Easy benchmarking of all publicly accessible implementations of convnets

PyTorch Tutorial for Deep Learning Researchers

official pytorch implementation of Tobias: A random CNN sees objects and its applications in self-supervised learning.

Pytorch Implement of Dynamic Snake Convolution (ICCV2023)

利用Swin-Unet(Swin Transformer Unet)实现对文档图片里表格结构的识别，Swin-unet (Swin Transformer Unet) is used to identify the document table structure

Table Structure Recognition

The ICDAR 2019 cTDaR is to evaluate the performance of methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables a…

Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"

The official code for "DaFIR: Distortion-Aware Representation Learning for Fisheye Image Rectification", TCSVT, 2023.

This is Low-light Object Detection (LOD) dataset of Crafting Object Detection in Very Low Light in BMVC(British Machine Vision Conference) 2021, by Yang Hong, Kaixuan Wei, Linwei Chen, and Ying Fu.

an edge-real-time anchor-free object detector with decent performance

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM , Smooth Grad-CAM , Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

Hiera: A fast, powerful, and simple hierarchical vision transformer.

深度学习的好用结构，可以帮忙设计相关结构，欢迎在问题中留言

Minimal Mamba-2 implementation in PyTorch

Only implemented through torch: "bi - mamba2" , "vision- mamba2 -torch". support 1d/2d/3d/nd and support export by jit.script/onnx;

Collection of LaTeX templates

Unofficial implementation of "TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images"

TableBank: A Benchmark Dataset for Table Detection and Recognition

Latex code for making neural networks diagrams

Edge YOLO paper

标注自己的数据集，训练、评估、测试、部署自己的人工智能算法

A Supervised and Semi-Supervised Object Detection Library for YOLO Series

This is an official implementation for "AutoFocusFormer: Image Segmentation off the Grid".

Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.

Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs (CVPR 2022)