Mango AI's Projects
AI算法岗求职攻略(涵盖准备攻略、刷题指南、内推和AI公司清单等资料)
Model code base for computer vision
deeplearning.ai(吴恩达老师的深度学习课程笔记及资源)
Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution,
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
🥇掘金翻译计划,可能是世界最大最好的英译中技术社区,最懂读者和译者的翻译平台:
ICCV2021/2019/2017 论文/代码/解读/直播合集,极市团队整理
iscyy
LeetCode Solutions: A Record of My Problem Solving Journey.( leetcode题解,记录自己的leetcode解题之路。)
LeetCode 力扣的 JavaScript 解题仓库,前端刷题路线(思维导图)。小伙伴们可以在Issues中提交自己的解题代码,🤝 欢迎Contributing,可打卡刷题,Give a ⭐️ if this project helped you!
LeetCode 刷题攻略:200道经典题目刷题顺序,共60w字的详细图解,视频难点剖析,50余张思维导图,支持C++,Java,Python,Go,JavaScript等多语言版本,从此算法学习不再迷茫!🔥🔥 来看看,你会发现相见恨晚!🚀
OpenMMLab Detection Toolbox and Benchmark
Multispectral Object Detection with Yolov5 and Transformer
Config files for my GitHub profile.
PyTorch implementation of YOLOv4
Scaled-YOLOv4: Scaling Cross Stage Partial Network
NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
🔥🔥🔥专注于改进YOLOv8模型,NEW - YOLOv8 🚀 RT-DETR 🥇 in PyTorch >, Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀
🔥🔥🔥 专注于YOLOv5,YOLOv7、YOLOv8、YOLOv9改进模型,Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀
☁️💡🎈专注于改进YOLOv7,Support to improve Backbone, Neck, Head, Loss, IoU, NMS and other modules
implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
YOLOv5 Series Multi-backbone(TPH-YOLOv5, Ghostnet, ShuffleNetv2, Mobilenetv3Small, EfficientNetLite, PP-LCNet, SwinTransformer YOLO), Module(CBAM, DCN), Pruning (EagleEye, Network Slimming), Quantization (MQBench) and Deployment (TensorRT, ncnn) Compression Tool Box.
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
🎈🎈🎈专注于改进YOLOv9模型,Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀