Archive | Deep Learning for Computer Vision

One-Shot Learning with Siamese Networks, Contrastive, and Triplet Loss for Face Recognition

One-Shot Learning for Face Recognition

One-shot learning is a classification task where one, or a few, examples are used to classify many new examples in the future. This characterizes tasks seen in the field of face recognition, such as face identification and face verification, where people must be classified correctly with different facial expressions, lighting conditions, accessories, and hairstyles given […]

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Detected Face of Jerry Seinfeld, Correctly Identified by the SVM Classifier

How to Develop a Face Recognition System Using FaceNet in Keras

Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. FaceNet is a face recognition system developed in 2015 by researchers at Google that achieved then state-of-the-art results on a range of face recognition benchmark datasets. The FaceNet system can be used broadly thanks to […]

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How to Perform Face Recognition With VGGFace2 Convolutional Neural Network in Keras

How to Perform Face Recognition With VGGFace2 in Keras

Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. Recently, deep learning convolutional neural networks have surpassed classical methods and are achieving state-of-the-art results on standard face recognition datasets. One example of a state-of-the-art model is the VGGFace and VGGFace2 model developed by researchers […]

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Swim Team Photograph With Bounding Boxes and Facial Keypoints Drawn for Each Detected Face Using MTCNN

How to Perform Face Detection with Deep Learning in Keras

Face detection is a computer vision problem that involves finding faces in photos. It is a trivial problem for humans to solve and has been solved reasonably well by classical feature-based techniques, such as the cascade classifier. More recently deep learning methods have achieved state-of-the-art results on standard benchmark face detection datasets. One example is […]

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Photograph Showing Object Detection Masks, Bounding Boxes, and Class Labels

How to Train an Object Detection Model with Keras

Object detection is a challenging computer vision task that involves predicting both where the objects are in the image and what type of objects were detected. The Mask Region-based Convolutional Neural Network, or Mask R-CNN, model is one of the state-of-the-art approaches for object recognition tasks. The Matterport Mask R-CNN project provides a library that […]

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Photograph of Three Zebra Each Detected with the YOLOv3 Model and Localized with Bounding Boxes

How to Perform Object Detection With YOLOv3 in Keras

Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph. It is a challenging problem that involves building upon methods for object recognition (e.g. where are they), object localization (e.g. what are their extent), and object classification (e.g. what are […]

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Photograph of an Elephant With All Objects Detected With a Bounding Box and Mask

How to Use Mask R-CNN in Keras for Object Detection in Photographs

Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph. It is a challenging problem that involves building upon methods for object recognition (e.g. where are they), object localization (e.g. what are their extent), and object classification (e.g. what are […]

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A Gentle Introduction to Object Recognition With Deep Learning

A Gentle Introduction to Object Recognition With Deep Learning

It can be challenging for beginners to distinguish between different related computer vision tasks. For example, image classification is straight forward, but the differences between object localization and object detection can be confusing, especially when all three tasks may be just as equally referred to as object recognition. Image classification involves assigning a class label […]

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How to Develop a Convolutional Neural Network to Classify Satellite Photos of the Amazon Rainforest

How to Develop a Deep CNN for Multi-Label Classification of Photos

The Planet dataset has become a standard computer vision benchmark that involves multi-label classification or tagging the contents satellite photos of Amazon tropical rainforest. The dataset was the basis of a data science competition on the Kaggle website and was effectively solved. Nevertheless, it can be used as the basis for learning and practicing how […]

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