Displaying 1 to 20 from 20 results

tf-faster-rcnn - Tensorflow Faster RCNN for Object Detection

  •    Python

For a good and more up-to-date implementation for faster/mask RCNN with multi-gpu support, please see the example in TensorPack here. A Tensorflow implementation of faster RCNN detection framework by Xinlei Chen (xinleic@cs.cmu.edu). This repository is based on the python Caffe implementation of faster RCNN available here.

tensornets - High level network definitions with pre-trained weights in TensorFlow

  •    Python

High level network definitions with pre-trained weights in TensorFlow (tested with >= 1.1.0). You can install TensorNets from PyPI (pip install tensornets) or directly from GitHub (pip install git+https://github.com/taehoonlee/tensornets.git).

TF-Tutorials - A collection of deep learning tutorials using Tensorflow and Python

  •    Jupyter

#Tensorflow Tutorials This repository contains a collection of miscellaneous Jupyter notebooks which implement or provide a tutorial on a different Deep Learning topic. All models are implemented in Tesnorflow.

cnn-models - ImageNet pre-trained models with batch normalization for the Caffe framework

  •    Python

This 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.




awesome-very-deep-learning - 🔥A curated list of papers and code about very deep neural networks

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awesome-very-deep-learning is a curated list for papers and code about implementing and training very deep neural networks. Value Iteration Networks are very deep networks that have tied weights and perform approximate value iteration. They are used as an internal (model-based) planning module.

ResNeXt-DenseNet - PyTorch Implementation for ResNet, Pre-Activation ResNet, ResNeXt, DenseNet, and Group Normalisation

  •    Python

PyTorch Implementation for ResNet, Pre-Activation ResNet, ResNeXt, DenseNet, and Group Normalisation

pytorch-speech-commands - Speech commands recognition with PyTorch

  •    Python

Convolutional neural networks for Google speech commands data set with PyTorch. We, xuyuan and tugstugi, have participated in the Kaggle competition TensorFlow Speech Recognition Challenge and reached the 10-th place. This repository contains a simplified and cleaned up version of our team's code.

resnet.torch - an updated version of fb.resnet.torch with many changes.

  •    Jupyter

This is a fork of https://github.com/facebook/fb.resnet.torch. Refer to that if you need to know the details of this library. This code is heavily modified with many additions throughout my research. Many of the changes are optional and defined in "opts.lua". Here is the list of the additions by no means complete.


ResNeXt.pytorch - Reproduces ResNet-V3 with pytorch

  •    Python

Reproduces ResNet-V3 (Aggregated Residual Transformations for Deep Neural Networks) with pytorch. It should reach ~3.65% on Cifar-10, and ~17.77% on Cifar-100.

tensorbag - Collection of tensorflow notebooks tutorials for implementing the most important Deep Learning algorithms

  •    Jupyter

Tensorbag is a collection of tensorflow tutorial on different Deep Learning and Machine Learning algorithms. The tutorials are organised as jupyter notebooks and require tensorflow >= 1.5. There is a subset of notebooks identified with the tag [quiz] that directly ask to the reader to complete part of the code. In the same folder there is always a complementary notebook with the complete solution.

SENet-Caffe - A Caffe Re-Implementation of SENet

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For offical implementations, please check this repo SENet. Here we provide a pretrained SE-ResNet-50 model on ImageNet, which achieves slightly better accuracy rates than the original one reported in the official repo. You can use the official bvlc caffe to run this model without any modifications.

resnet-cifar10-caffe - ResNet 20 32 44 56 110 for CIFAR10 with caffe

  •    Python

seems there's no much difference between resnet-20 and plain-20. However, from the second plot, you can see that plain-110 have difficulty to converge.

Tensorflow-Computer-Vision-Tutorial - Tutorials of deep learning for computer vision.

  •    Python

In these tutorials, we will learn to build several Convolutional Neural Networks (CNNs) developed recent years. All methods mentioned below are working in progress. Later, they will have their video and text tutorial in Chinese. Visit 莫烦 Python for more.

ResNetCAM-keras - Keras implementation of a ResNet-CAM model

  •    Python

The original Matlab implementation and paper (for AlexNet, GoogLeNet, and VGG16) can be found here. A Keras implementation of VGG-CAM can be found here. This implementation is written in Keras and uses ResNet-50, which was not explored in the original paper.

Model-Playgrounds - A project developed and maintained as part of the aim at bringing current capabilities in machine learning and artificial intelligence into practical use for non-programmers and average computer users

  •    Python

        The Machine Learning Model Playgrounds is a project that is part of the dream of a team of Moses Olafenwa and John Olafenwa to bring current capabilities in machine learning and artificial intelligence into practical use for non-programmers and average computer users. This project is the first step in what we hope will become mainstream application in modern technology in which Computers, Smartphones, Edge Devices and Systems will have in-built state-of-the-art Machine Learning and Artificial Intelligence capabilities without having to connect to cloud based services.         The Machine Learning Model Playgrounds is a series of Windows programs built using pure python libraries and code. Each of the programs is a user-friendly demo of Image Classification powered by a specific image classification model of popular Machine Learning Algorithms trained on the ImageNet (1000 object classes ) dataset. Each program provides a user interface where users can select a picture from their Windows system folder while the program process the selected picture and give top-10 possible results of the objects detected with percentage probability per each result.           This repository contains the source code, models and builds of each of the programs in the Model Playgrounds series. It is provided to allow other developers outside our team to adapt, modify or extend the code to produce more programs that may be specific to a social, business, economic or scientific need.         The dependencies used for this project are listed below:     - Python 3.5.2     - Tensorflow 1.4.0     - Keras 2.0.8     - Numpy 1.13.1     - Scipy 0.19.1     - wxPython 4.0.0 Below you will find the details and pictures of each of the programs in the series.           The ResNet Playground is powered by the ResNet50 model trained on the ImageNet dataset. You can find its source codes in the resnet-playground folder of this repository or follow this link. You can also download the Windows Installer for the program in the Release section of this project or follow this link.           This program is a Windows 64-bit software that can be installed on Windows 7 and later versions of the Operating System. It has an installer size of 227mb and install size of 690mb. The program was compiled using PyInstaller 3.3 for Python 3.5 .

CoreML-samples - Sample code for Core ML using ResNet50 provided by Apple and a custom model generated by coremltools

  •    Jupyter

This is the sample code for Core ML using ResNet50 provided by Apple. ResNet50 can categorize the input image to 1000 pre-trained categories. What's more, this includes a sample code for coremltools converting keras model to mlmodel.

Residual-of-Residual-Networks - Residual Network of Residual Networks in Keras

  •    Python

Ordinarily, Residual networks have hundreds or even thousands of layers to accurately classify images in major image recognition tasks, but building a network by simply stacking residual blocks inevitably limits its optimization ability. This paper attempts to improve the optimization ability of Residual Networks by adding level-wise shortcut connections upon original residual networks, to promote the learning capability of residual networks.

Grad-CAM-tensorflow - tensorflow implementation of Grad-CAM (CNN visualization)

  •    Jupyter

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.