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AI Blitz #8

F1 Car Rotation using Vgg19

F1 Car Rotation using Vgg19

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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Successfully installed fastai-2.3.1 torch-1.8.1 torchvision-0.9.1
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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]
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val.zip:   2%|▋                            | 1.05M/44.4M [00:00<00:33, 1.29MB/s]

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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]
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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]
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train.zip:  23%|██████▌                      | 101M/444M [00:20<01:35, 3.60MB/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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In [5]:
!rm ./test.zip
!rm ./val.zip
!rm ./train.zip

Import packages 📦

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

We unzip the ZIP files and move the CSVs.

In [6]:
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 [7]:
data_folder = "data"
In [8]:
train_df = pd.read_csv(os.path.join(data_folder, "train.csv"))
val_df = pd.read_csv(os.path.join(data_folder, "val.csv"))

Visualize the data 👀

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

In [9]:
train_df['label'].value_counts()
Out[9]:
left     15042
front    10046
right     9913
back      4999
Name: label, dtype: int64
In [10]:
val_df['label'].value_counts()
Out[10]:
left     1478
front    1029
right     976
back      517
Name: label, dtype: int64

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

In [11]:
train_df['ImageID'] = train_df['ImageID'].astype(str)+".jpg"
train_df
Out[11]:
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 [12]:
val_df['ImageID'] = val_df['ImageID']+40000
val_df['ImageID'] = val_df['ImageID'].astype(str)+".jpg"
val_df
Out[12]:
ImageID label
0 40000.jpg front
1 40001.jpg back
2 40002.jpg right
3 40003.jpg left
4 40004.jpg front
... ... ...
3995 43995.jpg left
3996 43996.jpg left
3997 43997.jpg left
3998 43998.jpg front
3999 43999.jpg right

4000 rows × 2 columns

In [13]:
from tqdm import tqdm

dir_ = "data/val"
for i in tqdm(os.listdir(dir_)):
    os.rename(os.path.join(dir_,i),os.path.join(dir_,str(int(i.split(".")[0])+40000))+".jpg")

100%|██████████| 4000/4000 [00:00<00:00, 47829.68it/s]
In [14]:
val_df['is_valid'] = True
train_df['is_valid'] = False
df = pd.concat([train_df,val_df])
In [15]:
!cp -r ./data/val/. ./data/train/.
In [16]:
len(os.listdir("./data/train"))
Out[16]:
44000
In [17]:
dls = ImageDataLoaders.from_df(df, path=os.path.join(data_folder, "train"), valid_col='is_valid', bs=16)

# 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 [18]:
#models.resnet50
learn = cnn_learner(dls, models.vgg19_bn ,metrics=F1Score(average="weighted") )

Downloading: "https://download.pytorch.org/models/vgg19_bn-c79401a0.pth" to /root/.cache/torch/hub/checkpoints/vgg19_bn-c79401a0.pth

Train the Model 🏃🏽‍♂️

In [19]:
learn.fine_tune(6)
epoch train_loss valid_loss f1_score time
0 0.119873 0.065620 0.978119 07:04
epoch train_loss valid_loss f1_score time
0 0.077831 0.028667 0.988918 09:33
1 0.017336 0.027565 0.992527 09:33
2 0.025011 0.012719 0.996240 09:32
3 0.011035 0.024824 0.994253 09:34
4 0.005762 0.018480 0.995988 09:34
5 0.000687 0.016111 0.995250 09:34

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 [20]:
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: 'back', 1: 'front', 2: 'left', 3: 'right'}
In [21]:
test_dls.show_batch()

Predict Test Set

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

In [22]:
_,_,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 [23]:
submission = pd.DataFrame({"ImageID":test_img_ids, "label":results})
submission
Out[23]:
ImageID label
0 7541 back
1 7963 left
2 2674 left
3 1089 front
4 7641 left
... ... ...
9995 7790 left
9996 7769 front
9997 4031 right
9998 6013 back
9999 369 left

10000 rows × 2 columns

In [24]:
submission.to_csv("submission.csv", index=False)

Making Direct Submission thought Aicrowd CLI

In [25]:
!aicrowd submission create -c f1-car-rotation -f submission.csv

submission.csv ━━━━━━━━━━━━━━━━━━ 100.0% • 105.6/103.9 KB • 345.1 kB/s • 0:00:00
                                                 ╭─────────────────────────╮                                                 
                                                 │ Successfully submitted! │                                                 
                                                 ╰─────────────────────────╯                                                 
                                                       Important links                                                       
┌──────────────────┬────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│  This submission │ https://www.aicrowd.com/challenges/ai-blitz-8/problems/f1-car-rotation/submissions/136535              │
│                  │                                                                                                        │
│  All submissions │ https://www.aicrowd.com/challenges/ai-blitz-8/problems/f1-car-rotation/submissions?my_submissions=true │
│                  │                                                                                                        │
│      Leaderboard │ https://www.aicrowd.com/challenges/ai-blitz-8/problems/f1-car-rotation/leaderboards                    │
│                  │                                                                                                        │
│ Discussion forum │ https://discourse.aicrowd.com/c/ai-blitz-8                                                             │
│                  │                                                                                                        │
│   Challenge page │ https://www.aicrowd.com/challenges/ai-blitz-8/problems/f1-car-rotation                                 │
└──────────────────┴────────────────────────────────────────────────────────────────────────────────────────────────────────┘
{'submission_id': 136535, 'created_at': '2021-05-12T04:50:05.829Z'}
In [ ]:

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