Wenjun Wang's repositories
awesome-chatgpt-prompts-zh
ChatGPT 中文调教指南。各种场景使用指南。学习怎么让它听你的话。
PaddleTS
PaddleTS - PaddlePaddle-based Time Series Modeling in Python
pumpkin-book
《机器学习》（西瓜书）公式详解
ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
machine-learning-toy-code
《机器学习》（西瓜书）代码实战
sliding-window
sliding window
paper-reading
深度学习经典、新论文逐段精读
structural_inspection_main
Collection of works in my PhD at Virginia Tech, mostly focused on computer vision and machine learning applications in structural inspection and structural health monitoring.
Awesome-Knowledge-Distillation-1
Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。
awesome-knowledge-distillation
Awesome Knowledge Distillation
flexible-yolov5
More readable and flexible yolov5 with more backbone(resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer) and (cbam，dcn and so on), and tensorrt
ml-visuals
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
RepDistiller
[ICLR 2020] Contrastive Representation Distillation (CRD), and benchmark of recent knowledge distillation methods
ImageStitching
Conducts image stitching upon an input video to generate a panorama in 3D
surface-distance
Library to compute surface distance based performance metrics for segmentation tasks.
Stock-Prediction-Models
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
crack_detection_CNN_masonry
This GitHub Repository was produced to share material relevant to the Journal paper "Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning" by D. Dais, İ. E. Bal, E. Smyrou, and V. Sarhosis published in "Automation in Construction".
mmdetection2.0_visualize
It is a simple tool based on mmdetection2.0. The tool is used for visualize test result produced by mmdetection 2.0, including drawing PR curves and loss curves.（PR曲线绘制，loss曲线绘制）
HDCB-Net
HDCB-Net
SE_DenseNet
This is a SE_DenseNet which contains a senet (Squeeze-and-Excitation Networks by Jie Hu, Li Shen, and Gang Sun) module, written in Pytorch, train, and eval codes have been released.
mmdetection_visualize
visualize training result for mmdetection 訓練文件可視化, PR curve绘制， F1-score计算
motion-flow-syn
Motion flow and corresponding blurry image synthesis. Training data generation for "From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur (CVPR'17)".