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Image Classification with OpenCV Java

Anastasia Murzova
Labhesh Valechha
November 4, 2020 Leave a Comment
Deep Learning Image Classification Java OpenCV 4 PyTorch Tutorial

November 4, 2020 By Leave a Comment

OpenCV library is widely used due to its extensive coverage of the computer vision tasks, and availability to involve it in various projects, including deep learning. Usually, OpenCV is used with C++ ...

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Tags: classification Java ONNX OpenCV OpenCV4 PyTorch
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Filed Under: Deep Learning, Image Classification, Java, OpenCV 4, PyTorch, Tutorial

CNN Receptive Field Computation Using Backprop with TensorFlow

Anastasia Murzova
July 23, 2020 Leave a Comment
Computer Vision Stories Deep Learning Feature Detection Image Classification Keras Object Detection Tensorflow Tools Tutorial

July 23, 2020 By Leave a Comment

In our recent post about receptive field computation, we examined the concept of receptive fields using PyTorch. We learned receptive field is the proper tool to understand what the network 'sees' ...

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Tags: bounding box classification CNN deep learning fully convolutional Fully Convolutional Network (FCN) imageNet Keras max activation Object Detection object detector ONNX pre-training preprocess unit pytorch2keras receptive field Resnet resnet18 resnet50 response map Tensorflow threshold
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Filed Under: Computer Vision Stories, Deep Learning, Feature Detection, Image Classification, Keras, Object Detection, Tensorflow, Tools, Tutorial

CNN Fully Convolutional Image Classification with TensorFlow

Anastasia Murzova
July 13, 2020 Leave a Comment
Deep Learning Feature Detection Image Classification Image Processing Keras Object Detection Tensorflow

July 13, 2020 By Leave a Comment

In a previous post, we had covered the concept of fully convolutional neural networks (FCN) in PyTorch, where we showed how we can solve the classification task using the input image of arbitrary ...

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Tags: classification fully convolutional Fully Convolutional Network (FCN) Image Classification imageNet Keras resnet50 Tensorflow
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Filed Under: Deep Learning, Feature Detection, Image Classification, Image Processing, Keras, Object Detection, Tensorflow

SVM using Scikit-Learn in Python

Kushashwa Ravi Shrimali
July 27, 2018 3 Comments
Machine Learning

July 27, 2018 By 3 Comments

SVM with scikit-learn

This post explains the implementation of Support Vector Machines (SVMs) using Scikit-Learn library in Python. We had discussed the math-less details of SVMs in the earlier post. In this post, we ...

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Tags: classification Grid Search Kernel Trick Parameter Tuning Python scikit-learn Support Vector Machine SVM
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Filed Under: Machine Learning

Support Vector Machines (SVM)

Satya Mallick
July 11, 2018 8 Comments
Machine Learning Theory

July 11, 2018 By 8 Comments

Support Vector Machine

Ideas in Machine Learning have a "winner takes all" quality. When an idea takes off, it dominates the field so completely that one tends to believe it is the only idea worth pursuing. Today, Deep ...

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Tags: classification Kernel Trick Radial Basis Function Support Vector Machine SVM
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Filed Under: Machine Learning, Theory

Keras Tutorial: Transfer Learning using pre-trained models

Vikas Gupta
Anastasia Murzova
January 3, 2018 17 Comments
Deep Learning Image Classification Image Recognition Tutorial

January 3, 2018 By 17 Comments

In our previous tutorial, we learned how to use models which were trained for Image Classification on the ILSVRC data. In this tutorial, we will discuss how to use those models as a Feature Extractor ...

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Tags: classification deep learning Keras Tensorflow transfer learning VGG16
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Filed Under: Deep Learning, Image Classification, Image Recognition, Tutorial

About

I am an entrepreneur with a love for Computer Vision and Machine Learning with a dozen years of experience (and a Ph.D.) in the field.

In 2007, right after finishing my Ph.D., I co-founded TAAZ Inc. with my advisor Dr. David Kriegman and Kevin Barnes. The scalability, and robustness of our computer vision and machine learning algorithms have been put to rigorous test by more than 100M users who have tried our products. Read More…

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