An image classification model from data collection, cleaning, model training, deployment and API integration.
The model can classify 10 different types of balls
The types are the following:
1. Football
2. Basketball
3. Volleyball
4. Rugby
5. Golf
6. Cricket
7. Tennis
8. Bowling
9. Billiards
10. Baseball

Data Collection: Downloaded from DuckDuckGo using term name and adding extra string ' only balls images'. A total of 2872 images were collected initially. After unlinking the damaged data, the total number of images was 2776.
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 data_prep.ipynb or

Training: Fine-tuned several pre-trained models for 5 epochs and got resnet34 as the best model. Then resnet34 was finetuned for 4 more times (Total 5 times) and achieved ~99% accuracy.
Results Comparison:

Data Cleaning: This part took the longest time. Since I collected data 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 model_imp.ipynb or .

I deployed the model to the HuggingFace Spaces Gradio App. The implementation can be found in the deployment folder or .

The deployed model API is integrated in the GitHub Pages Website. Implementation and other details can be found in the docs folder.