whrenstone

mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

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

BEVFormer

[ECCV 2022] This is the official implementation of BEVFormer, a camera-only framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation.

qcloud-documents

腾讯云官方文档

mmrotate

OpenMMLab Rotated Object Detection Toolbox and Benchmark

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

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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).

ParC-Net

[ECCV 2022] Source code of "EdgeFormer: Improving Light-weight ConvNets by Learning from Vision Transformers"

SuperYOLO

SuperYOLO is accepted by TGRS

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

FGVC-PIM

Pytorch implementation for "A Novel Plug-in Module for Fine-Grained Visual Classification". fine-grained visual classification task.

poet

ML model training for edge devices

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

WaveMix

2D discrete Wavelet Transform for Image Classification and Segmentation

MS-DAYOLO

Multiscale Domain Adaptive YOLO for Cross-Domain Object Detection

astro_rcnn

Astro R-CNN: Instance Segmentation in Astronomical Images using Mask R-CNN Deep Learning

parkweb

停车场收费系统

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

GF-CSL

SRFeat

SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence (3DV 2022)

ABECIS

A Free and Open-source Automated Building Exterior Crack Inspection Software for Construction and Facility Managers