Image Understanding

It's not who has the best algorithm that wins; It's who has the most data -- Andrew Ng. Image classification is the task of assigning an input image one label from a fixed set of categories. This is one of the core problems in Computer Vision that, despite its simplicity, has a large variety of practical applications. In this blog I will be demonstrating how deep learning can be applied even if we don't have enough data. I have created my own custom car vs bus classifier with 100 images of each category.

Image Classification is one of the fundamental supervised tasks in the world of machine learning. TensorFlow's new 2.0 version provides a totally new development ecosystem with Eager Execution enabled by default. By me, I assume most TF developers had a little hard time with TF 2.0 as we were habituated to use tf.Session and tf.placeholder that we can't imagine TensorFlow without. Today, we start with simple image classification without using TF Keras, so that we can take a look at the new API changes in TensorFlow 2.0 You can take a look at the Colab notebook for this story. We need to play around with the low-level TF APIs rather than input pipelines.

Image Classification is one of the fundamental supervised tasks in the world of machine learning. TensorFlow's new 2.0 version provides a totally new development ecosystem with Eager Execution enabled by default. By me, I assume most TF developers had a little hard time with TF 2.0 as we were habituated to use tf.Session and tf.placeholder that we can't imagine TensorFlow without. Today, we start with simple image classification without using TF Keras, so that we can take a look at the new API changes in TensorFlow 2.0 You can take a look at the Colab notebook for this story. We need to play around with the low-level TF APIs rather than input pipelines.

Due to the growing demand for improving surveillance capabilities in smart cities, systems need to be developed to provide better monitoring capabilities to competent authorities, agencies responsible for strategic resource management, and emergency call centers. This work assumes that, as a complementary monitoring solution, the use of a system capable of detecting the occurrence of sound events, performing the Sound Events Recognition (SER) task, is highly convenient. In order to contribute to the classification of such events, this paper explored several classifiers over the SESA dataset, composed of audios of three hazard classes (gunshots, explosions, and sirens) and a class of casual sounds that could be misinterpreted as some of the other sounds. The best result was obtained by SGD, with an accuracy of 72.13% with 6.81 ms classification time, reinforcing the viability of such an approach.

After watching dotNetConf videos over the last couple of weeks, I've been really excited to try out some of the new image classification techniques in Visual Studio. The dotNetConf keynote included a section from Bri Actman, who is a Program Manager on the .NET Team (the relevant section is on YouTube from 58m16 to 1hr06m35s). This section showed how developers can integrate various ML techniques and code into their projects using the ModelBuilder tool in Visual Studio – in her example, photographs of the outdoors were classified according to what kind of weather they showed. As well as the keynote, there's another relevant dotNetConf talk by Cesar de la Torre which is also available here on what's new in ML.NET And the way to integrate this into my project looks very straightforward – right click on the project - Add Machine Learning - and choose what type of scenario you want to use, as shown in the screenshot below. I've highlighted the feature that I'm really interested in – image classification.

This paper presents a study of semi-supervised learning with large convolutional networks. We propose a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images (up to 1 billion)... Our main goal is to improve the performance for a given target architecture, like ResNet-50 or ResNext. We provide an extensive analysis of the success factors of our approach, which leads us to formulate some recommendations to produce high-accuracy models for image classification with semi-supervised learning. As a result, our approach brings important gains to standard architectures for image, video and fine-grained classification. For instance, by leveraging one billion unlabelled images, our learned vanilla ResNet-50 achieves 81.2% top-1 accuracy on the ImageNet benchmark.

I think implementing papers is a good approach. Depending on the paper, this might involve just constructing a network of standard layers in the proposed architecture, or it could involve creating custom layers / operations / training loops. Most papers will claim results on standard / openly available datasets, so you can see if you can reproduce their claimed accuracy. Check out Papers with Code: Image Classification subtasks dataset ideas.

I had a lot of fun answering. If you're interested in me or the app, please follow us on twitter or insta (@genevievemp and @thetrashapp). If you sent me messages or emails, I'll get back to you as soon as I can. My name is Genevieve Patterson - I'm the Chief Scientist at TRASH, and a PhD in Computer Vision. I've been working on our AI, Otto, for over a year now, and it's getting smarter with every release - here is a blog post about our latest version, and how it collaborates with user inputs.