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image_classification_with_5_methods

Compared performance of KNN, SVM, BPNN, CNN, Transfer Learning (retrain on Inception v3) on image classification problem. CNN is implemented with TensorFlow

keras-image-segmentation

Image segmentation with keras. FCN, Unet, DeepLab V3 plus, Mask RCNN ... etc.

keras-inceptionv4

Keras Implementation of Google's Inception-V4 Architecture (includes Keras compatible pre-trained weights)

learning-3dreconstruction

基于python的单目三维重建

mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

masktrackrcnn

MaskTrackRCNN for video instance segmentation based on mmdetection

mmselfsup

OpenMMLab Self-Supervised Learning Toolbox and Benchmark

moco

PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722

modbus-tk

Create Modbus app easily with Python

models

Models and examples built with TensorFlow

modified-u-net-using-caffeframework

Caffe framework to implement semantic segmentation based on U*-Net.

mtt-distillation

Official code for our CVPR '22 paper "Dataset Distillation by Matching Training Trajectories"

pidinet

Code for the ICCV 2021 paper "Pixel Difference Networks for Efficient Edge Detection" (Oral).

predictablecontextprior

Implementation of CVPR'2022:Surface Reconstruction from Point Clouds by Learning Predictive Context Priors

pycontrast

PyTorch implementation of Contrastive Learning methods

pyqt5-yolov5

YOLOv5检测界面-PyQt5实现

r-fcn

R-FCN: Object Detection via Region-based Fully Convolutional Networks

simsiam

A pytorch implementation for paper 'Exploring Simple Siamese Representation Learning'

straug

Image transformations designed for Scene Text Recognition (STR) data augmentation. Published at ICCV 2021 Workshop on Interactive Labeling and Data Augmentation for Vision.

stud

source code for CVPR'22 paper "Unknown-Aware Object Detection: Learning What You Don’t Know from Videos in the Wild"

tensor2tensor

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

tensorflow-fcn

An Implementation of Fully Convolutional Networks in Tensorflow.

testing-fcn-for-cityscapes

Using fully convolutional networks for semantic segmentation with caffe for the cityscapes dataset

tf_fcn

A TensorFlow Implementation of Fully Convolutional Networks

unet-caffe

U-Net implemented by Caffe Deep Learning framework

vicreg

VICReg official code base

vissl

VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

voc2012-segmentation

A tool to use Pascal VOC 2012 dataset for Semantic Segmentation

w-ood

wenet

Production First and Production Ready End-to-End Speech Recognition Toolkit