NOTE: There is another awesome visualization of CNN called CNN-Fixations, which involvs only forward pass. Demo code is available for Caffe and Tensorflow ResNet, Vgg. Please check it out. This is tensorflow version of demo for Grad-CAM. I used ResNet-v1-101, ResNet-v1-50, and vgg16 for demo because this models are very popular CNN model. However grad-cam can be used with any other CNN models. Just modify convolution layer in my demo code.
https://github.com/insikk/Grad-CAM-tensorflowTags | machinelearning convolutional-neural-networks visualization tesnorflow gradcam grad-cam resnet vgg16 |
Implementation | Jupyter Notebook |
License | Public |
Platform |
If the sign of the value given by the saliency mask is not important, then use VisualizeImageGrayscale, otherwise use VisualizeImageDiverging. See the SmoothGrad paper for more details on which visualization method to use. This example iPython notebook shows these techniques is a good starting place.
machine-learning deep-learning deep-neural-networks tensorflow convolutional-neural-networks saliency-map object-detection image-recognitionA simple and well designed structure is essential for any Deep Learning project, so after a lot of practice and contributing in tensorflow projects here's a tensorflow project template that combines simplcity, best practice for folder structure and good OOP design. The main idea is that there's much stuff you do every time you start your tensorflow project, so wrapping all this shared stuff will help you to change just the core idea every time you start a new tensorflow project. You will find a template file and a simple example in the model and trainer folder that shows you how to try your first model simply.
tesnorflow software-engineering oop deep-learning neural-network convolutional-neural-networks tensorflow-tutorials deep-learning-tutorial best-practices tensorflow templateThis repository contains convolutional neural network (CNN) models trained on ImageNet by Marcel Simon at the Computer Vision Group Jena (CVGJ) using the Caffe framework as published in the accompanying technical report. Each model is in a separate subfolder and contains everything needed to reproduce the results. This repository focuses currently contains the batch-normalization-variants of AlexNet and VGG19 as well as the training code for Residual Networks (Resnet). No mean subtraction is required for the pre-trained models! We have a batch-normalization layer which basically does the same.
cnn-model resnet imagenet alexnet batch-normalization caffe-framework vgg16 vgg19 vggnet vgg resnet-10 resnet-50 resnet-preact ilsvrc pretrained-models pre-trained fine-tune fine-tuning-cnns very-deep-cnn caffeWe propose a simple technique to expose the implicit attention of Convolutional Neural Networks on the image. It highlights the most informative image regions relevant to the predicted class. You could get attention-based model instantly by tweaking your own CNN a little bit more. The paper is published at CVPR'16. You also could take a look at the unified PlacesCNN scene prediction code to see how the CAM along with scene categories, scene attributes are predicted. It has been used in the PlacesCNN scene recognition demo.
A generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception. This model has been pre-trained for the ImageNet Large Visual Recognition Challenge using the data from 2012, and it can differentiate between 1,000 different classes, like Dalmatian, dishwasher etc. The program applies Transfer Learning to this existing model and re-trains it to classify a new set of images.
image-detection machine-learning deep-learning deep-neural-networks convolutional-neural-networks tensorflowFigure 1: Original image and the reconstructed versions from maxpool layer 1,2 and 3 of Alexnet generated using tf_cnnvis. The function to generate the activation visualizations of the input image at the given layer.
tensorflow tensorboard convolutional-neural-networks cnn visualization deepdream convolutional-networksThis repository contains the code release for our paper titled as "Text-Independent Speaker Verification Using 3D Convolutional Neural Networks". The link to the paper is provided as well. The code has been developed using TensorFlow. The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Speaker Verification (SR) by using 3D convolutional neural networks following the SR protocol.
convolutional-neural-networks deep-learning speaker-recognition 3dThis repository is the out project about mood recognition using convolutional neural network for the course Seminar Neural Networks at TU Delft. We use the FER-2013 Faces Database, a set of 28,709 pictures of people displaying 7 emotional expressions (angry, disgusted, fearful, happy, sad, surprised and neutral).
emotion-recognition tensorflow machine-learning deep-neural-networks convolutional-neural-networksThis repository contains (TensorFlow and Keras) code that goes along with a related blog post and talk (PDF). Together, they act as a systematic look at convolutional neural networks from theory to practice, using artistic style transfer as a motivating example. The blog post provides context and covers the underlying theory, while working through the Jupyter notebooks in this repository offers a more hands-on learning experience. If you have any questions about any of this stuff, feel free to open an issue or tweet at me: @copingbear.
convolutional-neural-networks tutorial-code talk tensorflow kerasSome examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.
recurrent-neural-networks convolutional-neural-networks deep-learning-tutorial tensorflow tensorlayer keras deep-reinforcement-learning tensorflow-tutorials deep-learning machine-learning notebook autoencoder multi-layer-perceptron reinforcement-learning tflearn neural-networks neural-network neural-machine-translation nlp cnnThis code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post. It is slightly simplified implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in Tensorflow.
text-classification convolutional-neural-networks tensorflow cnn deep-learning chinese nlpJavaScript API for face detection and face recognition in the browser with tensorflow.js
face-recognition face js tensorflow tfjs neural-network resnet-34 convolutional-neural-networks face-detection face-similarity ssd-mobilenet face-landmarks mtcnn yolov2 tiny-yolo detection recognition tfAndroid TensorFlow MachineLearning Example (Building TensorFlow for Android)
tensorflow tensorflow-tutorials tensorflow-android machine-learning machine-learning-android tensorflow-models tensorflow-examples deep-learning deep-neural-networks deeplearning deep-learning-tutorialReal-time object detection and classification. Paper: version 1, version 2. Read more about YOLO (in darknet) and download weight files here. In case the weight file cannot be found, I uploaded some of mine here, which include yolo-full and yolo-tiny of v1.0, tiny-yolo-v1.1 of v1.1 and yolo, tiny-yolo-voc of v2.
tensorflow graph darknet deep-learning deep-neural-networks convolutional-neural-networks convolutional-networks image-processing object-detection machine-learning real-time mobile-developmentBender is an abstraction layer over MetalPerformanceShaders useful for working with neural networks. Bender is an abstraction layer over MetalPerformanceShaders which is used to work with neural networks. It is of growing interest in the AI environment to execute neural networks on mobile devices even if the training process has been done previously. We want to make it easier for everyone to execute pretrained networks on iOS.
machine-learning neural-networks metal apple iphone ios convolutional-neural-networks deep-learning deep-neural-networks residual-networksThe goal of this repository is to provide comprehensive tutorials for TensorFlow while maintaining the simplicity of the code. Each tutorial includes a detailed explanation (written in .ipynb) format, as well as the source code (in .py format).
deep-learning tensorflow reinforcement-learning machine-learning pattern-recognition object-detection convolutional-neural-networks recurrent-neural-networks neural-networkNiftyNet is a consortium of research organisations (BMEIS -- School of Biomedical Engineering and Imaging Sciences, King's College London; WEISS -- Wellcome EPSRC Centre for Interventional and Surgical Sciences, UCL; CMIC -- Centre for Medical Image Computing, UCL; HIG -- High-dimensional Imaging Group, UCL), where BMEIS acts as the consortium lead. NiftyNet is not intended for clinical use.
tensorflow distributed ml neural-network python2 python3 pip deep-neural-networks deep-learning convolutional-neural-networks medical-imaging medical-image-computing medical-image-processing medical-images segmentation gan autoencoder medical-image-analysis image-guided-therapyKeras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
deep-learning tensorflow theano neural-networks machine-learning data-scienceThis code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post. It is slightly simplified implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in Tensorflow.
Most of the codes are simple refactorings of Aymeric Damien's Tutorial or Nathan Lintz's Tutorial. There could be missing credits. Please let me know.
tensorflow-tutorials convolutional-neural-networks recurrent-neural-networks
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