CHESS POINTS
[Baseline] Chess Points
A getting started code for the Chess Points challenge.
ashivani
Getting Started Code for Chess Points 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-points -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¶
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import pandas as pd
from fastai.vision.all import *
import os
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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!
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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
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submission.to_csv("submission.csv", index=False)
🚧 Note :¶
- Do take a look at the submission format.
- The submission file should contain a header.
- Follow all submission guidelines strictly to avoid inconvenience.
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.¶
Content
Comments
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Hi @ashivani,
You used this line of code in order to load the dataset in the Colab environment: %aicrowd dataset download –challenge chess-points -j 3
Is it possible to modify it a little in order to load only one file, for instance train.zip? Thank you in advance.