Pinned Repositories
-Intracranial-Hemorrhage-Segmentation-Using-Deep-Convolutional-Model-U-Net-
A_General_Framework_for_Uncertainty_Estimation_in_Deep_Learning
This repository provides the code used to implement the framework to provide uncertainty estimates for Deep Learning predictions as described in "A General Framework for Uncertainty Estimation in Deep Learning" (Segù et al., 2019)
acmart-sigproc-template
New template for ACM Conferences (acmart / sigconf)
Active-Contour-Loss-pytorch
An unofficial pytorch implementation for "Learning Active Contour Models for Medical Image Segmentation" by Chen, Xu, et al.
active-learning
Active_Learning_Segmentation_Benchmarking
Deep Learning System course project: Benchmarking Active Learning for Image Segmentation
AI-research-tools
:hammer:AI 方向好用的科研工具
Awesome-LLM-Eval
Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表,主要面向基础大模型评测,旨在探求生成式AI的技术边界.
Awesome-Medical-Healthcare-Dataset-For-LLM
A curated list of popular Datasets, Models and Papers for LLMs in Medical/Healthcare
MPG
onejune2018's Repositories
onejune2018/Active-Contour-Loss-pytorch
An unofficial pytorch implementation for "Learning Active Contour Models for Medical Image Segmentation" by Chen, Xu, et al.
onejune2018/ai-edu
AI education materials for Chinese students, teachers and IT professionals.
onejune2018/ansible
Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy. Avoid writing scripts or custom code to deploy and update your applications — automate in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com/ansible/
onejune2018/azure-docs.zh-cn
onejune2018/benchmark
onejune2018/BR-Net
onejune2018/CBNet
Composite Backbone Network
onejune2018/cleanlab
Finding label errors in datasets and learning with noisy labels.
onejune2018/ECANet
Code for ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
onejune2018/Fleet
Distributed Training API and Benchmark For PaddlePaddle
onejune2018/FluidDoc
Documentations for PaddlePaddle
onejune2018/Learning-Loss-for-Active-Learning
Reproducing experimental results of LL4AL [Yoo et al. 2019 CVPR]
onejune2018/LT-GEE
Google Earth Engine implementation of the LandTrendr spectral-temporal segmentation algorithm. For documentation see:
onejune2018/mask_AD
Mask R-CNN for detecting and blurring advertising on streets.
onejune2018/mdmath
LaTeX Math for Markdown inside of Visual Studio Code.
onejune2018/ModelArts-Lab
ModelArts开发者交流互动平台,@ModelArts服务官网:https://www.huaweicloud.com/product/modelarts.html
onejune2018/NAS-Projects
Several neural architecture search (NAS) algorithms implemented in PyTorch.
onejune2018/PaddleDetection
onejune2018/PaddleSeg
A high performance semantic segmentation toolkit based on PaddlePaddle. (『飞桨』图像分割库)
onejune2018/PCA-GM
Code for ICCV 2019 oral paper: Learning Combinatorial Embedding Networks for Deep Graph Matching
onejune2018/practicalAI
📚 A practical approach to machine learning.
onejune2018/prodigy-recipes
🍳 Recipes for the Prodigy, our fully scriptable annotation tool
onejune2018/PseudoLabeling
Official implementation of "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning"
onejune2018/pyprobml
Python code for "Machine learning: a probabilistic perspective" (2nd edition)
onejune2018/Pytorch-Project-Template
A scalable template for PyTorch projects, with examples in Image Segmentation, Object classification, GANs and Reinforcement Learning.
onejune2018/Serving
A flexible, high-performance serving system for machine learning models(『飞桨』服务器端部署库)
onejune2018/streamlit
Streamlit — The fastest way to build custom ML tools
onejune2018/uda
Unsupervised Data Augmentation (UDA)
onejune2018/visdom
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.
onejune2018/VisualDL
A platform to visualize the deep learning process and result.