DenseNet-Cifar10 - Train DenseNet on Cifar-10 based on Keras

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Train the DenseNet-40-10 on Cifar-10 dataset with data augmentation. The implementation of DenseNet is based on titu1994/DenseNet. I've made some modifications so as to make it consistent with Keras2 interface.



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DenseNet - DenseNet implementation in Keras

  •    Python

The Bottleneck - Compressed DenseNets offer further performance benefits, such as reduced number of parameters, with similar or better performance. The best original model, DenseNet-100-24 (27.2 million parameters) achieves 3.74 % error, whereas the DenseNet-BC-190-40 (25.6 million parameters) achieves 3.46 % error which is a new state of the art performance on CIFAR-10.

densenet.pytorch - A PyTorch implementation of DenseNet.

  •    Python

This is a PyTorch implementation of the DenseNet-BC architecture as described in the paper Densely Connected Convolutional Networks by G. Huang, Z. Liu, K. Weinberger, and L. van der Maaten. This implementation gets a CIFAR-10+ error rate of 4.77 with a 100-layer DenseNet-BC with a growth rate of 12. Their official implementation and links to many other third-party implementations are available in the liuzhuang13/DenseNet repo on GitHub. As this table from the DenseNet paper shows, it provides competitive state of the art results on CIFAR-10, CIFAR-100, and SVHN.

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

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one-pixel-attack-keras - Keras reimplementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

  •    Jupyter

How simple is it to cause a deep neural network to misclassify an image if an attacker is only allowed to modify the color of one pixel and only see the prediction probability? Turns out it is very simple. In many cases, an attacker can even cause the network to return any answer they want. The following project is a Keras reimplementation and tutorial of "One pixel attack for fooling deep neural networks".

pytorch-cifar - 95.16% on CIFAR10 with PyTorch

  •    Python

I'm playing with PyTorch on the CIFAR10 dataset.

DenseNet-Caffe - DenseNet Caffe Models, converted from


We manually converted the original torch models into caffe format from Update (July 27, 2017): for your convenience, we also provide a link to these models on Baidu Disk.

kaggle-cifar10-torch7 - Code for Kaggle-CIFAR10 competition. 5th place.

  •    Lua

Please check your Torch7/CUDA environment when this code fails. Place the data files into a subfolder ./data.

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New problems can be implemented very easily. You can see in that the meta_minimize method from the MetaOptimizer class is given a function that returns the TensorFlow operation that generates the loss function we want to minimize (see for an example). It's important that all operations with Python side effects (e.g. queue creation) must be done outside of the function passed to meta_minimize. The cifar10 function in is a good example of a loss function that uses TensorFlow queues.

pytorch-cnn-finetune - Fine-tune pretrained Convolutional Neural Networks with PyTorch

  •    Python

VGG and AlexNet models use fully-connected layers, so you have to additionally pass the input size of images when constructing a new model. This information is needed to determine the input size of fully-connected layers. See examples/ file (requires PyTorch 0.4).

SparseConvNet - Submanifold sparse convolutional networks

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efficient_densenet_pytorch - A memory-efficient implementation of DenseNets

  •    Python

A PyTorch implementation of DenseNets, optimized to save GPU memory. While DenseNets are fairly easy to implement in deep learning frameworks, most implmementations (such as the original) tend to be memory-hungry. In particular, the number of intermediate feature maps generated by batch normalization and concatenation operations grows quadratically with network depth. It is worth emphasizing that this is not a property inherent to DenseNets, but rather to the implementation.

DenseNet - Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).

  •    Lua

Gao Huang*, Zhuang Liu*, Laurens van der Maaten and Kilian Weinberger (* Authors contributed equally). Now with much more memory efficient implementation! Please check the technical report and code for more infomation.

DenseNetCaffe - Caffe code for Densely Connected Convolutional Networks (DenseNets)

  •    Python

This repository contains the caffe version code for the paper Densely Connected Convolutional Networks. For a brief introduction of DenseNet, see our original Torch implementation.

LightNet - LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)

  •    Python

This repository contains the code (in PyTorch) for: "LightNet: Light-weight Networks for Semantic Image Segmentation " (underway) by Huijun Liu @ TU Braunschweig. Semantic Segmentation is a significant part of the modern autonomous driving system, as exact understanding the surrounding scene is very important for the navigation and driving decision of the self-driving car. Nowadays, deep fully convolutional networks (FCNs) have a very significant effect on semantic segmentation, but most of the relevant researchs have focused on improving segmentation accuracy rather than model computation efficiency. However, the autonomous driving system is often based on embedded devices, where computing and storage resources are relatively limited. In this paper we describe several light-weight networks based on MobileNetV2, ShuffleNet and Mixed-scale DenseNet for semantic image segmentation task, Additionally, we introduce GAN for data augmentation[17] (pix2pixHD) concurrent Spatial-Channel Sequeeze & Excitation (SCSE) and Receptive Field Block (RFB) to the proposed network. We measure our performance on Cityscapes pixel-level segmentation, and achieve up to 70.72% class mIoU and 88.27% cat. mIoU. We evaluate the trade-offs between mIoU, and number of operations measured by multiply-add (MAdd), as well as the number of parameters.

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

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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+

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


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ImageAI - A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

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A python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.

dlwin - GPU-accelerated Deep Learning on Windows 10 native

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There are certainly a lot of guides to assist you build great deep learning (DL) setups on Linux or Mac OS (including with Tensorflow which, unfortunately, as of this posting, cannot be easily installed on Windows), but few care about building an efficient Windows 10-native setup. Most focus on running an Ubuntu VM hosted on Windows or using Docker, unnecessary - and ultimately sub-optimal - steps. We also found enough misguiding/deprecated information out there to make it worthwhile putting together a step-by-step guide for the latest stable versions of Keras, Tensorflow, CNTK, MXNet, and PyTorch. Used either together (e.g., Keras with Tensorflow backend), or independently -- PyTorch cannot be used as a Keras backend, TensorFlow can be used on its own -- they make for some of the most powerful deep learning python libraries to work natively on Windows.

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