Recognizing objects in images with TensorFlow and Smalltalk

#artificialintelligence

In this post, I will be showing a simple example of object recognition in images using the TensorFlow library from Smalltalk. Whenever you start entering the world of AI and Machine Learning you will notice immediately that Python has been widely accepted as the "default" programming language for these topics. I am not against Python and I believe that people are using it for a reason. However, I do believe that providing alternatives is a good thing, too. And Smalltalk could be that alternative you are looking for.


Object detection with TensorFlow

#artificialintelligence

Attention readers: We invite you to access the corresponding Python code and iPython notebook for this article on GitHub. Image classification can perform some pretty amazing feats, but a large drawback of many image classification applications is that the model can only detect one class per image. With an object detection model, not only can you classify multiple classes in one image, but you can specify exactly where that object is in an image with a bounding box framing the object. The TensorFlow Models GitHub repository has a large variety of pre-trained models for various machine learning tasks, and one excellent resource is their object detection API. The object detection API makes it extremely easy to train your own object detection model for a large variety of different applications.


Comparing MobileNet Models in TensorFlow

#artificialintelligence

In recent years, neural networks and deep learning have sparked tremendous progress in the field of natural language processing (NLP) and computer vision. While many of the face, object, landmark, logo, and text recognition and detection technologies are provided for Internet-connected devices, we believe that the ever-increasing computational power of mobile devices can enable the delivery of these technologies into the hands of users anytime, anywhere, regardless of Internet connection. However, computer vision for on-device and embedded applications faces many challenges -- models must run quickly with high accuracy in a resource-constrained environment, making use of limited computation, power, and space. TensorFlow offers various pre-trained models, such as drag-and-drop models, in order to identify approximately 1,000 default objects. When compared with other similar models, such as the Inception model datasets, MobileNet works better with latency, size, and accuracy.


TensorFlow Mobile: Training and Deploying a Neural Network - inovex-Blog

#artificialintelligence

Smart Assistants, fancy image filters in Snapchat and apps like Prisma all have one thing in common--they are powered by Machine Learning. The use of Machine Learning in mobile apps is growing and new mobile apps are developed with Machine Learning based services as business models. In this blog series we want to give you hands-on advice on how you can train and deploy a convolutional neural network for image classification to a mobile app using the popular machine learning framework TensorFlow Mobile. Our task will be to classify images of houseplants which we have collected ourselves. You don't have to go and snap pictures of plants, however, because our approach is generic and can be used for training and deploying a convolutional neural network for image classification, independent of their subject.


How to Automate Surveillance Easily with Deep Learning

#artificialintelligence

Surveillance is an integral part of security and patrol. For the most part, the job entails extended periods of looking out for something undesirable to happen. It is crucial that we do this, but also it is a very mundane task. Wouldn't life be much simpler if there was something that could do the "watching and waiting" for us? With the advancements in technology over the past few years, we could write some scripts to automate the above tasks -- and that too, rather easily. Anyone familiar with Deep Learning would know that image classifiers have surpassed human level accuracy.