Car Model Classification

This repo contains code of a classification task of different car types (ex. trucks, sedan...etc.)

You will find two branches for different models trained and tested on given datasets

Files inside the repo:

• data_analysis_ipynb: notebook for data analysis with insights
• dataloader.py: has function that prepares dataloaders for the training/test
• prep_csv.py: preparing csv files from dataset
• test.py: testing file for dataset
• train.py: training file
• TrucksDataset.py: Dataset file
• utils.py: has some utily functions
• data_analysis.pdf: same jupyter notebook file but in pdf

Folders:

• dataset (contains train test folders)
• plots (plots drawn during training)
• saved_models (dvc files of saved models)
• trial_runs (text files of outputs of training/testing trials)

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Setups used in this repo:

• DVC for dataset shared on a drive link, to pull data:
  • dvc fetch
  • dvc pull
• MLFLow for models, to run the MLFLow UI
  • run mlflow ui --port 8800 in command (supposedly, will be run by default inside the docker contained)
  • open localhost:8800 in your browser
• Docker image (to do), to run it:
  • docker ...

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To start the project:

1. git clone https://github.com/RadwaSK/AITask.git
2. cd AITask
3. Move dataset into folder dataset (there should be dataset/train and dataset/test, and you would find the csv files cloned with the repo)
4. Pull docker container docker pull radwask/aitask
5. Download the attached model ResNet_0 and move it into a folder "saved_models" in the repo folder
6. To run test script:
   • sudo docker run -v <path to repo>/AITask/dataset:/AITask/dataset -v <path to repo>/AITask/saved_models:/AITask/saved_models --gpus device=0 radwask/aitask python3 test.py -m ResNet_0 -b 4

P.S. THIS IS ASSUMING YOU HAVE CUDA ON YOUR DEVICE ! The models are trained with CUDA available.