Train and deploy deep learning models for image classification, object detection, instance segmentation, and more.
Develop algorithms and techniques that enable computers to interpret and understand visual data from the world around them.
Research, evaluate, and select key components of your embedded vision system to optimize for performance and cost.
Build a cost-effective prototype that impresses stakeholders or evaluates feasibility of different solutions.
Increase the userbase for your software tool, library, or SDK by creating concise documentation and accessible tutorials.
We design vision-enabled products and provide expertise in every stage of deep learning model development.
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RAIN MAN 2.0 is a card counting blackjack AI that uses deep learning to identify playing cards.
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Building a quality dataset is the most important step of training an object detection model. This article provides tips for effectively gathering and labeling images.
Train and deploy your own TensorFlow Lite object detection model using Google's free GPUs on Google Colab.
TensorFlow Lite provides several object detection models, but how do you choose which model to use for your application? This article compares performance of several popular TFLite models.
