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F1 CAR ROTATION

[Baseline] F1 Car Rotation

F1 Car Rotation baseline notebook from F1 Car Rotation Challenge of Blitz 8

Shubhamaicrowd

creative blitz 8_7.1 _linkpreview copy 8.jpg

Getting Started Code for F1 Car Rotation on AIcrowd

Author : Shubhamai

Download Necessary Packages 📚

In [ ]:
!pip install --upgrade fastai 
!pip install aicrowd-cli

Collecting fastai
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ERROR: torchtext 0.9.1 has requirement torch==1.8.1, but you'll have torch 1.7.1 which is incompatible.
Installing collected packages: torch, torchvision, fastcore, fastai
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ERROR: torchtext 0.9.1 has requirement torch==1.8.1, but you'll have torch 1.7.1 which is incompatible.
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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.

In [ ]:
API_KEY = 'YOUR_API_KEY' #Please enter your API Key from [https://www.aicrowd.com/participants/me]
!aicrowd login --api-key $API_KEY

API Key valid
Saved API Key successfully!
In [ ]:
!aicrowd dataset download --challenge f1-car-rotation -j 3

train.csv:   0% 0.00/494k [00:00<?, ?B/s]
sample_submission.csv:   0% 0.00/114k [00:00<?, ?B/s]

test.zip:   0% 0.00/69.9M [00:00<?, ?B/s]
sample_submission.csv: 100% 114k/114k [00:00<00:00, 702kB/s]
train.csv: 100% 494k/494k [00:00<00:00, 1.62MB/s]
val.csv: 100% 45.4k/45.4k [00:00<00:00, 546kB/s]
train.zip:   0% 0.00/280M [00:00<?, ?B/s]

test.zip:  48% 33.6M/69.9M [00:01<00:02, 17.6MB/s]
train.zip:  12% 33.6M/280M [00:02<00:17, 14.4MB/s]

test.zip: 100% 69.9M/69.9M [00:03<00:00, 17.9MB/s]

val.zip: 100% 27.8M/27.8M [00:03<00:00, 9.15MB/s]
train.zip: 100% 280M/280M [00:18<00:00, 15.1MB/s]

Below, we create a new directory to put our downloaded data! 🏎

We unzip the ZIP files and move the CSVs.

In [ ]:
!rm -rf data
!mkdir data

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

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

Import packages 📦

Below, we create a new directory to put our downloaded data! 🏎

We unzip the ZIP files and move the CSVs.

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 [ ]:
data_folder = "data"
In [ ]:
train_df = pd.read_csv(os.path.join(data_folder, "train.csv"))

Visualize the data 👀

Using Pandas and the Matplot Library in Python, we will be viewing the images in our datasets.

In [ ]:
train_df
Out[ ]:
ImageID label
0 0 right
1 1 left
2 2 left
3 3 right
4 4 right
... ... ...
39995 39995 right
39996 39996 right
39997 39997 right
39998 39998 right
39999 39999 left

40000 rows × 2 columns

Adding .jpg to all the ImageIDs in "ImageID" column. This will help us with adding the path behind the names of these images.

In [ ]:
train_df['ImageID'] = train_df['ImageID'].astype(str)+".jpg"
train_df
Out[ ]:
ImageID label
0 0.jpg right
1 1.jpg left
2 2.jpg left
3 3.jpg right
4 4.jpg right
... ... ...
39995 39995.jpg right
39996 39996.jpg right
39997 39997.jpg right
39998 39998.jpg right
39999 39999.jpg left

40000 rows × 2 columns

In [ ]:
dls = ImageDataLoaders.from_df(train_df, path=os.path.join(data_folder, "train"), bs=8)

# Defining a function to take a look at the images
dls.show_batch()

TRAINING PHASE 🏋️

Now that we have the dataset is ready, it's time to create a model that we will train on our data!

In [ ]:
learn = cnn_learner(dls, alexnet)

Train the Model 🏃🏽‍♂️

In [ ]:
learn.fine_tune(1)
epoch train_loss valid_loss time
0 1.076775 0.903564 01:46
epoch train_loss valid_loss time
0 0.253281 0.146583 01:57

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.

Load Test Set

Load the test data on which final submission is to be made.

In [ ]:
test_imgs_name = get_image_files(os.path.join(data_folder, "test"))
test_dls = dls.test_dl(test_imgs_name)

# Convert categorical values into label names
class_to_label_mapping = {v: k for v, k in enumerate(dls.vocab)}
print(class_to_label_mapping)

test_img_ids = [re.sub(r"\D", "", str(img_name)) for img_name in test_imgs_name]

{0: 'front', 1: 'left', 2: 'right'}
In [ ]:
test_dls.show_batch()

Predict Test Set

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

In [ ]:
_,_,results = learn.get_preds(dl = test_dls, with_decoded = True)

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

Save the prediction to csv

🚧 Note :

  • Do take a look at the submission format.
  • The submission file should contain a header.
  • Follow all submission guidelines strictly to avoid inconvenience.
In [ ]:
submission = pd.DataFrame({"ImageID":test_img_ids, "label":results})
submission
Out[ ]:
ImageID label
0 6303 left
1 6264 left
2 347 front
3 9483 left
4 3499 front
... ... ...
9995 3268 right
9996 828 front
9997 2630 left
9998 1149 right
9999 8125 right

10000 rows × 2 columns

In [ ]:
submission.to_csv("submission.csv", index=False)

Making Direct Submission thought Aicrowd CLI

In [ ]:
!aicrowd submission create -c f1-car-rotation -f submission.csv

Content

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