whrenstone's starred repositories
Deep-Learning-in-Production
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
Awesome-Incremental-Learning
Awesome Incremental Learning
qcloud-documents
腾讯云官方文档
CVinW_Readings
A collection of papers on the topic of ``Computer Vision in the Wild (CVinW)''
MulimgViewer
MulimgViewer is a multi-image viewer that can open multiple images in one interface, which is convenient for image comparison and image stitching.
pytorch-deform-conv
PyTorch implementation of Deformable Convolution
online-continual-learning
A collection of online continual learning paper implementations and tricks for computer vision in PyTorch, including our ASER(AAAI-21), SCR(CVPR21-W) and an online continual learning survey (Neurocomputing).
MetaFormer
A PyTorch implementation of "MetaFormer: A Unified Meta Framework for Fine-Grained Recognition". A reference PyTorch implementation of “CoAtNet: Marrying Convolution and Attention for All Data Sizes”
CSL_RetinaNet_Tensorflow
Code for ECCV 2020 paper: Arbitrary-Oriented Object Detection with Circular Smooth Label
deep-geometric-prior
The reference implementaiton for the paper "Deep Geometric Prior for Surface Reconstruction"
StreamingCNN
To train deep convolutional neural networks, the input data and the activations need to be kept in memory. Given the limited memory available in current GPUs, this limits the maximum dimensions of the input data. Here we demonstrate a method to train convolutional neural networks while holding only parts of the image in memory.
deblur-pmp
Blind Image Deblurring Using Patch-Wise Minimal Pixels Regularization
astro_rcnn
Astro R-CNN: Instance Segmentation in Astronomical Images using Mask R-CNN Deep Learning
concrete_crack_detection
Concrete crack detection for structural inspection.
deep-active-learning-for-joint-classification-and-segmentation-with-weak-annotator
Pytorch code for the paper "Deep Active Learning for Joint Classification and Segmentation with Weak Annotator"
incremental-learning-image-classification
Replication of existing baselines that address incremental learning issues and definition of new approaches to overcome existing limitations