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AI Blitz #8

F1 team Classification

F1 Team Classification using Resnet18 Pretrained Model

g_mothy

Binary Image Classification Problem

Getting Started Code for F1 Team Classification Challenge on AIcrowd

Download Necessary Packages 📚

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

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 = '' #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-team-classification -j 3

test.zip:   0% 0.00/97.7M [00:00<?, ?B/s]
train.csv:   0% 0.00/569k [00:00<?, ?B/s]

sample_submission.csv:   0% 0.00/134k [00:00<?, ?B/s]

sample_submission.csv: 100% 134k/134k [00:00<00:00, 349kB/s]

train.csv: 100% 569k/569k [00:00<00:00, 868kB/s]
test.zip:  34% 33.6M/97.7M [00:02<00:04, 13.3MB/s]
val.csv:   0% 0.00/52.9k [00:00<?, ?B/s]
val.csv: 100% 52.9k/52.9k [00:00<00:00, 277kB/s]

train.zip:   0% 0.00/391M [00:00<?, ?B/s]

test.zip: 100% 97.7M/97.7M [00:05<00:00, 16.6MB/s]


val.zip:  86% 33.6M/38.9M [00:02<00:00, 13.2MB/s]

val.zip: 100% 38.9M/38.9M [00:02<00:00, 14.0MB/s]

train.zip:   9% 33.6M/391M [00:05<00:53, 6.65MB/s]
train.zip:  17% 67.1M/391M [00:09<00:42, 7.60MB/s]
train.zip:  26% 101M/391M [00:12<00:34, 8.45MB/s] 
train.zip:  34% 134M/391M [00:16<00:29, 8.61MB/s]
train.zip:  43% 168M/391M [00:22<00:31, 7.04MB/s]
train.zip:  52% 201M/391M [00:25<00:23, 7.92MB/s]
train.zip:  60% 235M/391M [00:29<00:19, 8.19MB/s]
train.zip:  69% 268M/391M [00:33<00:14, 8.19MB/s]
train.zip:  77% 302M/391M [00:38<00:11, 7.76MB/s]
train.zip:  86% 336M/391M [00:41<00:06, 8.26MB/s]
train.zip:  95% 369M/391M [00:46<00:02, 8.17MB/s]
train.zip: 100% 391M/391M [00:47<00:00, 8.16MB/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
In [ ]:
!rm -rf ./train.zip ./val.zip ./test.zip

Import packages

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 redbull
1 1 mercedes
2 2 redbull
3 3 redbull
4 4 redbull
... ... ...
39995 39995 redbull
39996 39996 redbull
39997 39997 redbull
39998 39998 redbull
39999 39999 redbull

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 redbull
1 1.jpg mercedes
2 2.jpg redbull
3 3.jpg redbull
4 4.jpg redbull
... ... ...
39995 39995.jpg redbull
39996 39996.jpg redbull
39997 39997.jpg redbull
39998 39998.jpg redbull
39999 39999.jpg redbull

40000 rows × 2 columns

In [ ]:
train_df['label'].value_counts()
Out[ ]:
redbull     20043
mercedes    19957
Name: label, dtype: int64
In [ ]:
dls = ImageDataLoaders.from_df(train_df, path=os.path.join(data_folder, "train"), 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 [ ]:
# learn = cnn_learner(dls, alexnet, metrics=F1Score())
learn = cnn_learner(dls, models.resnet18, pretrained=True,  metrics=F1Score()) #, lr=0.0001

Train the Model 🏃🏽‍♂️

In [ ]:
learn.fine_tune(1) #1, base_lr=0.0001
epoch train_loss valid_loss f1_score time
0 0.063166 0.000756 0.999749 04:24
epoch train_loss valid_loss f1_score time
0 0.000177 0.000000 1.000000 05:56

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: 'mercedes', 1: 'redbull'}
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 8847 mercedes
1 7457 mercedes
2 7911 mercedes
3 703 mercedes
4 5389 mercedes
... ... ...
9995 1310 redbull
9996 9804 mercedes
9997 6191 redbull
9998 823 mercedes
9999 3724 mercedes

10000 rows × 2 columns

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

Making Direct Submission thought Aicrowd CLI

In [ ]:
#!aicrowd submission create -c f1-team-classification -f submission.csv

submission.csv ━━━━━━━━━━━━━━━━━━ 100.0% • 135.1/133.4 KB • 614.4 kB/s • 0:00:00
                                                    ╭─────────────────────────╮                                                     
                                                    │ Successfully submitted! │                                                     
                                                    ╰─────────────────────────╯                                                     
                                                          Important links                                                           
┌──────────────────┬───────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│  This submission │ https://www.aicrowd.com/challenges/ai-blitz-8/problems/f1-team-classification/submissions/136936              │
│                  │                                                                                                               │
│  All submissions │ https://www.aicrowd.com/challenges/ai-blitz-8/problems/f1-team-classification/submissions?my_submissions=true │
│                  │                                                                                                               │
│      Leaderboard │ https://www.aicrowd.com/challenges/ai-blitz-8/problems/f1-team-classification/leaderboards                    │
│                  │                                                                                                               │
│ Discussion forum │ https://discourse.aicrowd.com/c/ai-blitz-8                                                                    │
│                  │                                                                                                               │
│   Challenge page │ https://www.aicrowd.com/challenges/ai-blitz-8/problems/f1-team-classification                                 │
└──────────────────┴───────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
{'submission_id': 136936, 'created_at': '2021-05-13T08:55:39.419Z'}
In [ ]:

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