Road Sign Classifier

Multiclass classification of road signs using diverse methods, featuring from-scratch, transfer learning, and transformer architectures. Demonstrates model training and monitoring with DVC.

Dataset Details

• Dataset Source: Roboflow - Self-Driving Cars
• License: CC BY 4.0
• Annotation Format: YOLOv8

This dataset, provided by a Roboflow user, contains road sign images for multiclass classification tasks in self-driving car applications. It was exported via Roboflow on October 20, 2023, at 9:18 AM GMT.

Methodology

Raw Data

• Image Count: 4969
• Image Dimensions: 416x416 (Resized)
• Image Augmentation: NoneS

Preliminary EDA revealed that the ideal categories for conducting classification would be the following:

[   'Speed Limit 100',
    'Speed Limit 120',
    'Speed Limit 20',
    'Speed Limit 30',
    'Speed Limit 40',
    'Speed Limit 50',
    'Speed Limit 60',
    'Speed Limit 70',
    'Speed Limit 80',
    'Speed Limit 90']

Accordingly, the data was processed into the standard image classification folder style with the above mentioned labels.

Data Augmentations

For the experimental stage, only image resizing and normalizing transformations have been used. If these do not result in adequate prediction accuracies, further augmentations will be considered.

Training

1. TinyVGG network - I am currently working on this. I have trained the TinyVGG model in Jupyter Notebook. I am currently working on moving the code to .py files and setting up experiment tracking.
2. TODO

Experiment Tracking

For this project I am using DVC to track my data and projects. I am currently building reproducible training pipelines.

Evaluation

TODO

References

TODO

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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Project based on the cookiecutter data science project template. #cookiecutterdatascience