With applications ranging from classifying objects in self driving cars to identifying blood cells in healthcare industry to identifying defective items in manufacturing industry, image classification is one of the most important applications of computer vision.

How does it work? Which framework should you use?

Here’s a great tutorial.

In this article, we will understand how to build a basic image classification model in PyTorch and TensorFlow. We will start with a brief overview of both PyTorch and TensorFlow. And then we will take the benchmark MNIST handwritten digit classification dataset and build an image classification model using CNN (Convolutional Neural Network) in PyTorch and TensorFlow.

YOLO, abbreviated as You Only Look Once, was proposed as a real-time object detection technique by Joseph Redmon et al in their research work.

It frames object detection in images as a regression problem to spatially separated bounding boxes and associated class probabilities.

In this approach, a single neural network divides the image into regions and predicts bounding boxes and probabilities for each region.

Here’s great article on the subject.

In this article, we will learn how to detect objects present in the images. For the detection of objects, we will use the YOLO (You Only Look Once) algorithm and demonstrate this task on a few images. In the result, we will get the image with captioned and highlighted objects with their probability of correct detection.

Here’s a great tutorial on how OpenCV.

Time Index:

  • Introduction to Images: 2:17
  • Installations: 4:37
  • Chapter 1: 9:09
  • Chapter 2: 17:01
  • Chapter 3: 27:31
  • Chapter 4: 34:12
  • Chapter 5: 44:59
  • Chapter 6: 50:04
  • Chapter 7: 56:14
  • Chapter 8: 1:15:37
  • Chapter 9: 1:40:31
  • Project 1: 1:46:03
  • Project 2: 2:15:45
  • Project 3: 2:56:34

Here’s an interesting computer vision project that should make recycling easier.

Trash Classifier Example Project The Trash Classifier project, affectionately known as “Where does it go?!”, is designed to make throwing things away faster and more reliable. The Trash Classifier project uses a Machine Learning model trained in Lobe to identify whether an object goes in the garbage, recycling, compost, […]

We live in an amazing time where even beginners can take on project that  were the sole domains of expert researchers a decade ago or were straight up science fiction.

Here’s a great write up on the differences between PyTorch and TensorFlow when it come to object detection.

As I already was an experienced data scientist and had developed a few productive machine learning software components in the past years, I thought it should be a fairly easy job to get online, find a pre-trained CNN and train it further on an unseen data set to enable it to detect some previously unknown objects. Up until about a a year ago I had mostly worked with tree-based models, a lot of scikit-learn as well xgboost and of course tons and tons of pandas. As with many AI tasks, my first approach with this one turned out to be a classic version of a: “Not so fast!“ There are a few stepping stones on the way that you have to know of, that however not many articles and blogs seem to mention. After having spent many hours on this topic and having read a lot of TensorFlow source code I know the answers to questions like:

Computer have wide applications across industries for quality control.

For instance, the majority of all medical data is image-based: The assessment of X-rays and scans is crucial for the right diagnosis and, thus, for the right treatment.

Public health depends on the accurate interpretation of every single image, and many physicians are obliged to choose between longer working hours or doing less detailed and precise medical image analysis.

Right now, medical staffers around the world are stretched thin, this is where AI can come into play.

Artificial intelligence in healthcare speeds up the process of medical image analysis and makes it more accurate and stress-free for medical personnel. Using artificial intelligence, it is possible to detect rare diseases, such as Noonan syndrome, or identify viruses and bacteria by analyzing Petri dish images. Computer vision and machine learning for medical image analysis are becoming as vital as an experienced lab worker with modern equipment.

MIT Introduction to Deep Learning 6.S191: Lecture 6 with Ava Soleimany.

Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

Lecture Outline

  • 0:00 – Introduction
  • 0:58 – Course logistics
  • 3:59 – Upcoming guest lectures
  • 5:35 – Deep learning and expressivity of NNs
  • 10:02 – Generalization of deep models
  • 14:14 – Adversarial attacks
  • 17:00 – Limitations summary
  • 18:18 – Structure in deep learning
  • 22:53 – Uncertainty & bayesian deep learning
  • 28:09 – Deep evidential regression
  • 33:08 – AutoML
  • 36:43 – Conclusion