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tensorpack - A Neural Net Training Interface on TensorFlow
Tensorpack is a training interface based on TensorFlow. It's Yet Another TF high-level API, with speed, readability and flexibility built together.
tensorflow imagenet deep-learning reinforcement-learning neural-networks machine-learningone-pixel-attack-keras - Keras reimplementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet
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".
keras cnn cifar10 machine-learning tensorflow deep-learning neural-network imagenet image-processing nlpMobileNet-Caffe - Caffe Implementation of Google's MobileNets (v1 and v2)
We provide pretrained MobileNet models on ImageNet, which achieve slightly better accuracy rates than the original ones reported in the paper.
mobilenet caffe imagenet mobilenetv2 mobilnet-v2labelImg - :metal: LabelImg is a graphical image annotation tool and label object bounding boxes in images
LabelImg is a graphical image annotation tool. It is written in Python and uses Qt for its graphical interface.
python2 python3 annotations tools imagenet deep-learning detection recognition image-classificationml5-library - Friendly machine learning for the web! 🤖
This project is currently in development. ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.
lstm deep-learning imagenet machine-learning neural-network p5xjs p5jstensorlayer - Deep Learning and Reinforcement Learning Library for Developers and Scientists
TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides a large collection of customizable neural layers / functions that are key to build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. Simplicity : TensorLayer lifts the low-level dataflow interface of TensorFlow to high-level layers / models. It is very easy to learn through the rich example codes contributed by a wide community.
tensorlayer deep-learning tensorflow machine-learning data-science neural-network reinforcement-learning artificial-intelligence gan a3c tensorflow-tutorials dqn object-detection chatbot tensorflow-tutorial imagenet googlecvat - Computer Vision Annotation Tool (CVAT) is a web-based tool which helps to annotate video and images for Computer Vision algorithms
CVAT is completely re-designed and re-implemented version of Video Annotation Tool from Irvine, California tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team. Code released under the MIT License.
video-annotation computer-vision computer-vision-annotation deep-learning image-annotation annotation-tool annotation labeling labeling-tool image-labeling image-labelling-tool bounding-boxes boundingbox image-classification annotations imagenet detection recognition tensorflowcaffenet-benchmark - Evaluation of the CNN design choices performance on ImageNet-2012.
Welcome to evaluation of CNN design choises performance on ImageNet-2012. Here you can find prototxt's of tested nets and full train logs. **upd2.: Some of the pretrained models are in Releases section. They are licensed for unrestricted use.
convolutional-neural-networks convolutional-networks batch-size caffenet lr-policy architecture relu caffe benchmark activations dataset imagenetSwitchable-Normalization - Code for Switchable Normalization from "Differentiable Learning-to-Normalize via Switchable Normalization", https://arxiv
Switchable Normalization is a normalization technique that is able to learn different normalization operations for different normalization layers in a deep neural network in an end-to-end manner. This repository provides imagenet classification results and models trained with Switchable Normalization. You are encouraged to cite the following paper if you use SN in research.
normalization deeplearning convolutional-neural-networks imagenet pytorchcnn-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 caffeDenseNet-Caffe - DenseNet Caffe Models, converted from https://github.com/liuzhuang13/DenseNet
We manually converted the original torch models into caffe format from https://github.com/liuzhuang13/DenseNet. Update (July 27, 2017): for your convenience, we also provide a link to these models on Baidu Disk.
densenet caffe torch imagenet deep-learningMSDNet - Multi-Scale Dense Networks for Resource Efficient Image Classification （ICLR 2018 Oral）
This repository provides the code for the paper Multi-Scale Dense Networks for Resource Efficient Image Classification. This paper studies convolutional networks that require limited computational resources at test time. We develop a new network architecture that performs on par with state-of-the-art convolutional networks, whilst facilitating prediction in two settings: (1) an anytime-prediction setting in which the network's prediction for one example is progressively updated, facilitating the output of a prediction at any time; and (2) a batch computational budget setting in which a fixed amount of computation is available to classify a set of examples that can be spent unevenly across 'easier' and 'harder' examples.
deep-learning imagenet cifar computer-visionchainer-pspnet - PSPNet in Chainer
This is an unofficial implementation of Pyramid Scene Parsing Network (PSPNet) in Chainer. Caffe is NOT needed to convert .caffemodel to Chainer model. Use caffe_pb2.py.
semantic-segmentation deep-learning pspnet cityscapes pascal-voc ade20k imagenet convolutional-neural-networks chainerartos - Adaptive Real-Time Object Detection System with HOG and CNN Features
ARTOS is the Adaptive Real-Time Object Detection System, created at the University of Jena (Germany). It can be used to quickly learn models for visual object detection without having to collect a set of samples manually. To make this possible, it uses ImageNet, a large image database with more than 20,000 categories. It provides an average of 300-500 images with bounding box annotations for more than 3,000 of those categories and, thus, is suitable for object detection. The purpose of ARTOS is not limited to using those images in combination with clustering and a technique called Whitened Histograms of Orientations (WHO, Hariharan et al.) to quickly learn new models, but also includes adapting those models to other domains using in-situ images and applying them to detect objects in images and video streams.
object-detection imagenet hog whitened-hog gui machine-learning machinelearning machine-learning-library computer-vision image-processingtf-mobilenet-v2 - Mobilenet V2(Inverted Residual) Implementation & Trained Weights Using Tensorflow
Mobilenet V2(Inverted Residual) Implementation & Trained Weights Using Tensorflow
tensorflow deeplearning mobilenet residual cnn convolutional-neural-networks google imagenet image-classification image-detectionSENet-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 caffedawn-bench-entries - DAWNBench: An End-to-End Deep Learning Benchmark and Competition
To add your model to our leaderboard, open a Pull Request with title <Model name> || <Task name> || <Author name> (example PR), with JSON (and TSV where applicable) result files in the format outlined below. We evaluate image classification performance on the CIFAR10 dataset.
imagenet squad cifar10 inference training deeplearningimagenetscraper - 👁 Bulk-download all thumbnails from an ImageNet synset, with optional rescaling
Command-line utility for downloading all thumbnail images from an ImageNet synset, optionally rescaling to a different resolution. SYNSET_ID is the n00000000 part. For example, for the "person, individual, someone, somebody, mortal, soul" synset at http://image-net.org/synset?wnid=n00007846, the corresponding synset id is n00007846.
imagenet scraper machine-learning datasetImageModels - ImageNet model implemented using the Keras Functional API
For full details of this repository please see the MKDocs documentation provided here.
image-processing imagenet imagenet-classifier keras mkdocs mkdocs-material
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