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assets/hub/datvuthanh_hybridnets.ipynb

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"cells": [
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{
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"cell_type": "markdown",
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"id": "235429a5",
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"id": "b550807b",
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"metadata": {},
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"source": [
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"### This notebook is optionally accelerated with a GPU runtime.\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "20d680a9",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "markdown",
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"id": "4f8a31f6",
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"id": "63c6ecd3",
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"metadata": {},
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"source": [
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"## Model Description\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8dec92bc",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "markdown",
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"id": "c9151eb0",
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"metadata": {},
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"### Citation\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f5cd558d",
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"metadata": {
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"classes": [

assets/hub/facebookresearch_WSL-Images_resnext.ipynb

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"cells": [
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"id": "cca24186",
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"metadata": {},
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"### This notebook is optionally accelerated with a GPU runtime.\n",
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{
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"cell_type": "code",
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"execution_count": null,
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},
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{
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"cell_type": "markdown",
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"id": "3e953f96",
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"id": "f26e273c",
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"metadata": {},
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"source": [
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"All pre-trained models expect input images normalized in the same way,\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "59b71a55",
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"metadata": {},
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"outputs": [],
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "49f747b0",
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"metadata": {},
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},
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{
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"cell_type": "markdown",
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"id": "6a8a1ffe",
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"metadata": {},
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"### Model Description\n",

assets/hub/facebookresearch_pytorch-gan-zoo_dcgan.ipynb

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"cells": [
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"id": "190c485e",
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"id": "b58a8056",
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"metadata": {},
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"### This notebook is optionally accelerated with a GPU runtime.\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "52e11fa7",
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"metadata": {},
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},
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{
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"cell_type": "markdown",
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"id": "d3541c45",
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"The input to the model is a noise vector of shape `(N, 120)` where `N` is the number of images to be generated.\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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},
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{
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"cell_type": "markdown",
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"id": "61a5ffb2",
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"You should see an image similar to the one on the left.\n",

assets/hub/facebookresearch_pytorch-gan-zoo_pgan.ipynb

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"cells": [
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"metadata": {},
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"### This notebook is optionally accelerated with a GPU runtime.\n",
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{
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"cell_type": "code",
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"execution_count": null,
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"The input to the model is a noise vector of shape `(N, 512)` where `N` is the number of images to be generated.\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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},
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{
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"cell_type": "markdown",
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"id": "8536a852",
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"metadata": {},
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"You should see an image similar to the one on the left.\n",

assets/hub/facebookresearch_pytorchvideo_resnet.ipynb

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"cells": [
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"id": "61b459fe",
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"metadata": {},
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"# 3D ResNet\n",
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{
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},
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{
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"cell_type": "markdown",
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"Import remaining functions:"
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{
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},
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"#### Setup\n",
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"Download the id to label mapping for the Kinetics 400 dataset on which the torch hub models were trained. This will be used to get the category label names from the predicted class ids."
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{
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"cell_type": "code",
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},
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"#### Define input transform"
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"#### Run Inference\n",
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},
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"Load the video and transform it to the input format required by the model."
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},
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"#### Get Predictions"
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{
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"### Model Description\n",

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