combining image-to-image translation with pose estimation to produce photo-realistic ‘do-as-i-do’ motion transfer. Researchers used roughly 20 mins of video shot at 120 fps of a subject moving through a normal range of body motion. It is also important for source and target videos to be taken from similar perspectives. Generally, this is a fixed camera angle at a third person perspective with the subject’s body occupying most of the image....

Check out the repo and enjoy the video on YogAI and ActionAI Wanting a personal trainer to help track our fitness goals, we figured we could build our own. The goal was to build an application that could track how we were exercising and began with Yoga as a simple context. We dubbed our first iteration of this application as YogAI. We thought about the YogAI concept for some time....