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tf-faster-rcnn - Tensorflow Faster RCNN for Object Detection
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.
tensorflow object-detection faster-rcnn coco voc resnet mobilenet tensorboardmmclassification - OpenMMLab Image Classification Toolbox and Benchmark
MMClassification is an open source image classification toolbox based on PyTorch. It is a part of the OpenMMLab project. This project is released under the Apache 2.0 license.
pytorch imagenet image-classification resnet resnext mobilenet shufflenet senet regnetBMW-TensorFlow-Training-GUI - This repository allows you to get started with a gui based training a State-of-the-art Deep Learning model with little to no configuration needed! NoCode training with TensorFlow has never been so easy
This repository allows you to get started with training a State-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset and you can start the training right away and monitor it with TensorBoard. You can even test your model with our built-in Inference REST API. Training with TensorFlow has never been so easy.
docker gui deep-neural-networks computer-vision deep-learning neural-network tensorflow rest-api tensorboard resnet deeplearning object-detection nvidia-docker computervision objectdetection no-code tensorflow-training detection-api tensorflow-gui inference-apiPaddleX - PaddlePaddle End-to-End Development Toolkit（『飞桨』深度学习全流程开发工具）
PaddlePaddle End-to-End Development Toolkit（『飞桨』深度学习全流程开发工具）
deep-neural-networks deployment detection neural-networks classification segmentation resnet deeplearning unet industry jetson mobilenet yolov3tensornets - High level network definitions with pre-trained weights in TensorFlow
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).
tensorflow zoo pretrained-models machine-learning deep-learning image-classification object-detection yolo yolov2 yolov3 faster-rcnn resnet inception nasnet pnasnet vgg densenet mobilenet mobilenetv2 squeezenetTF-Tutorials - A collection of deep learning tutorials using Tensorflow and Python
#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.
tensorflow gan resnetcnn-models - ImageNet pre-trained models with batch normalization for the Caffe framework
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.
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 caffeawesome-very-deep-learning - 🔥A curated list of papers and code about very deep neural networks
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.
highway-network deep-learning densenet resnet awesome-list machine-learning vinself-label - Self-labelling via simultaneous clustering and representation learning. (ICLR 2020)
🆕✅🎉 updated code: 23rd April 2020: bug fixes + CIFAR code + evaluation for resnet & alexnet. Checkout our blogpost for a quick non-technical overview and an interactive visualization of our clusters.
clustering resnet representation-learning self-supervised-learning resnet-v2 iclr2020pytorch-speech-commands - Speech commands recognition with PyTorch
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.
speech-recognition deep-learning cifar10 neural-network kaggle classification pytorch resnet resnext densenet wide-residual-networks dual-path-networksresnet.torch - an updated version of fb.resnet.torch with many changes.
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.
resnet torch deep-learningchainer-cifar10 - Various CNN models including for CIFAR10 with Chainer
See the details at transform function in train.py.
cifar10 chainer deep-learning neural-networks resnet vgg densenet wide-residual-networks residual-networks network-in-network deep-convolutional-networks convolutional-neural-networks convnetResNeXt.pytorch - Reproduces ResNet-V3 with pytorch
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.
pytorch cifar resnet resnextDeepLearningModels - deep learning model sets
deep learning model sets
deeplearning vgg resnet xception inceptionv3 unet segnettensorbag - Collection of tensorflow notebooks tutorials for implementing the most important Deep Learning algorithms
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.
cifar-10 generative-adversarial-networks mnist lenet-5 convolutional-neural-networks resnet-18 notebook deep-learning tensorflow resnet tfrecord-format cifar-100 tensorflow-tutorials kmeans-clustering perceptron autoencoder tutorialSENet-Caffe - A Caffe Re-Implementation of SENet
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.
senet resnet imagenet caffeTensorflow-Computer-Vision-Tutorial - Tutorials of deep learning for computer vision.
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.
computer-vision cnn resnet googlenet lenet deepdreamResNetCAM-keras - Keras implementation of a ResNet-CAM model
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.
keras cnn resnet-50 resnet localization cnn-keras cnn-model cnns localisation image-analysis classification image-classification keras-neural-networks keras-tensorflow keras-visualization keras-models keras-classification-models
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