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.

Seeed SenseCraft Model Assistant is an open-source project focused on embedded AI. 🔥🔥🔥

虚拟爱抖露(アイドル)共享计划, 是基于单目RGB摄像头的人眼与人脸特征点检测算法, 在实时3D面部捕捉以及模型驱动领域的应用.

Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX

🚀 Accelerate training and inference of 🤗 Transformers and 🤗 Diffusers with easy to use hardware optimization tools

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

Go binding for TensorFlow Lite

⚡ TensorFlowASR: Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2. Supported languages that can use characters or subwords

Recyclr is a revolutionary waste management app. It uses advanced Object Detection to identify and classify 91 waste items in real-time, then notifies nearby agencies for automatic pickup with precise geolocation. ♻️🗑️📍

Custom delegate for CNN applications

A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.

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

Bharat Leaf Lens is your pocket guide to identifying medicinal plants in India. No user images are stored in our database, ensuring privacy.

Accelerate object detection on your Raspberry Pi 5 with the Coral Edge TPU! This project leverages PyCoral's optimized TensorFlow Lite API and a FastAPI server for high-performance, real-time object recognition

Highly optimized inference engine for Binarized Neural Networks

Android and iOS .NET ports of TensorFlow Lite classification examples:

Want a faster ML processor? Do it yourself! -- A framework for playing with custom opcodes to accelerate TensorFlow Lite for Microcontrollers (TFLM). . . . . . Online tutorial: https://google.github.io/CFU-Playground/ For reference docs, see the link below.

Quantization of Models : Post-Training Quantization(PTQ) and Quantize Aware Training(QAT)

Deep Learning Inference benchmark. Supports OpenVINO™ toolkit, Caffe, TensorFlow, TensorFlow Lite, ONNX Runtime, OpenCV DNN, MXNet, PyTorch, Apache TVM, ncnn, etc.