CarClassifier

This repository gathers the code for car image classification from the in-class Kaggle challenge. See more details in report.

Reproducing Submission

Our model achieve 95.04% accuracy in testing set.

To reproduct my submission without retrainig, do the following steps:

1. Installation
2. Ensemble Prediction

Installation

All requirements should be detailed in requirements.txt. Using Anaconda is strongly recommended.

conda create -n carClassifier python=3.7
conda activate carClassifier
pip install -r requirements.txt

Download Official Image

Official image can be downloaded from Kaggle challenge or just downloaded from my repository.

Dataset Preparation

After downloading images from Kaggle challenge, we expect the data directory is structured as:

data
  +- training_data        # all training data from kaggle
  ∣- 000001.jpg
  ∣- 000002.jpg
  ∣- ..........
  +- testing_data         # all testing data from kaggle
  ∣- 000004.jpg
  ∣- 000005.jpg
  ∣- ..........
  - training_labels.csv   # csv file contain img's id and label

Run the following command to build the data directory above

Run:

mkdir data\train
mkdir data\val
python src/dataPreprocessing.py

After run the command, the data directory should be following struture:

data
    +- training_data        # all training data from kaggle
    +- testing_data         # all testing data from kaggle
    +- train                # training set split
    +- val                  # validatoin set split from training_data
    - train.csv             # record img's id and label in train folder
    - val.csv               # record img's id and albel in val folder
    - training_labels.csv   # csv file contain img's id and label
    - training_labelsWithInt.csv # csv file contain img's id and label and label(int)

Training

Training configuration can be specified in src/configs.py.

Then, run:

python src/train.py 

Default model will use pretrain ResNet-50.

In addition, you can use parameter -m "model_name" specify what kinds of pretrained model you want to train.

For instance:

python src/train.py -m "resnet50"

Pretrained model come from PyTorch office [1], rwightman/pytorch-image-models [2]. Avaliable pretrained model_name in this task are showed below:

"resnet50", "resnet101", "tresnet_l", "tresnet_m", "densenet121", "resnext50_32x4d", "resnext101_32x8d"

Trained model will be save as src/model/trained_model/model.pth

Inference

If trained model are prepared, use it to infer your testing data.

Run:

python src/infer.py -m "model_name" -mp "trained_model_path"

This will save the testing predictions in test_result/test_pred.csv.

Ensemble Prediction

In the task, we apply bagging (Majority vote) method, one kinds of ensemble, to improve our accuracy in testing set.

Make sure your testing results (*.csv) in Inference stage inside ./test_result. The program will use all .csv file in ./test_result to perform bagging (Majority vote) method.

Run:

python src/ensemble.py

Result will be save in ./ensemble.csv.

For Reproducing Submission: There are some testing result in ./test_result that we tested before. You can directly run python src/ensemble.py without traning.

Future Work

Provide more argument parsing parameters to make the user easier to use our program.

References

[1] Pretrained model in PyTorch Office WebSite

[2] rwightman/pytorch-image-models.