jia2lin3yuan1 Goto Github PK

2020-instanceseg

Deep Variational Instance Segmentation

am3-for-content-moderation

bayes-nn

Lecture notes on Bayesian deep learning

blog-file

blog files : backup & reading . coding and learning notes.

deep-interactive

fcis

Fully Convolutional Instance-aware Semantic Segmentation

filepreprocess

Implementation of some basic preprocess over image dataset, to generate input for different model. like:

focal-loss

Tensorflow version implementation of focal loss for binary and multi classification

gwas-root-analysis

segment of plant and compute traits

image-annotator-docker

jia2lin3yuan1.github.io

github pages

keras-deeplab-v3-plus

Keras implementation of Deeplab v3+ with pretrained weights

keras-gan

Keras implementations of Generative Adversarial Networks.

leetcode-oral

leetcode record

linuxtool

Linux下常用工具及其命令介绍

models

Models and examples built with TensorFlow

my-vim-runtime

mycplusplusmodules

Including useful basic implementation in Image/Video Processing domain.

omni3d-box

Code release for "Omni3D A Large Benchmark and Model for 3D Object Detection in the Wild"

pydensecrf

Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.

pytorch-cyclegan-and-pix2pix

Image-to-image translation in PyTorch (e.g., horse2zebra, edges2cats, and more)

pytorch-meta

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

pytorch-semantic-segmentation

PyTorch for Semantic Segmentation

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

self-driving-car

Udacity Self-Driving Car Engineer Nanodegree projects.

solo

SOLO and SOLOv2 for instance segmentation, ECCV 2020 & NeurIPS 2020.

summary

learning materials summary, including blogs, authors, papers, conference, etc..

tensorboard-guidance

Introduction about how to create log-file visualizing in TensorBoard in different framework

tensorflow-deeplab-resnet

DeepLab-ResNet rebuilt in TensorFlow

tensorflow-examples

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)