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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"source": [
"In this notebook, we will be discussing about the pytorch lightning datamodule library with images in a folder strutcture with folders as class labels. We will be using the cats and dogs dataset from kaggle. The dataset can be downloaded from [here](https://www.kaggle.com/c/dogs-vs-cats/data). The dataset contains 25000 images of cats and dogs. We will be using 20000 images for training and 5000 images for validation. The images are in a folder structure with folders as class labels."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "IPython.notebook.set_autosave_interval(300000)"
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Autosaving every 300 seconds\n"
]
}
],
"source": [
"%autosave 300\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"%reload_ext autoreload\n",
"%config Completer.use_jedi = False"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/mnt/batch/tasks/shared/LS_root/mounts/clusters/soutrik-vm-dev/code/Users/Soutrik.Chowdhury/pytorch-template-aws\n"
]
}
],
"source": [
"\n",
"import os\n",
"\n",
"os.chdir(\"..\")\n",
"print(os.getcwd())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "emlo_env",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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