Github reverses dmca takedown code anime3/16/2023 ![]() ![]() Obtained by executing the code with ‘Runtime->Run All’ or the Ctrl+F9 The flower_ir.bin and flower_ir.xml (pre-trained models) can be The training code is based on the official TensorFlow Image Notebook, check out the Post-Training Quantization with TensorFlow For faster inference speed on the model created in this Notebook shows the process where we perform the inference step on theįreshly trained model that is converted to OpenVINO IR with Model This tutorial demonstrates how to train, convert, and deploy an imageĬlassification model with TensorFlow and OpenVINO. # Copyright 2018 The TensorFlow Authors # Modified for OpenVINO Notebooks ![]() # See the License for the specific language governing permissions and # limitations under the License. # You may obtain a copy of the License at # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # Licensed under the Apache License, Version 2.0 (the "License") # you may not use this file except in compliance with the License. Quantization Aware Training with NNCF, using TensorFlow Framework Post-Training Quantization with TensorFlow Classification Modelįrom Training to Deployment with TensorFlow and OpenVINO Quantization Aware Training with NNCF, using PyTorch framework ![]() Style Transfer on ONNX Models with OpenVINO Live Inference and Benchmark CT-scan Data with OpenVINO Image Background Removal with U^2-Net and OpenVINO Photos to Anime with PaddleGAN and OpenVINO Super Resolution with PaddleGAN and OpenVINO Single Image Super Resolution with OpenVINO Optical Character Recognition (OCR) with OpenVINO Quantize a Segmentation Model and Show Live Inference Quantize NLP models with OpenVINO Post-Training Optimization Tool Ĭonvert a PaddlePaddle Model to ONNX and OpenVINO IR Post-Training Quantization of PyTorch models with NNCFĬonvert a PyTorch Model to ONNX and OpenVINO IR ![]()
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