Yang Cheng's repositories
NonCuboidRoom
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image
pytorch_ssn
A pytorch version of SSN
awesome-holistic-3d
A list of papers and resources (data,code,etc) for holistic 3D reconstruction in computer vision
awesome-scene-understanding
A list of papers for scene understanding in computer vision.
CVPR2020_Auxiliary_Quantization
Training Quantized Neural Networks with a Full-precision Auxiliary Module
EWGS
An official implementation of "Network Quantization with Element-wise Gradient Scaling" (CVPR 2021) in PyTorch.
funNLP
中英文敏感词、语言检测、中外手机/电话归属地/运营商查询、名字推断性别、手机号抽取、身份证抽取、邮箱抽取、中日文人名库、中文缩写库、拆字词典、词汇情感值、停用词、反动词表、暴恐词表、繁简体转换、英文模拟中文发音、汪峰歌词生成器、职业名称词库、同义词库、反义词库、否定词库、汽车品牌词库、汽车零件词库、连续英文切割、各种中文词向量、公司名字大全、古诗词库、IT词库、财经词库、成语词库、地名词库、历史名人词库、诗词词库、医学词库、饮食词库、法律词库、汽车词库、动物词库、中文聊天语料、中文谣言数据、百度中文问答数据集、句子相似度匹配算法集合、bert资源、文本生成&摘要相关工具、cocoNLP信息抽取工具、国内电话号码正则匹配、清华大学XLORE:中英文跨语言百科知识图谱、清华大学人工智能技术系列报
GNNPapers
Must-read papers on graph neural networks (GNN)
GPTQ-triton
GPTQ inference Triton kernel
LeNet5-MNIST-PyTorch
The simplest implementation of LeNet5 with mnist in PyTorch. Accuracy: ~99%
MMdnn
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
MQBench
Model Quantization Benchmark
onnx
Open standard for machine learning interoperability
optimizer
Actively maintained ONNX Optimizer
overhaul-distillation
Official PyTorch implementation of "A Comprehensive Overhaul of Feature Distillation" (ICCV 2019)
PyTorch-VAE
A Collection of Variational Autoencoders (VAE) in PyTorch.
seaborn
Statistical data visualization in Python
torchdistill
A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆20 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.
Towards-Effective-Low-bitwidth-Convolutional-Neural-Networks
This repository implements the paper "Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations"