Displaying 1 to 20 from 30 results

tensorpack - A Neural Net Training Interface on TensorFlow

  •    Python

Tensorpack is a training interface based on TensorFlow. It's Yet Another TF high-level API, with speed, readability and flexibility built together.

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".

MobileNet-Caffe - Caffe Implementation of Google's MobileNets (v1 and v2)

  •    Python

We provide pretrained MobileNet models on ImageNet, which achieve slightly better accuracy rates than the original ones reported in the paper.

ml5-library - Friendly machine learning for the web! 🤖

  •    Javascript

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.

tensorlayer - Deep Learning and Reinforcement Learning Library for Developers and Scientists

  •    Python

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.

mmclassification - OpenMMLab Image Classification Toolbox and Benchmark

  •    Jupyter

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.

big_transfer - Official repository for the "Big Transfer (BiT): General Visual Representation Learning" paper

  •    Python

Update 18/06/2021: We release new high performing BiT-R50x1 models, which were distilled from BiT-M-R152x2, see this section. More details in our paper "Knowledge distillation: A good teacher is patient and consistent". Update 08/02/2021: We also release ALL BiT-M models fine-tuned on ALL 19 VTAB-1k datasets, see below.

cvat - Computer Vision Annotation Tool (CVAT) is a web-based tool which helps to annotate video and images for Computer Vision algorithms

  •    Javascript

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.

caffenet-benchmark - Evaluation of the CNN design choices performance on ImageNet-2012.

  •    Jupyter

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.

Switchable-Normalization - Code for Switchable Normalization from "Differentiable Learning-to-Normalize via Switchable Normalization", https://arxiv

  •    HTML

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.

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.

DenseNet-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.

MSDNet - Multi-Scale Dense Networks for Resource Efficient Image Classification (ICLR 2018 Oral)

  •    Lua

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.

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

chainer-pspnet - PSPNet in Chainer

  •    Python

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.

artos - Adaptive Real-Time Object Detection System with HOG and CNN Features

  •    C++

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.

SENet-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.

dawn-bench-entries - DAWNBench: An End-to-End Deep Learning Benchmark and Competition

  •    Python

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.