The convertor/conversion of deep learning models for different deep learning frameworks/softwares.

A clear, concise, simple yet powerful and efficient API for deep learning.

Infrastructures™ for Machine Learning Training/Inference in Production.

A lightweight deep learning library

One-Stop System for Machine Learning.

A deep learning framework created from scratch with Python and NumPy

Deep Learning Library. For education. Based on pure Numpy. Support CNN, RNN, LSTM, GRU etc.

Use Jax functions in Pytorch

benchmark for embededded-ai deep learning inference engines, such as NCNN / TNN / MNN / TensorFlow Lite etc.

[Experimental] Graph and Tensor Abstraction for Deep Learning all in Common Lisp

以jax为后端的类似keras的框架

[MICCAI'24] Official implementation of "BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection".

[CVPR 2024] CFAT: Unleashing Triangular Windows for Image Super-resolution

implementation of WSAE-LSTM model as defined by Bao, Yue, Rao (2017)

NumPy实现类PyTorch的动态计算图和神经网络框架(MLP, CNN, RNN, Transformer)

[IMAVIS] Official implementation of "ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation".

A visual Deep Learning Framework for the Web - Built with WebGPU, Next.js and ReactFlow.

Deep Learning framework in C++/CUDA that supports symbolic/automatic differentiation, dynamic computation graphs, tensor/matrix operations accelerated by GPU and implementations of various state-of-the-art graph neural networks and other Machine Learning models including Covariant Compositional Networks For Learning Graphs [Risi et al]

Explore the latest AI Agent Framework!

Flow-based data pre-processing for deep learning