54 Repositories
Python caffe-segnet Libraries
Pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments
Cascaded-FCN This repository contains the pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments the liver and its lesions out of
This repository contains code, network definitions and pre-trained models for working on remote sensing images using deep learning
Deep learning for Earth Observation This repository contains code, network definitions and pre-trained models for working on remote sensing images usi
Caffe-like explicit model constructor. C(onfig)Model
cmodel Caffe-like explicit model constructor. C(onfig)Model Installation pip install git+https://github.com/bonlime/cmodel Usage In order to allow usi
The original weights of some Caffe models, ported to PyTorch.
pytorch-caffe-models This repo contains the original weights of some Caffe models, ported to PyTorch. Currently there are: GoogLeNet (Going Deeper wit
Caffe implementation for Hu et al. Segmentation for Natural Language Expressions
Segmentation from Natural Language Expressions This repository contains the Caffe reimplementation of the following paper: R. Hu, M. Rohrbach, T. Darr
Files for a tutorial to train SegNet for road scenes using the CamVid dataset
SegNet and Bayesian SegNet Tutorial This repository contains all the files for you to complete the 'Getting Started with SegNet' and the 'Bayesian Seg
SphereFace: Deep Hypersphere Embedding for Face Recognition
SphereFace: Deep Hypersphere Embedding for Face Recognition By Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj and Le Song License SphereFa
Setup and customize deep learning environment in seconds.
Deepo is a series of Docker images that allows you to quickly set up your deep learning research environment supports almost all commonly used deep le
Not Suitable for Work (NSFW) classification using deep neural network Caffe models.
Open nsfw model This repo contains code for running Not Suitable for Work (NSFW) classification deep neural network Caffe models. Please refer our blo
Largest list of models for Core ML (for iOS 11+)
Since iOS 11, Apple released Core ML framework to help developers integrate machine learning models into applications. The official documentation We'v
Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome
bottom-up-attention This code implements a bottom-up attention model, based on multi-gpu training of Faster R-CNN with ResNet-101, using object and at
Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras
Face Mask Detection Face Mask Detection System built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect
C3D is a modified version of BVLC caffe to support 3D ConvNets.
C3D C3D is a modified version of BVLC caffe to support 3D convolution and pooling. The main supporting features include: Training or fine-tuning 3D Co
Deep Inside Convolutional Networks - This is a caffe implementation to visualize the learnt model
Deep Inside Convolutional Networks This is a caffe implementation to visualize the learnt model. Part of a class project at Georgia Tech Problem State
Keras Realtime Multi-Person Pose Estimation - Keras version of Realtime Multi-Person Pose Estimation project
This repository has become incompatible with the latest and recommended version of Tensorflow 2.0 Instead of refactoring this code painfully, I create
Video2x - A lossless video/GIF/image upscaler achieved with waifu2x, Anime4K, SRMD and RealSR.
Official Discussion Group (Telegram): https://t.me/video2x A Discord server is also available. Please note that most developers are only on Telegram.
Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR 2018).
Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR2018) By Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu and J
ncnn is a high-performance neural network inference framework optimized for the mobile platform
ncnn ncnn is a high-performance neural network inference computing framework optimized for mobile platforms. ncnn is deeply considerate about deployme
Semantic Segmentation in Pytorch. Network include: FCN、FCN_ResNet、SegNet、UNet、BiSeNet、BiSeNetV2、PSPNet、DeepLabv3_plus、 HRNet、DDRNet
🚀 If it helps you, click a star! ⭐ Update log 2020.12.10 Project structure adjustment, the previous code has been deleted, the adjustment will be re-
Implementation of U-Net and SegNet for building segmentation
Specialized project Created by Katrine Nguyen and Martin Wangen-Eriksen as a part of our specialized project at Norwegian University of Science and Te
Caffe models in TensorFlow
Caffe to TensorFlow Convert Caffe models to TensorFlow. Usage Run convert.py to convert an existing Caffe model to TensorFlow. Make sure you're using
Joint Detection and Identification Feature Learning for Person Search
Person Search Project This repository hosts the code for our paper Joint Detection and Identification Feature Learning for Person Search. The code is
A Light CNN for Deep Face Representation with Noisy Labels
A Light CNN for Deep Face Representation with Noisy Labels Citation If you use our models, please cite the following paper: @article{wulight, title=
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
The repository contains source code and models to use PixelNet architecture used for various pixel-level tasks. More details can be accessed at http://www.cs.cmu.edu/~aayushb/pixelNet/.
PixelNet: Representation of the pixels, by the pixels, and for the pixels. We explore design principles for general pixel-level prediction problems, f
DilatedNet in Keras for image segmentation
Keras implementation of DilatedNet for semantic segmentation A native Keras implementation of semantic segmentation according to Multi-Scale Context A
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
ENet in Caffe Execution times and hardware requirements Network 1024x512 1280x720 Parameters Model size (fp32) ENet 20.4 ms 32.9 ms 0.36 M 1.5 MB SegN
Using fully convolutional networks for semantic segmentation with caffe for the cityscapes dataset
Using fully convolutional networks for semantic segmentation (Shelhamer et al.) with caffe for the cityscapes dataset How to get started Download the
DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe.
DeepLab Introduction DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe. It combines densely-compute
SegNet including indices pooling for Semantic Segmentation with tensorflow and keras
SegNet SegNet is a model of semantic segmentation based on Fully Comvolutional Network. This repository contains the implementation of learning and te
Implement slightly different caffe-segnet in tensorflow
Tensorflow-SegNet Implement slightly different (see below for detail) SegNet in tensorflow, successfully trained segnet-basic in CamVid dataset. Due t
SegNet-Basic with Keras
SegNet-Basic: What is Segnet? Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-wise Image Segmentation Segnet = (Encoder + Decoder)
Torch implementation of SegNet and deconvolutional network
Torch implementation of SegNet and deconvolutional network
SegNet-like Autoencoders in TensorFlow
SegNet SegNet is a TensorFlow implementation of the segmentation network proposed by Kendall et al., with cool features like strided deconvolution, a
SegNet model implemented using keras framework
keras-segnet Implementation of SegNet-like architecture using keras. Current version doesn't support index transferring proposed in SegNet article, so
A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Segnet is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This is implementation of http://arxiv.org/pdf/15
Implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling
Caffe SegNet This is a modified version of Caffe which supports the SegNet architecture As described in SegNet: A Deep Convolutional Encoder-Decoder A
Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.
Image Segmentation Keras : Implementation of Segnet, FCN, UNet, PSPNet and other models in Keras. Implementation of various Deep Image Segmentation mo
a reimplementation of LiteFlowNet in PyTorch that matches the official Caffe version
pytorch-liteflownet This is a personal reimplementation of LiteFlowNet [1] using PyTorch. Should you be making use of this work, please cite the paper
A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .onnx]
PINTO_model_zoo Please read the contents of the LICENSE file located directly under each folder before using the model. My model conversion scripts ar
Visualizer for neural network, deep learning, and machine learning models
Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX, TensorFlow Lite, Keras, Caffe, Darknet, ncnn,
Pixel-wise segmentation on VOC2012 dataset using pytorch.
PiWiSe Pixel-wise segmentation on the VOC2012 dataset using pytorch. FCN SegNet PSPNet UNet RefineNet For a more complete implementation of segmentati
Code repo for realtime multi-person pose estimation in CVPR'17 (Oral)
Realtime Multi-Person Pose Estimation By Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh. Introduction Code repo for winning 2016 MSCOCO Keypoints Cha
Oriented Response Networks, in CVPR 2017
Oriented Response Networks [Home] [Project] [Paper] [Supp] [Poster] Torch Implementation The torch branch contains: the official torch implementation
caffe re-implementation of R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection Abstract This is a caffe re-implementation of R2CNN: Rotational Region CNN fo
textspotter - An End-to-End TextSpotter with Explicit Alignment and Attention
An End-to-End TextSpotter with Explicit Alignment and Attention This is initially described in our CVPR 2018 paper. Getting Started Installation Clone
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
MMdnn MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model manage
Visualizer for neural network, deep learning, and machine learning models
Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Tens
hipCaffe: the HIP port of Caffe
Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Cent
Machine Learning Platform for Kubernetes
Reproduce, Automate, Scale your data science. Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applica
Visualizer for neural network, deep learning, and machine learning models
Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Tens
Deep Learning GPU Training System
DIGITS DIGITS (the Deep Learning GPU Training System) is a webapp for training deep learning models. The currently supported frameworks are: Caffe, To
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Build Type Linux MacOS Windows Build Status OpenPose has represented the first real-time multi-person system to jointly detect human body, hand, facia
Caffe: a fast open framework for deep learning.
Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berke