Getting Started Code for F1 Car Rotation on AIcrowd¶
Download Necessary Packages 📚¶
In [1]:
!pip install --upgrade fastai
!pip install aicrowd-cli
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Collecting fastai
Downloading fastai-2.3.1-py3-none-any.whl (194 kB)
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Collecting torch<1.9,>=1.7.0
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Installing collected packages: torch, torchvision, fastai
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Collecting aicrowd-cli
Downloading aicrowd_cli-0.1.6-py3-none-any.whl (51 kB)
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Installing collected packages: commonmark, rich, requests-toolbelt, aicrowd-cli
Successfully installed aicrowd-cli-0.1.6 commonmark-0.9.1 requests-toolbelt-0.9.1 rich-10.1.0
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 [2]:
API_KEY = '9cce69d6577e95bdcfaf107bb38f8ff2' #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 [3]:
!aicrowd dataset download --challenge f1-car-rotation -j 3
train.csv: 100%|██████████████████████████████| 449k/449k [00:00<00:00, 703kB/s] sample_submission.csv: 100%|██████████████████| 104k/104k [00:00<00:00, 333kB/s] test.zip: 1%|▎ | 1.05M/111M [00:00<01:28, 1.24MB/s] test.zip: 2%|▌ | 2.10M/111M [00:00<00:44, 2.43MB/s] val.csv: 100%|██████████████████████████████| 40.9k/40.9k [00:00<00:00, 271kB/s] test.zip: 8%|██▏ | 8.39M/111M [00:02<00:22, 4.47MB/s] test.zip: 12%|███▌ | 13.6M/111M [00:02<00:13, 7.37MB/s] val.zip: 0%| | 0.00/44.4M [00:00<?, ?B/s] train.zip: 0%| | 1.05M/444M [00:00<05:56, 1.24MB/s] train.zip: 0%|▏ | 2.10M/444M [00:00<03:01, 2.43MB/s] train.zip: 1%|▎ | 4.19M/444M [00:01<01:26, 5.07MB/s] train.zip: 1%|▍ | 6.29M/444M [00:01<00:56, 7.75MB/s] val.zip: 2%|▋ | 1.05M/44.4M [00:00<00:33, 1.29MB/s] val.zip: 5%|█▎ | 2.10M/44.4M [00:00<00:16, 2.50MB/s] val.zip: 9%|██▋ | 4.19M/44.4M [00:01<00:07, 5.19MB/s] test.zip: 20%|█████▋ | 22.0M/111M [00:04<00:15, 5.74MB/s] val.zip: 19%|█████▍ | 8.39M/44.4M [00:02<00:09, 3.76MB/s] train.zip: 2%|▌ 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8%|██▎ | 35.7M/444M [00:07<01:01, 6.64MB/s] test.zip: 42%|████████████▎ | 47.2M/111M [00:09<00:09, 6.61MB/s] train.zip: 9%|██▍ | 38.8M/444M [00:07<00:46, 8.67MB/s] test.zip: 45%|█████████████▏ | 50.3M/111M [00:10<00:11, 5.33MB/s] test.zip: 47%|█████████████▋ | 52.4M/111M [00:10<00:09, 5.89MB/s] test.zip: 50%|██████████████▌ | 55.6M/111M [00:11<00:07, 7.79MB/s] val.zip: 87%|█████████████████████████▎ | 38.8M/44.4M [00:08<00:00, 7.45MB/s] train.zip: 9%|██▋ | 41.9M/444M [00:09<01:34, 4.25MB/s] train.zip: 10%|██▊ | 44.0M/444M [00:09<01:19, 5.04MB/s] val.zip: 100%|█████████████████████████████| 44.4M/44.4M [00:08<00:00, 5.09MB/s] test.zip: 58%|████████████████▋ | 64.0M/111M [00:12<00:06, 7.46MB/s] train.zip: 11%|███▏ | 50.3M/444M [00:10<01:33, 4.23MB/s] train.zip: 12%|███▎ | 52.4M/444M [00:10<01:17, 5.04MB/s] test.zip: 65%|██████████████████▉ | 72.4M/111M [00:14<00:05, 6.74MB/s] train.zip: 13%|███▋ | 58.7M/444M [00:12<01:20, 4.79MB/s] train.zip: 14%|███▊ | 60.8M/444M [00:12<01:06, 5.77MB/s] 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Below, we create a new directory to put our downloaded data! 🏎
We unzip the ZIP files and move the CSVs.
In [4]:
!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
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