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Discover 1,591 open source projects
Showing 81-100 of 1,591 projects
junyanz/pytorch-CycleGAN-and-pix2pix
PyTorch implementation of CycleGAN and pix2pix for image-to-image translation
HumanSignal/labelImg
Image annotation tool for computer vision projects
fastai/fastbook
Deep learning book with Jupyter Notebooks covering fastai and PyTorch
bytedance/deer-flow
DeerFlow is a super agent harness for orchestrating sub-agents, memory, and sandboxes in deep research workflows.
microsoft/JARVIS
JARVIS connects LLMs with ML community for task automation and research.
HarisIqbal88/PlotNeuralNet
Generates neural network diagrams in LaTeX for reports and presentations
Anjok07/ultimatevocalremovergui
GUI for vocal removal using deep learning models
PaddlePaddle/Paddle
PaddlePaddle is a deep learning framework for industrial-scale ML, offering distributed training, model deployment, and Python/C++ support.
pyg-team/pytorch_geometric
Graph Neural Network library for PyTorch
lukasmasuch/best-of-ml-python
Curated list of top machine learning Python libraries, updated weekly with quality scores.
facebookresearch/audiocraft
AudioCraft is a PyTorch library for audio generation with deep learning models like MusicGen and AudioGen.
Tencent/ncnn
High-performance mobile-optimized neural network inference framework for deploying AI models on mobile devices
spmallick/learnopencv
Learn OpenCV with C++ and Python examples for computer vision and AI
Dao-AILab/flash-attention
Optimized attention mechanism for deep learning
serengil/deepface
DeepFace is a lightweight Python library for face recognition and facial attribute analysis, including age, gender, emotion, and race detection.
MLEveryday/100-Days-Of-ML-Code
机器学习100天学习计划，涵盖有监督和无监督学习算法
harvard-edge/cs249r_book
ML Systems textbook and hands-on learning stack for building real-world AI systems
amusi/CVPR2026-Papers-with-Code
CVPR 2025 论文和开源项目合集
jina-ai/serve
Build and deploy AI services with cloud-native stack
zergtant/pytorch-handbook
PyTorch Handbook is a comprehensive guide for learning and using PyTorch in deep learning, with tested tutorials and examples.
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