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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

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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