PyTorch to TensorFlow Lite converter
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Updated
Jul 30, 2024 - Python
PyTorch to TensorFlow Lite converter
Edge TPU Accelerator / Multi-TPU + MobileNet-SSD v2 + Python + Async + LattePandaAlpha/RaspberryPi3/LaptopPC
Implementation of UNet by Tensorflow Lite. Semantic segmentation without using GPU with RaspberryPi + Python. In order to maximize the learning efficiency of the model, this learns only the "Person" class of VOC2012. And Comparison with ENet.
An end-to-end speech recognition system with Wavenet. Built using C++ and python.
run YOLO (object detection model) on various frameworks
A lightweight Python package optimized for integrating exported models from Google's Teachable Machine Platform into robotics and embedded systems environments. This streamlined version of Teachable Machine Package is specifically designed for resource-constrained devices, making it easier to deploy and use your trained models in embedded apps.
MNIST Live Detection using OpenCV, Tensorflow Lite and AutoKeras
Quantization (post-training quantization) your (custom mobilenet_v2) models .h5 or .pb models using TensorFlow Lite 2.4
This is a series of demolition repositories that use the Tensorflow.js Task API to perform browser-side inference on object detection models created with Tensorflow Lite Model Maker.
Compile and run the model for the Edge TPU
Simple image classification (persons, animals, other) on raspberry pi used custom model tflite (output to terminal) dividing image into 4 parts using OpenCV and TensorFlow Lite
Object-Detection-with-Voice-Feedback-using-Raspberry-Pi-4-and-Bullseye-OS
Simple image classification (persons, animals, other) on raspberry pi used custom model tflite (output to terminal)
Bilgisayar Görüntüsü (Computer Vision) ve Evrişimsel Sinir Ağları (CNN) kullanarak parça ayrımı yapabilmek. Tensorflo Lite Model ve Nesne Algılama (Object Detection) Modelleri de içermektedir.
Image classification (persons, animals, other) on raspberry pi from pi-camera (with cycle while) used custom tflite model (output to terminal)
Detailed guide on how to train model using Python TensorFlow and the training data. Also has code to split, train, test and convert the .h5/.keras to .tflite
WellnessWise_ml model is neural network machine learning model trained for Providing health risk for diseases like Cardiovascular, Hypertension, cancer, Diabetes, and obesity. There are two types of model one for h5, other is in tensor flow lite formate for Mobile phone integration and low powered device's.
The backend of a chatbot system was developed using NLP techniques to help students to present their concerns about university works & get solutions in real time
Senior Design - Computer Vision Enabled Automatic Pooper Scooper
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