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‎docs/_notebooks/EfficientNet-example.html

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<img alt="5c32311a1dc64c5a9b9ecb54622d17fb" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
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<img alt="e966b9f25ef4423fb0102a7744f4394a" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
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<section id="Torch-TensorRT-Getting-Started---EfficientNet-B0">
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<h1 id="notebooks-efficientnet-example--page-root">

‎docs/_notebooks/Resnet50-example.html

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<img alt="cd5503bf7d124883803fcea639636073" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
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<img alt="05abea58b6f34fe9b9e8f56ea1c3cd11" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
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<span></span>!pip install numpy==1.21.2 scipy==1.5.2 Pillow==6.2.0 scikit-image==0.17.2 matplotlib==3.3.0
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!nvidia-smi
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!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org
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‎docs/_notebooks/Resnet50-example.ipynb

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"!pip install numpy==1.21.2 scipy==1.5.2 Pillow==6.2.0 scikit-image==0.17.2 matplotlib==3.3.0\n",
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"name": "python",
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‎docs/_notebooks/lenet-getting-started.html

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<img alt="a09533d2854a4a4fac78bf5daa624438" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
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<img alt="615ad33b718745f3b3b45ac8062bc599" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
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to prepare a Docker container, within which you can run this notebook.
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<pre><span></span>[ ]:
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<span></span>!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org
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## 2. Creating TorchScript modules
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‎docs/_notebooks/lenet-getting-started.ipynb

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‎docs/_notebooks/ssd-object-detection-demo.html

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<span></span># Known working versions
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!pip install numpy==1.21.2 scipy==1.5.2 Pillow==6.2.0 scikit-image==0.17.2 matplotlib==3.3.0
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‎docs/_notebooks/ssd-object-detection-demo.ipynb

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"!pip install numpy==1.21.2 scipy==1.5.2 Pillow==6.2.0 scikit-image==0.17.2 matplotlib==3.3.0\n",
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‎docs/_notebooks/vgg-qat.html

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## 1. Requirements Please install the required dependencies and import these libraries accordingly
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## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows
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<img alt="d52cf8532e934d3b81a57cb0cd58a6c7" src="https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png"/>
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## 3. Training a baseline VGG16 model We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your

‎docs/_notebooks/vgg-qat.ipynb

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"Please install the <a href=\"https://github.com/NVIDIA/Torch-TensorRT/tree/master/examples/int8/training/vgg16#prequisites\">required dependencies</a> and import these libraries accordingly"
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‎docs/_sources/_notebooks/Resnet50-example.ipynb.txt

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‎docs/_sources/_notebooks/lenet-getting-started.ipynb.txt

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‎docs/_sources/_notebooks/ssd-object-detection-demo.ipynb.txt

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‎docs/_sources/_notebooks/vgg-qat.ipynb.txt

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‎docs/py_api/ts.html

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‎docs/searchindex.js

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