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AI Blitz #8

F1 Car Rotation using Vgg19

F1 Car Rotation using Vgg19

By  g_mothy


Getting Started Code for F1 Car Rotation on AIcrowd

Download Necessary Packages 📚

In [1]:
!pip install --upgrade fastai 
!pip install aicrowd-cli
Requirement already satisfied: fastai in /opt/conda/lib/python3.7/site-packages (2.3.0)
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  Downloading torch-1.8.1-cp37-cp37m-manylinux1_x86_64.whl (804.1 MB)
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Installing collected packages: torch, torchvision, fastai
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Collecting aicrowd-cli
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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]
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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%|▌                           | 8.39M/444M [00:02<02:55, 2.48MB/s]

val.zip:  24%|██████▊                      | 10.5M/44.4M [00:02<00:06, 4.88MB/s]
train.zip:   2%|▋                           | 10.5M/444M [00:03<02:08, 3.37MB/s]

val.zip:  31%|████████▉                    | 13.6M/44.4M [00:02<00:04, 7.39MB/s]
train.zip:   3%|▊                           | 12.6M/444M [00:03<01:31, 4.72MB/s]
test.zip:  23%|██████▌                      | 25.2M/111M [00:05<00:20, 4.28MB/s]

test.zip:  27%|███████▉                     | 30.4M/111M [00:06<00:12, 6.39MB/s]
train.zip:   4%|█                           | 16.8M/444M [00:04<02:11, 3.24MB/s]
train.zip:   4%|█▏                          | 18.9M/444M [00:04<01:44, 4.05MB/s]
test.zip:  35%|██████████▏                  | 38.8M/111M [00:08<00:12, 5.59MB/s]
train.zip:   6%|█▌                          | 25.2M/444M [00:06<01:39, 4.19MB/s]

val.zip:  43%|████████████▎                | 18.9M/44.4M [00:05<00:11, 2.27MB/s]

val.zip:  50%|██████████████▍              | 22.0M/44.4M [00:05<00:06, 3.37MB/s]
train.zip:   6%|█▋                          | 27.3M/444M [00:06<01:24, 4.92MB/s]
test.zip:  38%|██████████▉                  | 41.9M/111M [00:09<00:15, 4.41MB/s]

val.zip:  57%|████████████████▍            | 25.2M/44.4M [00:06<00:05, 3.59MB/s]
test.zip:  40%|███████████▍                 | 44.0M/111M [00:09<00:13, 4.89MB/s]
train.zip:   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]
test.zip:  73%|█████████████████████        | 80.7M/111M [00:15<00:04, 7.39MB/s]
train.zip:  15%|████▏                       | 67.1M/444M [00:13<01:14, 5.09MB/s]
train.zip:  16%|████▎                       | 69.2M/444M [00:13<01:00, 6.18MB/s]
test.zip:  80%|███████████████████████▎     | 89.1M/111M [00:16<00:03, 7.21MB/s]
train.zip:  17%|████▊                       | 75.5M/444M [00:15<01:24, 4.34MB/s]
train.zip:  17%|████▉                       | 77.6M/444M [00:15<01:10, 5.17MB/s]
test.zip:  88%|█████████████████████████▍   | 97.5M/111M [00:18<00:02, 6.51MB/s]
train.zip:  19%|█████▎                      | 83.9M/444M [00:16<01:19, 4.55MB/s]
train.zip:  19%|█████▍                      | 86.0M/444M [00:16<01:08, 5.19MB/s]
test.zip:  95%|████████████████████████████▌ | 106M/111M [00:19<00:00, 7.96MB/s]
train.zip:  21%|█████▊                      | 92.3M/444M [00:18<01:14, 4.74MB/s]
train.zip:  21%|█████▉                      | 94.4M/444M [00:18<01:01, 5.65MB/s]
test.zip: 100%|██████████████████████████████| 111M/111M [00:20<00:00, 5.33MB/s]

train.zip:  23%|██████▌                      | 101M/444M [00:20<01:35, 3.60MB/s]
train.zip:  23%|██████▋                      | 103M/444M [00:20<01:18, 4.32MB/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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