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AI Blitz #6

[Baseline] Chess Pieces

A getting started code for the Chess Pieces challenge.

ashivani

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Getting Started Code for Chess Pieces on AIcrowd

Author : Shubhamai

Download Necessary Packages 📚

In this baseline we are going to use FastAI as our main library to train out model and making & submitting predictions

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!pip install --upgrade fastai git+https://gitlab.aicrowd.com/yoogottamk/aicrowd-cli.git >/dev/null
%load_ext aicrowd.magic

Download Data

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.

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API_KEY = '' #Please enter your API Key [https://www.aicrowd.com/participants/me]
%aicrowd login --api-key $API_KEY
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%aicrowd dataset download --challenge chess-pieces -j 3
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!rm -rf data
!mkdir data

!unzip train.zip  -d data/ 
!unzip val.zip -d data/ 
!unzip test.zip  -d data/ 

!mv train.csv data/train.csv
!mv val.csv data/val.csv
!mv sample_submission.csv data/sample_submission.csv

Import packages

In [ ]:
import pandas as pd
from fastai.vision.all import *
from fastai.data.core import *
import os

Load Data

  • We use pandas 🐼 library to load our data.
  • Pandas loads the data into dataframes and facilitates us to analyse the data.
  • Learn more about it here 🤓
In [ ]:
train_df = pd.read_csv("data/train.csv")

Visualize the data 👀

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train_df
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train_df['ImageID'] = train_df['ImageID'].astype(str)+".jpg"
train_df
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dls = ImageDataLoaders.from_df(train_df, path="data/train", bs=8)
dls.show_batch()

TRAINING PHASE 🏋️

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learn = cnn_learner(dls, alexnet, metrics=F1Score())

Train the Model

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learn.fine_tune(1)

Testing Phase 😅

We are almost done. We trained and validated on the training data. Now its the time to predict on test set and make a submission.# Prediction on Evaluation Set

Predict Test Set

Predict on the test set and you are all set to make the submission!

In [ ]:
test_imgs_name = get_image_files("data/test")
test_dls = dls.test_dl(test_imgs_name)

label_to_class_mapping = {v: k for v, k in enumerate(dls.vocab)}
print(label_to_class_mapping)

test_img_ids = [re.sub(r"\D", "", str(img_name)) for img_name in test_imgs_name]
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test_dls.show_batch()
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_,_,results = learn.get_preds(dl = test_dls, with_decoded = True)

results = [label_to_class_mapping[i] for i in results.numpy()]

Save the prediction to csv

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submission = pd.DataFrame({"ImageID":test_img_ids, "label":results})
submission

🚧 Note :

  • Do take a look at the submission format.
  • The submission file should contain a header.
  • Follow all submission guidelines strictly to avoid inconvenience.
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submission.to_csv("submission.csv", index=False)

To download the generated csv in colab run the below command.

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try:
    from google.colab import files
    files.download('submission.csv') 
except:
    print("Option Only avilable in Google Colab")

Well Done! 👍 We are all set to make a submission and see your name on leaderborad. Let navigate to challenge page and make one.

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