Coreml Python Packages

catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

coremltools

Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

onnx2tf

A tool for converting ONNX files to LiteRT/TFLite/TensorFlow, PyTorch native code (nn.Module), TorchScript (.pt), state_dict (.pt), Exported Program (.pt2), and Dynamo ONNX. It also supports direct conversion from LiteRT to PyTorch.

netron

Visualizer for neural network, deep learning and machine learning models

catboost-dev

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

tflite2tensorflow

Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite, ONNX, OpenVINO, Myriad Inference Engine blob and .pb from .tflite. Support for building environments with Docker. It is possible to directly access the host PC GUI and the camera to verify the operation. NVIDIA GPU (dGPU) support. Intel iHD GPU (iGPU) support. Supports inverse quantization of INT8 quantization model.

openvino2tensorflow

This script converts the ONNX/OpenVINO IR model to Tensorflow's saved_model, tflite, h5, tfjs, tftrt(TensorRT), CoreML, EdgeTPU, ONNX and pb. PyTorch (NCHW) -> ONNX (NCHW) -> OpenVINO (NCHW) -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TFLite (NHWC/NCHW). And the conversion from .pb to saved_model and from saved_model to .pb and from .pb to .tflite and saved_model to .tflite and saved_model to onnx. Support for building environments with Docker. It is possible to directly access the host PC GUI and the camera to verify the operation. NVIDIA GPU (dGPU) support. Intel iHD GPU (iGPU) support.

ultralytics-yolov5

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

yolov5-inference

code de inference yolov5

deepvac

PyTorch Project Specification.

mlbuild

Performance CI/CD for on-device ML models — catch inference regressions before they ship

phostt

Local Vietnamese STT server powered by Zipformer-vi RNN-T

mesh-cognition

Python coupling kernel for Mesh Cognition — per-field admission and state blending for CfC neural networks. Canonical home: meshcognition.org

coreml-pytorch-utils

Coreml utils that helps export pytorch based models

torch2coreml

Torch7 -> CoreML

mmdnn

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.

ml2rt

Machine learning utilities for model conversion, serialization, loading etc

pipersr

First super-resolution model designed for Apple Neural Engine. 2x upscale, real-time, on-device. Built by Ben Racicot.

runlocal-hub

Python client for benchmarking and validating ML models on real devices via RunLocal API.

neuron-ml

A tiny and very high level transfer learning library for image classification 📚

catboost-no-sse4

CatBoost Python Package

langchain-mesh-cognition

LangChain integration for Mesh Cognition — give any agent distributed memory across devices

wnetron

Visualizer for neural network, deep learning and machine learning models