"# Getting Started Code for [JIGSAW Challenge](www.aicrowd.com/challenges/jigsaw) on AIcrowd\n",
"#### Author : Sanjay Pokkali"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download Necessary Packages 📚"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install numpy\n",
"!pip install pandas\n",
"!pip install scikit-learn\n",
"!pip install textdistance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download Data\n",
"The first step is to download out train test data. We will be training a model on the train data and make predictions on test data. We submit our predictions.\n"
"- The next step is to think of a way to test how well our model is performing. we cannot use the test data given as it does not contain the data labels for us to verify. \n",
"- The workaround this is to split the given training data into training and validation. Typically validation sets give us an idea of how our model will perform on unforeseen data. it is like holding back a chunk of data while training our model and then using it to for the purpose of testing. it is a standard way to fine-tune hyperparameters in a model. \n",
"- There are multiple ways to split a dataset into validation and training sets. following are two popular ways to go about it, [k-fold](https://machinelearningmastery.com/k-fold-cross-validation/), [leave one out](https://en.wikipedia.org/wiki/Cross-validation_statistics). 🧐\n",
"- Validation sets are also used to avoid your model from [overfitting](https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/) on the train dataset."
"- We have decided to split the data with 20 % as validation and 80 % as training. \n",
"- To learn more about the train_test_split function [click here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html). 🧐 \n",
"- This is of course the simplest way to validate your model by simply taking a random chunk of the train set and setting it aside solely for the purpose of testing our train model on unseen data. as mentioned in the previous block, you can experiment 🔬 with and choose more sophisticated techniques and make your model better."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Now, since we have our data splitted into train and validation sets, we need to get the corresponding labels separated from the data. \n",
"- with this step we are all set move to the next step with a prepared dataset."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TRAINING PHASE 🏋️"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**We will use PyTesseract, an Optical Character Recognition library to recognize the characters in the test captcha directly and make a submission in this notebook.But lest see its performace on the train set.**"
"**Here for evaluation mean over normalised [Levenshtein Similarity Score](https://en.wikipedia.org/wiki/Levenshtein_distance) will be used to test the efficiency of the model.**\n"
"- The submission file should contain a header. \n",
"- Follow all submission guidelines strictly to avoid inconvenience."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## To download the generated csv in colab run the below command"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" from google.colab import files\n",
" files.download('submission.csv') \n",
"except:\n",
" print(\"Option Only avilable in Google Colab\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Well Done! 👍 We are all set to make a submission and see your name on leaderborad. Lets navigate to [challenge page](https://www.aicrowd.com/challenges/CPTCHA) and make one."
"# Getting Started Code for [JIGSAW Challenge](www.aicrowd.com/challenges/jigsaw) on AIcrowd\n",
"#### Author : Sharada Mohanty"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FSQaFNqj4Xqg"
},
"source": [
"This baseline creates the image of desired size and places the puzzle pieces randomly."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g3NTJjh6zsZj"
},
"source": [
"## Download Necessary Packages 📚"
]
},
{
"cell_type": "code",
"metadata": {
"id": "52VNLEdxzsZl"
},
"source": [
"!pip install numpy\n",
"!pip install pandas"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "v1252SIXzsZp"
},
"source": [
"## Download Data\n",
"The first step is to download out train test data. We will be training a model on the train data and make predictions on test data. We submit our predictions.\n"
"# Create the tar file with all the solved images and store it in the OUTPUT PATH."
]
},
{
"cell_type": "code",
"metadata": {
"id": "KMRVv9Cw3n29"
},
"source": [
"with tarfile.open(OUTPUT_PATH, mode=\"w:gz\") as tar_file:\n",
" for _filepath in glob.glob(os.path.join(TEMP_SUBMISSION_DIR_PATH, \"*.jpg\")):\n",
" print(_filepath)\n",
" _, filename = os.path.split(_filepath)\n",
" tar_file.add(_filepath, arcname=filename)\n",
"\n",
"print(\"Wrote output file to : \", OUTPUT_PATH)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "k-WPsT0Szsag"
},
"source": [
"## To download the generated tar.gz in colab run the below command"
]
},
{
"cell_type": "code",
"metadata": {
"id": "c2lFyA17zsah"
},
"source": [
"try:\n",
" from google.colab import files\n",
" files.download(OUTPUT_PATH) \n",
"except:\n",
" print(\"Option Only avilable in Google Colab\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "KKZfXEPUzsak"
},
"source": [
"### Well Done! 👍 We are all set to make a submission and see your name on leaderborad. Lets navigate to [challenge page](https://www.aicrowd.com/challenges/jigsaw) and make one."
