zhengweisrc,zhengwei,github

awesome-automl-and-lightweight-models

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

awesome-object-detection

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

cardetectionexample

Sinovation Ventures Challenge

codecraft2020

codecraft2020_huawei2020_toy

2020华为软件精英挑战赛热身赛代码-0.032

craft-pytorch

Official implementation of Character Region Awareness for Text Detection (CRAFT)

craft-reimplementation

CRAFT-Pyotorch：Character Region Awareness for Text Detection Reimplementation for Pytorch

cs231n-convolutional-neural-networks-solutions

Assignment solutions for the CS231n course taught by Stanford on visual recognition. Spring 2017 solutions are for both deep learning frameworks: TensorFlow and PyTorch.

deep-learning-interview-book

深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）

deep-learning-with-tensorflow-book

深度学习开源书，基于TensorFlow 2.0实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.

deeplearningbook-chinese

Deep Learning Book Chinese Translation

detcomp

dynaslam

基于ORB-SLAM2修改动态环境建模 dynamic environments for monocular, stereo and RGB-D setups

efficientdet

EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow

efficientdet.pytorch

Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch

g2opy

Python binding of SLAM graph optimization framework g2o

grad-cam.pytorch

pytorch实现Grad-CAM和Grad-CAM++,可以可视化任意分类网络的Class Activation Map (CAM)图,包括自定义的网络;同时也实现了目标检测faster r-cnn和retinanet两个网络的CAM图;欢迎试用、关注并反馈问题...

hrnet-semantic-segmentation

The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919