🚀 Accelerate training and inference of 🤗 Transformers and 🤗 Diffusers with easy to use hardware optimization tools
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Updated
Jun 3, 2024 - Python
🚀 Accelerate training and inference of 🤗 Transformers and 🤗 Diffusers with easy to use hardware optimization tools
Use AnimeGANv3 to make your own animation works, including turning photos or videos into anime.
🔥 High-performance TensorFlow Lite library for React Native with GPU acceleration
Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
Seeed SenseCraft Model Assistant is an open-source project focused on embedded AI. 🔥🔥🔥
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.
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.
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. ♻️🗑️📍
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.
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
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)
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