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）

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OpenCV 4相关文件

推荐系统入门教程，在线阅读地址：https://datawhalechina.github.io/fun-rec/

⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end…

🛠 A lite C++ toolkit of 100+ Awesome AI models, support ORT, MNN, NCNN, TNN and TensorRT. 🎉🎉

🔥🔥🔥TensorRT for YOLOv8、YOLOv8-Pose、YOLOv8-Seg、YOLOv8-Cls、YOLOv7、YOLOv6、YOLOv5、YOLONAS......🚀🚀🚀CUDA IS ALL YOU NEED.🍎🍎🍎

FewX is an open-source toolbox on top of Detectron2 for data-limited instance-level recognition tasks.

Code for AAAI 2022 Oral paper: 'Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment'

PyTorch implementation of paper "Dataset Distillation via Factorization" in NeurIPS 2022.

Source code for CVPR 2022 paper Sylph A Hypernetwork Framework for Few-shot Object Detection

Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection, ECCV 2022

[T-PAMI 2022] Meta-DETR for Few-Shot Object Detection: Official PyTorch Implementation

The official implementation of Efficient Few-Shot Object Detection via Knowledge Inheritance (TIP 2022)

Running YOLOv5 through web browser using Flask microframework

Official implementation of the CVPR 2022 paper "DETReg: Unsupervised Pretraining with Region Priors for Object Detection".

Companion code to my O'Reilly book "Flask Web Development", second edition.

A pytorch implementation of face detection project

Serve, optimize and scale PyTorch models in production

Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

The official code for the paper: https://openreview.net/forum?id=_PHymLIxuI

CVPR 2022 papers with code (论文及代码）

🌈Python3网络爬虫实战：淘宝、京东、网易云、B站、12306、抖音、笔趣阁、漫画小说下载、音乐电影下载等

CCNet: Criss-Cross Attention for Semantic Segmentation (TPAMI 2020 & ICCV 2019).

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐