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Beijing Unervisity of Posts and Telecommunications
- Haidian, Beijing
Stars
这是一份入门AI/LLM大模型的逐步指南,包含教程和演示代码,带你从API走进本地大模型部署和微调,代码文件会提供Kaggle或Colab在线版本,即便没有显卡也可以进行学习。项目中还开设了一个小型的代码游乐场🎡,你可以尝试在里面实验一些有意思的AI脚本。同时,包含李宏毅 (HUNG-YI LEE)2024生成式人工智能导论课程的完整中文镜像作业。
[Pytorch] The repo contains the code for "FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets"
GRID: Generative Recommendation with Semantic IDs
Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
Official implementation of MedCLIP-SAM (MICCAI 2024)
BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks
A collection of resources on applications of multi-modal learning in medical imaging.
Notebooks for fine-tuning and running of PubMedBERT-based classification models, used in the ANDDigest tool
Official code of "EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model"
雅意大模型:为客户打造安全可靠的专属大模型,基于大规模中英文多领域指令数据训练的 LlaMA 2 & BLOOM 系列模型,由中科闻歌算法团队研发。(Repo for YaYi Chinese LLMs based on LlaMA2 & BLOOM)
主要记录大语言大模型(LLMs) 算法(应用)工程师相关的知识及面试题
Tuning LLMs with no tears💦; Sample Design Engineering (SDE) for more efficient downstream-tuning.
List of Tech Company OAs. Save your time from finding them all over the internet.
Polyp-SAM++ is the first text-guided polyp-segmentation method using segment anything model (SAM).
A lightweight adapter bridges SAM with medical imaging [MedIA]
[MICCAI 2021] You Only Learn Once: Universal Anatomical Landmark Detection https://arxiv.org/abs/2103.04657
润学全球官方指定GITHUB,整理润学宗旨、纲领、理论和各类润之实例;解决为什么润,润去哪里,怎么润三大问题; 并成为新中国人的核心宗教,核心信念。
CL-Detection2023 Challenge Official Repository
Replication of simple CV Projects including attention, classification, detection, keypoint detection, etc.
2D keypoint detection with Pytorch Lightning and wandb
Four landmark detection algorithms, implemented in PyTorch.
The implementation of the technical report: "Customized Segment Anything Model for Medical Image Segmentation"