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
A small speech recognizer
- speech-recognition
- python
- c
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
Seamlessly integrate LLMs into scikit-learn.
Nx-powered Neural Networks
- nx
- elixir
- deep-learning
- neural-networks
1M param OCR model - runs on CPU, 15x faster than tesseract, close to 1B+ param models in accuracy
A clinical decision-evidence benchmark: pull the one fact an answer rests on, and see whether the assistant's action moves with the evidence. Built on HealthBench.
Dataflow compiler for QNN inference on FPGAs
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