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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": "2cb13c50",
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"id": "5ef1d2fa",
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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": "f15bf5e2",
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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": "2cb1eb56",
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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": "a7ab72c3",
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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": "a4c94a0d",
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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": "c3e85e12",
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"id": "109bf351",
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"metadata": {
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"attributes": {
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"classes": [

assets/hub/facebookresearch_WSL-Images_resnext.ipynb

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"cells": [
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"id": "c2ef6550",
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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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"metadata": {},
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},
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{
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"cell_type": "markdown",
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"id": "84db86dc",
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"id": "53e297d7",
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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": "a84b1f62",
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"id": "cdcf4f7b",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "186f7be9",
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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": "bb98bdf6",
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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": "f0231cd5",
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"id": "bcdf727d",
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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": "2388c16a",
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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": "3df5f8f5",
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"metadata": {},
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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": "293c8bc7",
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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_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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"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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"metadata": {},
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},
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{
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"cell_type": "markdown",
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"id": "1bfb51f7",
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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": "f058dab2",
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"metadata": {},
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"# 3D ResNet\n",
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{
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"cell_type": "code",
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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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"metadata": {},
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"#### Setup\n",
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{
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"cell_type": "code",
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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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{
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"#### Run Inference\n",
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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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