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tf-pose-estimation - Deep Pose Estimation implemented using Tensorflow with Custom Architectures for fast inference
'Openpose' for human pose estimation have been implemented using Tensorflow. It also provides several variants that have made some changes to the network structure for real-time processing on the CPU or low-power embedded devices. 2018.5.21 Post-processing part is implemented in c++. It is required compiling the part. See: https://github.com/ildoonet/tf-pose-estimation/tree/master/src/pafprocess 2018.2.7 Arguments in run.py script changed. Support dynamic input size.
deep-learning openpose tensorflow mobilenet pose-estimation convolutional-neural-networks neural-network image-processing human-pose-estimation embedded realtime cnn mobile ros robotics catkinMobileNet-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-v2tf-faster-rcnn - Tensorflow Faster RCNN for Object Detection
For a good and more up-to-date implementation for faster/mask RCNN with multi-gpu support, please see the example in TensorPack here. A Tensorflow implementation of faster RCNN detection framework by Xinlei Chen (xinleic@cs.cmu.edu). This repository is based on the python Caffe implementation of faster RCNN available here.
tensorflow object-detection faster-rcnn coco voc resnet mobilenet tensorboardMobileNet-CoreML - The MobileNet neural network using Apple's new CoreML framework
This is the MobileNet neural network architecture from the paper MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications implemented using Apple's shiny new CoreML framework. This uses the pretrained weights from shicai/MobileNet-Caffe.
core-ml machine-learning mobilenet iostensornets - High level network definitions with pre-trained weights in TensorFlow
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+https://github.com/taehoonlee/tensornets.git).
tensorflow zoo pretrained-models machine-learning deep-learning image-classification object-detection yolo yolov2 yolov3 faster-rcnn resnet inception nasnet pnasnet vgg densenet mobilenet mobilenetv2 squeezenetDepthwiseConvolution - A personal depthwise convolution layer implementation on caffe by liuhao
[^nocudnn]: When turn on cudnn, the memory consuming of mobilenet would increase to unbelievable level. You may try.
mobilenetLightNet - LightNet: Light-weight Networks for Semantic Image Segmentation (Cityscapes and Mapillary Vistas Dataset)
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-losspytorch-mobilenet - PyTorch MobileNet Implementation of "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
PyTorch MobileNet Implementation of "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
pytorch mobilenetmace-models - Mobile AI Compute Engine Model Zoo
This project hosts Mobile AI Compute Engine (MACE) models. Each yml deployment script describes a case of deployments, which will generate one or one group (in case more than one ABIs specified) of static libraries and headers. To learn how to add new models, please refer to MACE documents.
deep-learning neural-networks style-transfer deeplabv3 mobilenet inceptionv3ios-short-core-ml - iOS image classification app using Core ML and MobileNet
Resources for Udacity's Core ML mini-course. This repository contains the iOS image classification app (SmartGroceryList) in various formats including its integration with and without Vision framework. A custom Core ML model is also included. If you don't have Docker installed, you can find instructions on installing here.
coreml mobilenet coremltools iostf-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-detectionawesome-embedded-ai - Curated list of awesome AI resources about embedded and mobile devices
This awesome list will be continually updated. Besides, you can read new bi-weekly-reports: PerfXLab/embedded_ai. A curated list of awesome A.I. & Embedded/Mobile-devices resources, tools and more.
embedded deep-learning machine-learning mobilenet mobile neural-network prun paperclip awesome-listmk-tfjs - Play MK.js with TensorFlow.js
Source code for my article "Playing Mortal Kombat with TensorFlow.js. Transfer learning and data augmentation". You can find the post here and MK.js here.
tensorflow-js mobilenet ml cnn transfer-learningssds_pytorch - Multiple basenet MobileNet v1,v2, ResNet combined with SSD detection method and it's variants such as RFB, FSSD etc
This repo contains many object detection methods that aims at single shot and real time, so the speed is the only thing we talk about. Currently we have some base networks that support object detection task such as MobileNet V2, ResNet, VGG etc. And some SSD variants such as FSSD, RFBNet, Retina, and even Yolo are contained. If you have any faster object detection methods welcome to discuss with me to merge it into our master branches.
pytorch ssd mobilenetv2 mobilenetphp-opencv-examples - Tutorial for computer vision and machine learning in PHP by opencv (installation + examples + docs)
Tutorial for computer vision and machine learning in PHP by opencv (installation + examples + docs)
php7 opencv face detection recognition dnn caffe torch lbf lbph facemark facial-landmarks waifu2x ubuntu docker mobilenet imagenet imagenet-classifier tensorflowopenpose-plus - High-Performance and Flexible Pose Estimation Framework using TensorFlow, OpenPose and TensorRT
train.py automatically download MSCOCO 2017 dataset into dataset/coco17. The default model is VGG19 used in the OpenPose paper. To customize the model, simply changing it in models.py.
tensorlayer tensorflow openpose pose-estimation tensorflow-tutorials mobilenet tensorrt vgg19 horovod jetson-tx1 jetson-tx2 computer-vision deep-learning deep-neural-networks distributed-training gpu affine-transformation vggkeras-mobile-colorizer - U-Net Model conditioned with MobileNet features for Grayscale -> Color mapping
Uses MobileNets for memory efficiency in comparison to Inception-ResNet-V2 so that training can be done on a single GPU (of 4 GB size minimum). Open the data_utils.py script and edit the TRAIN_IMAGE_PATH and VALIDATION_IMAGE_PATH to point to directories of images. There must be at least 1 folder pointed to by each of those paths.
keras mobilenet tensorflow colorization
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