An image classification model from data collection, cleaning, model training, deployment and API integration. The model can classify 10 different types of art styles. The types are the following:
- Art Nouveau
- Constructivism Art
- Cubism Art
- Dadaism Art
- Fauvism Art
- Gothic Art
- Minimalism Art
- Pointillism Art
- Pop Art
- Prehistoric Art
Data Collection: Downloaded from DuckDuckGo using term names. A total of 1890 images were collected.
DataLoader: Used FastAI DataBlock API to set up the DataLoader.
Data Augmentation: FastAI provides default data augmentation which operates in GPU.
Details can be found in notebooks/data_preparation.ipynb
Training: Fine-tuned resnet34 for 5 epochs (3 times) and achieved ~88% accuracy.
Data Cleaning: As the data was collected from the browser, there were many noises. Also, some images contained irrelevant data. I cleaned and updated data using FastAI ImageClassifierCleaner. I cleaned the data each time after training or fine-tuning, except for the last time which was the final iteration of the model.
You can check the data training and cleaning process in notebooks/training_and_data_cleaning.ipynb
I deployed the model to the HuggingFace Spaces Gradio App. The implementation can be found in the deployment folder or here
The deployed model API is integrated here in the GitHub Pages Website.
Implementation and other details can be found in the docs folder