Popular GitHub Repositories for Tensorflow Lite

Visualizer for neural network, deep learning and machine learning models

The challenge projects for Inferencing machine learning models on iOS

TensorFlow Lite Samples on Unity

? High-performance TensorFlow Lite library for React Native with GPU acceleration

Self-Created Tools to convert ONNX files (NCHW) to TensorFlow/TFLite/Keras format (NHWC). The purpose of this tool is to solve the massive Transpose extrapolation problem in onnx-tensorflow (onnx-tf). I don't need a Star, but give me a pull request.

Android TensorFlow Lite Machine Learning Example

The Qualcomm? AI Hub Models are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm? devices.

GPU accelerated deep learning inference applications for RaspberryPi / JetsonNano / Linux PC using TensorflowLite GPUDelegate / TensorRT

DistilBERT / GPT-2 for on-device inference thanks to TensorFlow Lite with Android demo apps

Sample projects for TensorFlow Lite in C++ with delegates such as GPU, EdgeTPU, XNNPACK, NNAPI

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.

Go binding for TensorFlow Lite

Pytorch to Keras/Tensorflow/TFLite conversion made intuitive

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.

物体検出を用いてNARUTOの印(子～亥、壬、合掌)を検出するモデルとサンプルプログラムです。このリポジトリでは、YOLOXを使用しています(This is a model and sample program that detects NARUTO's hand sign using object detection. This repository use YOLOX.)

MNIST with TensorFlow Lite on Android

Emgu TF is a cross platform .Net wrapper for the Google Tensorflow library. Allows Tensorflow functions to be called from .NET compatible languages such as C#, VB, VC++, IronPython.

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

The Qualcomm? AI Hub apps are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm? devices.

A workout trainer Dash/Flask app that helps track your HIIT workouts by analyzing real-time video streaming from your sweet Pi using machine learning and Edge TPU..