pytorch-segmentation-detection - Image Segmentation and Object Detection in Pytorch

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So far, the library contains an implementation of FCN-32s (Long et al.), Resnet-18-8s, Resnet-34-8s (Chen et al.) image segmentation models in Pytorch and Pytorch/Vision library with training routine, reported accuracy, trained models for PASCAL VOC 2012 dataset. To train these models on your data, you will have to write a dataloader for your dataset. Models for Object Detection will be released soon.

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

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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.

semantic-segmentation mobilenet-v2 deeplabv3plus mixedscalenet senet wide-residual-networks dual-path-networks pytorch cityscapes mapillary-vistas-dataset shufflenet inplace-activated-batchnorm encoder-decoder-model mobilenet light-weight-net deeplabv3 mobilenetv2plus rfmobilenetv2plus group-normalization semantic-context-loss

trashnet - Dataset of images of trash; Torch-based CNN for garbage image classification

•    Lua

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Code (only for the convolutional neural network) and dataset for mine and Mindy Yang's final project for Stanford's CS 229: Machine Learning class. Our paper can be found here. The convolutional neural network results on the poster are dated since we continued working after the end of the quarter and were able to achieve around 75% test accuracy (with 70/13/17 train/val/test split) after changing the weight initialization to the Kaiming method. The pictures were taken by placing the object on a white posterboard and using sunlight and/or room lighting. The pictures have been resized down to 512 x 384, which can be changed in data/constants.py (resizing them involves going through step 1 in usage). The devices used were Apple iPhone 7 Plus, Apple iPhone 5S, and Apple iPhone SE.

deep-learning torch trash dataset image-classification convolutional-neural-networks garbage

universal-data-tool - Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app

•    Javascript

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Try it out at udt.dev, download the desktop app or run on-premise. The Universal Data Tool is a web/desktop app for editing and annotating images, text, audio, documents and to view and edit any data defined in the extensible .udt.json and .udt.csv standard.

machine-learning csv computer-vision deep-learning image-annotation desktop dataset named-entity-recognition classification labeling image-segmentation hacktoberfest semantic-segmentation annotation-tool text-annotation labeling-tool entity-recognition annotate-images image-labeling-tool text-labeling

video-classification-3d-cnn-pytorch - Video classification tools using 3D ResNet

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This is a pytorch code for video (action) classification using 3D ResNet trained by this code. The 3D ResNet is trained on the Kinetics dataset, which includes 400 action classes. This code uses videos as inputs and outputs class names and predicted class scores for each 16 frames in the score mode. In the feature mode, this code outputs features of 512 dims (after global average pooling) for each 16 frames. Torch (Lua) version of this code is available here.

video-classification computer-vision computer-vision-tools pytorch action-recognition deep-learning

CV-pretrained-model - A collection of computer vision pre-trained models.

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A pre-trained model is a model created by some one else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application. For example, if you want to build a self learning car. You can spend years to build a decent image recognition algorithm from scratch or you can take inception model (a pre-trained model) from Google which was built on ImageNet data to identify images in those pictures.

data-science image computer-vision deep-learning neural-network mxnet tensorflow model models keras python3 pytorch model-selection image-classification awesome-list object-detection pretrained-models pretrained video-analysis

Hub - Fastest dataset optimization and management for machine and deep learning

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Note: the translations of this document may not be up-to-date. For the latest version, please check the README in English. Software 2.0 needs Data 2.0, and Hub delivers it. Most of the time Data Scientists/ML researchers work on data management and preprocessing instead of training models. With Hub, we are fixing this. We store your (even petabyte-scale) datasets as single numpy-like array on the cloud, so you can seamlessly access and work with it from any machine. Hub makes any data type (images, text files, audio, or video) stored in cloud usable as fast as if it were stored on premise. With same dataset view, your team can always be in sync.

training data-science machine-learning cloud ai computer-vision deep-learning tensorflow cv ml collaboration pytorch cloud-computing datasets dataset-generation data-processing data-version-control data-pipelines mlops

Labelbox - The most versatile data labeling platform for training expert AI.

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Labelbox is a data labeling tool that's purpose built for machine learning applications. Start labeling data in minutes using pre-made labeling interfaces, or create your own pluggable interface to suit the needs of your data labeling task. Labelbox is lightweight for single users or small teams and scales up to support large teams and massive data sets. Simple image labeling: Labelbox makes it quick and easy to do basic image classification or segmentation tasks. To get started, simply upload your data or a CSV file containing URLs pointing to your data hosted on a server, select a labeling interface, (optional) invite collaborators and start labeling.

image-classification image-segmentation computer-vision tensorflow labeling annotations deep-learning recognition tools image-annotation

mmclassification - OpenMMLab Image Classification Toolbox and Benchmark

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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 regnet

T2F - T2F: text to face generation using Deep Learning

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Text-to-Face generation using Deep Learning. This project combines two of the recent architectures StackGAN and ProGAN for synthesizing faces from textual descriptions. The project uses Face2Text dataset which contains 400 facial images and textual captions for each of them. The data can be obtained by contacting either the RIVAL group or the authors of the aforementioned paper. The code is present in the implementation/ subdirectory. The implementation is done using the PyTorch framework. So, for running this code, please install PyTorch version 0.4.0 before continuing.

gan generative-adversarial-network adversarial-machine-learning progressively-growing-gan text-to-image

Deep-Learning-Boot-Camp - A community run, 5-day PyTorch Deep Learning Bootcamp

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Tel-Aviv Deep Learning Bootcamp is an intensive (and free!) 5-day program intended to teach you all about deep learning. It is nonprofit focused on advancing data science education and fostering entrepreneurship. The Bootcamp is a prominent venue for graduate students, researchers, and data science professionals. It offers a chance to study the essential and innovative aspects of deep learning. Participation is via a donation to the A.L.S ASSOCIATION for promoting research of the Amyotrophic Lateral Sclerosis (ALS) disease.

gpu nvidia docker-image machine-learning deep-learning data-science cuda-kernels kaggle-competition cuda pytorch pytorch-tutorials pytorch-tutorial bootcamp meetup kaggle kaggle-scripts pycuda

BicycleGAN - [NIPS 2017] Toward Multimodal Image-to-Image Translation

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Pytorch implementation for multimodal image-to-image translation. For example, given the same night image, our model is able to synthesize possible day images with different types of lighting, sky and clouds. The training requires paired data. Note: The current software works well with PyTorch 0.4. Check out the older branch that supports PyTorch 0.1-0.3.

pytorch pix2pix gans generative-adversarial-network deep-learning

All-About-the-GAN - All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN

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The purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014. You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title here.

gan adversarial-networks arxiv neural-network unsupervised-learning adversarial-nets image-synthesis deep-learning generative-adversarial-network medical-imaging tensorflow pytorch paper cgan ct-denoising segmentation medical-image-synthesis reconstruction detection classification

Objectron - Objectron is a dataset of short, object-centric video clips

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Objectron is a dataset of short object centric video clips with pose annotations. The Objectron dataset is a collection of short, object-centric video clips, which are accompanied by AR session metadata that includes camera poses, sparse point-clouds and characterization of the planar surfaces in the surrounding environment. In each video, the camera moves around the object, capturing it from different angles. The data also contain manually annotated 3D bounding boxes for each object, which describe the object’s position, orientation, and dimensions. The dataset consists of 15K annotated video clips supplemented with over 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes. In addition, to ensure geo-diversity, our dataset is collected from 10 countries across five continents. Along with the dataset, we are also sharing a 3D object detection solution for four categories of objects — shoes, chairs, mugs, and cameras. These models are trained using this dataset, and are released in MediaPipe, Google's open source framework for cross-platform customizable ML solutions for live and streaming media.

machine-learning ai computer-vision deep-learning neural-network tensorflow augmented-reality pytorch dataset 3d 3d-reconstruction 3d-vision

lightlda - Scalable, fast, and lightweight system for large-scale topic modeling

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LightLDA is a distributed system for large scale topic modeling. It implements a distributed sampler that enables very large data sizes and models. LightLDA improves sampling throughput and convergence speed via a fast O(1) metropolis-Hastings algorithm, and allows small cluster to tackle very large data and model sizes through model scheduling and data parallelism architecture. LightLDA is implemented with C++ for performance consideration.

MMLSpark - Microsoft Machine Learning for Apache Spark

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MMLSpark provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.MMLSpark requires Scala 2.11, Spark 2.1+, and either Python 2.7 or Python 3.5+. See the API documentation for Scala and for PySpark.

machine-learning spark cntk pyspark azure microsoft-machine-learning microsoft ml

Facial-Similarity-with-Siamese-Networks-in-Pytorch - Implementing Siamese networks with a contrastive loss for similarity learning

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The goal is to teach a siamese network to be able to distinguish pairs of images. This project uses pytorch. Any dataset can be used. Each class must be in its own folder. This is the same structure that PyTorch's own image folder dataset uses.

pytorch-tutorial deep-learning neural-network siamese-network pytorch face-recognition

sceneparsing - Development kit for MIT Scene Parsing Benchmark

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Please open an issue for questions, comments, and bug reports. The goal of this benchmark is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. It is similar to semantic segmentation tasks in COCO and Pascal Dataset, but the data is more scene-centric and with a diverse range of object categories. The data for this benchmark comes from ADE20K Dataset (the full dataset will be released after the benchmark) which contains more than 20K scene-centric images exhaustively annotated with objects and object parts. Specifically, the benchmark data is divided into 20K images for training, 2K images for validation, and another batch of held-out images for testing. There are in total 150 semantic categories included in the benchmark for evaluation, which include stuffs like sky, road, grass, and discrete objects like person, car, bed. Note that non-uniform distribution of objects occurs in the images, mimicking a more natural object occurrence in daily scenes.

emcee - The Python ensemble sampling toolkit for affine-invariant MCMC

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emcee is a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the Astrophysics literature. Read the docs at emcee.readthedocs.io.

mcmc mcmc-sampler probabilistic-data-analysis

PyTorch-NLP - Supporting Rapid Prototyping with a Toolkit (incl. Datasets and Neural Network Layers)

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PyTorch-NLP, or torchnlp for short, is a library of neural network layers, text processing modules and datasets designed to accelerate Natural Language Processing (NLP) research. Join our community, add datasets and neural network layers! Chat with us on Gitter and join the Google Group, we're eager to collaborate with you.

pytorch nlp natural-language-processing pytorch-nlp torchnlp data-loader embeddings word-vectors deep-learning dataset metrics neural-network sru machine-learning