Deeplabv3plus PytorchDeepLabv3, DeepLabv3+ and pretrained weights on VOC & Cityscapes
Stars: ✭ 337 (-72.31%)
Tensorflow model slim classifyTrain/Eval the popular network by TF-Slim,include mobilenet/shufflenet/squeezenet/resnet/inception/vgg/alexnet
Stars: ✭ 41 (-96.63%)
hand detectionA Light CNN based Method for Hand Detection and Orientation Estimation
Stars: ✭ 116 (-90.47%)
crowd density segmentationThe code for preparing the training data for crowd counting / segmentation algorithm.
Stars: ✭ 21 (-98.27%)
Mobilefacenet V2🔥improve the accuracy of mobilefacenet(insight face) reached 99.733 in the cfp-ff、 the 99.68+ in lfw,96.71+ in agedb30.🔥
Stars: ✭ 339 (-72.14%)
Mobilenet CoremlThe MobileNet neural network using Apple's new CoreML framework
Stars: ✭ 678 (-44.29%)
Rectlabel SupportRectLabel - An image annotation tool to label images for bounding box object detection and segmentation.
Stars: ✭ 338 (-72.23%)
SWCaffeA Deep Learning Framework customized for Sunway TaihuLight
Stars: ✭ 37 (-96.96%)
OpenposeOpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Stars: ✭ 22,892 (+1781.02%)
keras cv attention modelsKeras/Tensorflow attention models including beit,botnet,CMT,CoaT,CoAtNet,convnext,cotnet,davit,efficientdet,efficientnet,fbnet,gmlp,halonet,lcnet,levit,mlp-mixer,mobilevit,nfnets,regnet,resmlp,resnest,resnext,resnetd,swin,tinynet,uniformer,volo,wavemlp,yolor,yolox
Stars: ✭ 159 (-86.94%)
ImageModelsImageNet model implemented using the Keras Functional API
Stars: ✭ 63 (-94.82%)
Caffe64No dependency caffe replacement
Stars: ✭ 335 (-72.47%)
RandwirennImplementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"
Stars: ✭ 675 (-44.54%)
PyTorch-LMDBScripts to work with LMDB + PyTorch for Imagenet training
Stars: ✭ 49 (-95.97%)
Nn toolsNeural Network Tools: Converter and Analyzer. For caffe, pytorch, draknet and so on.
Stars: ✭ 334 (-72.56%)
darknet2caffeConversion of yolo from DarkNet to Caffe
Stars: ✭ 25 (-97.95%)
LearningThe data is the future of oil, digging the potential value of the data is very meaningful. This library records my road of machine learning study.
Stars: ✭ 330 (-72.88%)
Person searchJoint Detection and Identification Feature Learning for Person Search
Stars: ✭ 666 (-45.28%)
sparsezooNeural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes
Stars: ✭ 264 (-78.31%)
Mace ModelsMobile AI Compute Engine Model Zoo
Stars: ✭ 329 (-72.97%)
Mini ImagenetGenerate mini-ImageNet with ImageNet for fewshot learning
Stars: ✭ 22 (-98.19%)
Data Science Ipython NotebooksData 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.
Stars: ✭ 22,048 (+1711.67%)
R2CNNcaffe re-implementation of R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
Stars: ✭ 80 (-93.43%)
One Pixel Attack KerasKeras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet
Stars: ✭ 1,097 (-9.86%)
TPU-MobilenetSSDEdge TPU Accelerator / Multi-TPU + MobileNet-SSD v2 + Python + Async + LattePandaAlpha/RaspberryPi3/LaptopPC
Stars: ✭ 82 (-93.26%)
Efficientnet PytorchA PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)
Stars: ✭ 6,685 (+449.3%)
GuidedNetCaffe implementation for "Guided Optical Flow Learning"
Stars: ✭ 28 (-97.7%)
Largemargin softmax lossImplementation for <Large-Margin Softmax Loss for Convolutional Neural Networks> in ICML'16.
Stars: ✭ 319 (-73.79%)
Bottom Up AttentionBottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome
Stars: ✭ 989 (-18.73%)
XNOR-NetXNOR-Net, CUDNN5 supported version of XNOR-Net-caffe: https://github.com/loswensiana/BWN-XNOR-caffe
Stars: ✭ 30 (-97.53%)
Assembled CnnTensorflow implementation of "Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network"
Stars: ✭ 319 (-73.79%)
PSPNet-PytorchImplemetation of Pyramid Scene Parsing Network in Pytorch
Stars: ✭ 26 (-97.86%)
Pinto model zooA 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]
Stars: ✭ 634 (-47.9%)
caffe srganA Caffe Implementation of SRGAN
Stars: ✭ 59 (-95.15%)
X2paddleDeep learning model converter for PaddlePaddle. (『飞桨』深度学习模型转换工具)
Stars: ✭ 315 (-74.12%)
ArtosAdaptive Real-Time Object Detection System with HOG and CNN Features
Stars: ✭ 64 (-94.74%)
Pnasnet.pytorchPyTorch implementation of PNASNet-5 on ImageNet
Stars: ✭ 309 (-74.61%)
Swin-TransformerThis is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
Stars: ✭ 8,046 (+561.13%)
TokenLabelingPytorch implementation of "All Tokens Matter: Token Labeling for Training Better Vision Transformers"
Stars: ✭ 385 (-68.36%)
py-faster-rcnn-imagenetTrain faster rcnn on imagine dataset, related blog post: https://andrewliao11.github.io/object/detection/2016/07/23/detection/
Stars: ✭ 133 (-89.07%)
Constrained attention filter(ECCV 2020) Tensorflow implementation of A Generic Visualization Approach for Convolutional Neural Networks
Stars: ✭ 36 (-97.04%)
ShufflenetThis is a fast caffe implementation of ShuffleNet.
Stars: ✭ 446 (-63.35%)
ddrlDeep Developmental Reinforcement Learning
Stars: ✭ 27 (-97.78%)
All Classifiers 2019A collection of computer vision projects for Acute Lymphoblastic Leukemia classification/early detection.
Stars: ✭ 22 (-98.19%)
SeefoodInspired by HBO's Silicon Valley: SeeFood is an iOS app that uses CoreML to detect various dishes
Stars: ✭ 445 (-63.43%)
facial-landmarksFacial landmarks detection with OpenCV, Dlib, DNN
Stars: ✭ 25 (-97.95%)
perceptual-advexCode and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".
Stars: ✭ 44 (-96.38%)
MsdnetMulti-Scale Dense Networks for Resource Efficient Image Classification (ICLR 2018 Oral)
Stars: ✭ 443 (-63.6%)