Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
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Visualizer for neural network, deep learning and machine learning models
ncnn is a high-performance neural network inference framework optimized for the mobile platform
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Open standard for machine learning interoperability
Fast and accurate AI powered file content types detection
- deep-learning
- filetype
- keras-classification-models
- keras-models
Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
- autodiff
- deep-learning
- machine-learning
- rust
Speech-to-text, text-to-speech, speaker diarization, speech enhancement, source separation, and VAD using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, HarmonyOS, Raspberry Pi, RISC-V, RK NPU, Axera NPU, Ascend NPU, x86_64 servers, websocket server/client, support 12 programming languages
Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learn...
- java
- gpu
- deeplearning
- neural-nets
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
- bert
- sentence-encoding
- deep-learning
- clip-model
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
- yolox
- yolov3
- onnx
- tensorrt
PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
- cntk
- visualization
- tensorflow
- model-converter
Simple and Distributed Machine Learning Python Library porting ML algorithms for Spark
State of the Art Natural Language Processing
Open-source library of optimized deep learning operations (matmul, convolution, attention) for CPUs (x64, AArch64, RISC-V) and Intel GPUs. Used by PyTorch, TensorFlow, OpenVINO, and ONNX Runtime.
- onednn
- oneapi
- performance
- cpp
1M param OCR model - runs on CPU, 15x faster than tesseract, close to 1B+ param models in accuracy
onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library.
- go
- onnx
- neural-network
- open-source
PyTorch to Keras model convertor
- pytorch
- keras-model-convertor
- keras
- tensorflow
Onnx transformer pipelines in Golang
Laya typed decisions on Linux x86_64 via ONNX Runtime
A distributed Spark/Scala implementation of the isolation forest and extended isolation forest algorithms for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.
- isolation-forest
- spark
- outlier-detection
Your users download a 4GB AI model, the connection drops at 3.8GB, verifyfetch resumes from 3.8GB and verifies every byte. Drop-in integrity verification for Transformers.js, WebLLM, and any large file in the browser.
- ai-models
- browser
- fetch
- integrity
Push-to-talk voice dictation for Linux. 100% local, multilingual (25+ languages), with speaker diarization. Qt frontend, Rust backend on NVIDIA Parakeet via ONNX Runtime. KDE Plasmoid integrated.
- asr
- linux
- nvidia
- parakeet