Pinned Repositories
AnimateDiff
Official implementation of AnimateDiff.
annotated_deep_learning_paper_implementations
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
anylabeling
Effortless AI-assisted data labeling with AI support from Segment Anything and YOLO!
Awesome-AIGC
AIGC资料汇总学习，持续更新......
Awesome-ChatGPT-with-AI
Some materials collected during personal study of AI LLM such as ChatGPT, including a series of prompts with good performance on the internet. 个人学习 chatGPT 等 AI 大模型过程中收集的资料
CMake
Mirror of CMake upstream repository
Co-DETR
[ICCV 2023] DETRs with Collaborative Hybrid Assignments Training
CppCCQTemplate
A C++ Conan CMake Qt6 Project Template
MindMapRa
Simple mindmapping tool on Qt
Open3D
Open3D: A Modern Library for 3D Data Processing
hankhaohao's Repositories
hankhaohao/AnimateDiff
Official implementation of AnimateDiff.
hankhaohao/annotated_deep_learning_paper_implementations
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
hankhaohao/cutlass
CUDA Templates for Linear Algebra Subroutines
hankhaohao/D-FINE
D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement 💥💥💥
hankhaohao/EfficientSAM
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
hankhaohao/FasterTransformer
Transformer related optimization, including BERT, GPT
hankhaohao/GLM-4
GLM-4 series: Open Multilingual Multimodal Chat LMs | 开源多语言多模态对话模型
hankhaohao/grok-1
Grok open release
hankhaohao/IndustrialSoftwareOfQt
基于qt的工业软件收录，为工业软件开发提供参考
hankhaohao/line_profiler
Line-by-line profiling for Python
hankhaohao/llama.cpp
LLM inference in C/C++
hankhaohao/llm.c
LLM training in simple, raw C/CUDA
hankhaohao/machinelearning2
My blogs and code for machine learning. http://cnblogs.com/pinard
hankhaohao/MambaMorph
MambaMorph: a Mamba-based Backbone with Contrastive Feature Learning for Deformable MR-CT Registration
hankhaohao/memory_profiler
Monitor Memory usage of Python code
hankhaohao/mistral-common
hankhaohao/mmdetection
OpenMMLab Detection Toolbox and Benchmark
hankhaohao/mmpose
OpenMMLab Pose Estimation Toolbox and Benchmark.
hankhaohao/mmrotate
OpenMMLab Rotated Object Detection Toolbox and Benchmark
hankhaohao/nanodet
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
hankhaohao/nanoGPT
The simplest, fastest repository for training/finetuning medium-sized GPTs.
hankhaohao/OpenOCR
OpenOCR: A general OCR system with accuracy and efficiency. Supporting 24 Scene Text Recognition methods trained from scratch on large-scale real datasets, and will continue to add the latest methods.
hankhaohao/protobuf
Protocol Buffers - Google's data interchange format
hankhaohao/pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
hankhaohao/Swin-Transformer
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
hankhaohao/T-Rex
[ECCV2024] API code for T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy
hankhaohao/TensorRT-Alpha
🔥🔥🔥TensorRT for YOLOv8、YOLOv8-Pose、YOLOv8-Seg、YOLOv8-Cls、YOLOv7、YOLOv6、YOLOv5、YOLONAS......🚀🚀🚀CUDA IS ALL YOU NEED.🍎🍎🍎
hankhaohao/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
hankhaohao/vision_transformer
hankhaohao/wesam
[CVPR 2024] Code for "Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation"