Food101 CoremlA CoreML model which classifies images of food
Stars: ✭ 119 (-6.3%)
Image Quality AssessmentConvolutional Neural Networks to predict the aesthetic and technical quality of images.
Stars: ✭ 1,300 (+923.62%)
KerasrR interface to the keras library
Stars: ✭ 90 (-29.13%)
DeepecgECG classification programs based on ML/DL methods
Stars: ✭ 124 (-2.36%)
Capsnet PytorchMy attempt at implementing CapsNet from the paper Dynamic Routing Between Capsules
Stars: ✭ 87 (-31.5%)
DeepwayThis project is an aid to the blind. Till date there has been no technological advancement in the way the blind navigate. So I have used deep learning particularly convolutional neural networks so that they can navigate through the streets.
Stars: ✭ 118 (-7.09%)
Niftynet[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy
Stars: ✭ 1,276 (+904.72%)
JsnetJavascript/WebAssembly deep learning library for MLPs and convolutional neural networks
Stars: ✭ 126 (-0.79%)
Tf Mobilenet V2Mobilenet V2(Inverted Residual) Implementation & Trained Weights Using Tensorflow
Stars: ✭ 85 (-33.07%)
Mp Cnn TorchMulti-Perspective Convolutional Neural Networks for modeling textual similarity (He et al., EMNLP 2015)
Stars: ✭ 106 (-16.54%)
Pynq DlXilinx Deep Learning IP
Stars: ✭ 84 (-33.86%)
All Conv KerasAll Convolutional Network: (https://arxiv.org/abs/1412.6806#) implementation in Keras
Stars: ✭ 115 (-9.45%)
TnnBiologically-realistic recurrent convolutional neural networks
Stars: ✭ 83 (-34.65%)
Facial Expression RecognitionClassify each facial image into one of the seven facial emotion categories considered using CNN based on https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge
Stars: ✭ 82 (-35.43%)
Lenet 5PyTorch implementation of LeNet-5 with live visualization
Stars: ✭ 122 (-3.94%)
Idn CaffeCaffe implementation of "Fast and Accurate Single Image Super-Resolution via Information Distillation Network" (CVPR 2018)
Stars: ✭ 104 (-18.11%)
Deep K Means Pytorch[ICML 2018] "Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions"
Stars: ✭ 115 (-9.45%)
EmnistA project designed to explore CNN and the effectiveness of RCNN on classifying the EMNIST dataset.
Stars: ✭ 81 (-36.22%)
Sigmoidal aiTutoriais de Python, Data Science, Machine Learning e Deep Learning - Sigmoidal
Stars: ✭ 103 (-18.9%)
SeranetSuper Resolution of picture images using deep learning
Stars: ✭ 79 (-37.8%)
Deepco3[CVPR19] DeepCO3: Deep Instance Co-segmentation by Co-peak Search and Co-saliency (Oral paper)
Stars: ✭ 127 (+0%)
Wav2letterSpeech Recognition model based off of FAIR research paper built using Pytorch.
Stars: ✭ 78 (-38.58%)
BrainforgeA Neural Networking library based on NumPy only
Stars: ✭ 114 (-10.24%)
Quicknat pytorchPyTorch Implementation of QuickNAT and Bayesian QuickNAT, a fast brain MRI segmentation framework with segmentation Quality control using structure-wise uncertainty
Stars: ✭ 74 (-41.73%)
Antialiased Cnnspip install antialiased-cnns to improve stability and accuracy
Stars: ✭ 1,363 (+973.23%)
DrlnDensely Residual Laplacian Super-resolution, IEEE Pattern Analysis and Machine Intelligence (TPAMI), 2020
Stars: ✭ 120 (-5.51%)
Equivariant Transformers Equivariant Transformer (ET) layers are image-to-image mappings that incorporate prior knowledge on invariances with respect to continuous transformations groups (ICML 2019). Paper: https://arxiv.org/abs/1901.11399
Stars: ✭ 68 (-46.46%)
Cutmixa Ready-to-use PyTorch Extension of Unofficial CutMix Implementations with more improved performance.
Stars: ✭ 99 (-22.05%)
Deep PlantDeep-Plant: Plant Classification with CNN/RNN. It consists of CAFFE/Tensorflow implementation of our PR-17, TIP-18 (HGO-CNN & PlantStructNet) and MalayaKew dataset.
Stars: ✭ 66 (-48.03%)
DeepgazeComputer Vision library for human-computer interaction. It implements Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks, Skin Detection through Backprojection, Motion Detection and Tracking, Saliency Map.
Stars: ✭ 1,552 (+1122.05%)
Bayesian cnnBayes by Backprop implemented in a CNN in PyTorch
Stars: ✭ 98 (-22.83%)
QuiverInteractive convnet features visualization for Keras
Stars: ✭ 1,619 (+1174.8%)
GtsrbConvolutional Neural Network for German Traffic Sign Recognition Benchmark
Stars: ✭ 65 (-48.82%)
Sign LanguageSign Language Recognition for Deaf People
Stars: ✭ 65 (-48.82%)
Sigver wiwdLearned representation for Offline Handwritten Signature Verification. Models and code to extract features from signature images.
Stars: ✭ 112 (-11.81%)
Rcnn Relation ExtractionTensorflow Implementation of Recurrent Convolutional Neural Network for Relation Extraction
Stars: ✭ 64 (-49.61%)
CnniqaCVPR2014-Convolutional neural networks for no-reference image quality assessment
Stars: ✭ 96 (-24.41%)
Deep RankingLearning Fine-grained Image Similarity with Deep Ranking is a novel application of neural networks, where the authors use a new multi scale architecture combined with a triplet loss to create a neural network that is able to perform image search. This repository is a simplified implementation of the same
Stars: ✭ 64 (-49.61%)
ContextConText v4: Neural networks for text categorization
Stars: ✭ 120 (-5.51%)
Rcnn Text ClassificationTensorflow Implementation of "Recurrent Convolutional Neural Network for Text Classification" (AAAI 2015)
Stars: ✭ 127 (+0%)
Pytorch convlstmconvolutional lstm implementation in pytorch
Stars: ✭ 126 (-0.79%)
Aaltd18Data augmentation using synthetic data for time series classification with deep residual networks
Stars: ✭ 124 (-2.36%)
Ti PoolingTI-pooling: transformation-invariant pooling for feature learning in Convolutional Neural Networks
Stars: ✭ 119 (-6.3%)
Cs231n Convolutional Neural Networks SolutionsAssignment solutions for the CS231n course taught by Stanford on visual recognition. Spring 2017 solutions are for both deep learning frameworks: TensorFlow and PyTorch.
Stars: ✭ 110 (-13.39%)
ProjectaiaiAiAi.care project is teaching computers to "see" chest X-rays and interpret them how a human Radiologist would. We are using 700,000 Chest X-Rays + Deep Learning to build an FDA 💊 approved, open-source screening tool for Tuberculosis and Lung Cancer. After an MRMC clinical trial, AiAi CAD will be distributed for free to emerging nations, charitable hospitals, and organizations like WHO 🌏 We will also release our pretrained models and weights as Medical Imagenet.
Stars: ✭ 92 (-27.56%)