tf_trt_models - TensorFlow models accelerated with NVIDIA TensorRT

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This repository contains scripts and documentation to use TensorFlow image classification and object detection models on NVIDIA Jetson. The models are sourced from the TensorFlow models repository and optimized using TensorRT. Flash your Jetson TX2 with JetPack 3.2 (including TensorRT).

neural-network tensorflow models realtime inference optimize nvidia image-classification object-detection train tx1 jetson tensorrt tx2

tensorrt - TensorFlow/TensorRT integration

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This repository contains a number of different examples that show how to use TF-TRT. TF-TRT is a part of TensorFlow that optimizes TensorFlow graphs using TensorRT. We have used these examples to verify the accuracy and performance of TF-TRT. For more information see Verified Models. This module provides necessary bindings and introduces TRTEngineOp operator that wraps a subgraph in TensorRT. This module is under active development.

jetson-inference - Guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson

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Welcome to our training guide for inference and deep vision runtime library for NVIDIA DIGITS and Jetson Xavier/TX1/TX2. This repo uses NVIDIA TensorRT for efficiently deploying neural networks onto the embedded platform, improving performance and power efficiency using graph optimizations, kernel fusion, and half-precision FP16 on the Jetson.

deep-learning inference computer-vision embedded image-recognition object-detection segmentation jetson jetson-tx1 jetson-tx2

torch2trt - An easy to use PyTorch to TensorRT converter

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What models are you using, or hoping to use, with TensorRT? Feel free to join the discussion here. Please note, this converter has limited coverage of TensorRT / PyTorch. We created it primarily to easily optimize the models used in the JetBot project. If you find the converter helpful with other models, please let us know.

inference pytorch classification tensorrt jetson-tx2 jetson-xavier jetson-nano

text-classification-models-tf - Tensorflow implementations of Text Classification Models.

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Tensorflow implementation of Text Classification Models. Semi-supervised text classification(Transfer learning) models are implemented at [dongjun-Lee/transfer-learning-text-tf].

tensorflow text-classification

jetson_easy - 🔩 Automatically script to setup and configure your NVIDIA Jetson [Nano, Xavier, TX2i, TX2, TX1, TK1]

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The idea of this project is automatically update and setup your NVIDIA Jetson [Nano, Xavier, TX2i, TX2, TX1, TK1] embedded board without wait a lot of time. The Bibbibi Boddibi Boo script recognize if the script run on the NVIDIA Jetson or remotely and request the address and the password to connect on your board.

opencv kernel deep-learning agx ros nvidia jetson-tx1 zed xavier jetson jetson-tk1 jetson-tx2 nvidia-jetson jetson-easy jetson-performance jetson-nano

jetson_stats - 📊 Simple package for monitoring and control your NVIDIA Jetson [Xavier NX, Nano, AGX Xavier, TX1, TX2]

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jetson-stats is a package for monitoring and control your NVIDIA Jetson [Xavier NX, Nano, AGX Xavier, TX1, TX2] Works with all NVIDIA Jetson ecosystem. Read the Wiki for more detailed information or read the package documentation.

pypi nvidia tegra jetson-tx1 cpu-monitoring jetson jetson-tx2 gpu-monitoring memory-monitoring tegrastats nvidia-jetson jetson-xavier jtop jetson-nano jetson-stats nvpmodel jetson-clocks jetson-release jetson-config jetson-xavier-nx

decision-forests - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras

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TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models and supports classification, regression and ranking. TF-DF is a TensorFlow wrapper around the Yggdrasil Decision Forests C++ libraries. Models trained with TF-DF are compatible with Yggdrasil Decision Forests' models, and vice versa.

machine-learning random-forest tensorflow keras ml decision-trees gradient-boosting interpretability decision-forest

node-tensorflow - Node.js + TensorFlow

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TensorFlow is Google's machine learning runtime. It is implemented as C++ runtime, along with Python framework to support building a variety of models, especially neural networks for deep learning. It is interesting to be able to use TensorFlow in a node.js application using just JavaScript (or TypeScript if that's your preference). However, the Python functionality is vast (several ops, estimator implementations etc.) and continually expanding. Instead, it would be more practical to consider building Graphs and training models in Python, and then consuming those for runtime use-cases (like prediction or inference) in a pure node.js and Python-free deployment. This is what this node module enables.

tensorflow node-tensorflow nodejs machine-learning deep-learning npm-package tf tensor ml ai neural-networks neuralnetworks deeplearning model numerical-computation google

tf-image-segmentation - Image Segmentation framework based on Tensorflow and TF-Slim library

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So far, the framework contains an implementation of the FCN models (training and evaluation) in Tensorflow and TF-Slim library with training routine, reported accuracy, trained models for PASCAL VOC 2012 dataset. To train these models on your data, convert your dataset to tfrecords and follow the instructions below. The end goal is to provide utilities to convert other datasets, report accuracies on them and provide models.

trt_pose - Real-time pose estimation accelerated with NVIDIA TensorRT

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Pre-trained models for human pose estimation capable of running in real time on Jetson Nano. This makes it easy to detect features like left_eye, left_elbow, right_ankle, etc. Training scripts to train on any keypoint task data in MSCOCO format. This means you can experiment with training trt_pose for keypoint detection tasks other than human pose.

real-time pytorch human-pose-estimation pretrained-models jetson live-demo tensorrt human-pose jetson-xavier jetson-nano torch2trt

hub - A library for transfer learning by reusing parts of TensorFlow models.

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TensorFlow Hub is a library to foster the publication, discovery, and consumption of reusable parts of machine learning models. In particular, it provides modules, which are pre-trained pieces of TensorFlow models that can be reused on new tasks. If you'd like to contribute to TensorFlow Hub, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

tensorflow machine-learning transfer-learning embeddings image-classification ml

cnn-text-classification-tf-chinese - CNN for Chinese Text Classification in Tensorflow

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This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post. It is slightly simplified implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in Tensorflow.

text-classification convolutional-neural-networks tensorflow cnn deep-learning chinese nlp

food-101-keras - Food Classification with Deep Learning in Keras / Tensorflow

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If you are reading this on GitHub, the demo looks like this. Please follow the link below to view the live demo on my blog. Convolutional Neural Networks (CNN), a technique within the broader Deep Learning field, have been a revolutionary force in Computer Vision applications, especially in the past half-decade or so. One main use-case is that of image classification, e.g. determining whether a picture is that of a dog or cat.

deep-learning image-classification ai machine-learning food-classification keras tensorflow

text - Making text a first-class citizen in TensorFlow.

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IMPORTANT: When installing TF Text with pip install, please note the version of TensorFlow you are running, as you should specify the corresponding minor version of TF Text (eg. for tensorflow==2.3.x use tensorflow_text==2.3.x). TensorFlow Text provides a collection of text related classes and ops ready to use with TensorFlow 2.0. The library can perform the preprocessing regularly required by text-based models, and includes other features useful for sequence modeling not provided by core TensorFlow.

neuralmonkey - An open-source tool for sequence learning in NLP built on TensorFlow.

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The Neural Monkey package provides a higher level abstraction for sequential neural network models, most prominently in Natural Language Processing (NLP). It is built on TensorFlow. It can be used for fast prototyping of sequential models in NLP which can be used e.g. for neural machine translation or sentence classification. The higher-level API brings together a collection of standard building blocks (RNN encoder and decoder, multi-layer perceptron) and a simple way of adding new building blocks implemented directly in TensorFlow.

neural-machine-translation tensorflow nlp sequence-to-sequence neural-networks nmt machine-translation mt deep-learning image-captioning encoder-decoder gpu

cnn-text-classification-tf - Convolutional Neural Network for Text Classification in Tensorflow

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This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post. It is slightly simplified implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in Tensorflow.

NLP-Models-Tensorflow - Gathers machine learning and Tensorflow deep learning models for NLP problems, 1

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NLP-Models-Tensorflow, Gathers machine learning and tensorflow deep learning models for NLP problems, code simplify inside Jupyter Notebooks 100%. I will attached github repositories for models that I not implemented from scratch, basically I copy, paste and fix those code for deprecated issues.

nlp machine-learning embedded deep-learning chatbot language-detection lstm summarization attention speech-to-text neural-machine-translation optical-character-recognition pos-tagging lstm-seq2seq-tf dnc-seq2seq luong-api

tf-rnn-attention - Tensorflow implementation of attention mechanism for text classification tasks.

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Tensorflow implementation of attention mechanism for text classification tasks. Inspired by "Hierarchical Attention Networks for Document Classification", Zichao Yang et al. (http://www.aclweb.org/anthology/N16-1174).

attention tensorflow rnn text-classification sentiment-analysis

deep-learning-containers - AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet

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AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well.

docker aws mxnet tensorflow pytorch sagemaker tensorflow2