An image classification model from data collection, cleaning, model training, deployment and API integration.
The model can classify 15 different types of vegetables
The types are following:

1. Carrots
2. Tomatoes
3. Lettuce
4. Spinach
5. Broccoli
6. Cucumbers
7. Bell Peppers
8. Potatoes
9. Onions
10. Garlic
11. Zucchini
12. Cabbage
13. Kale
14. Radishes
15. Avocado

Data Collection: Downloaded from DuckDuckGo using term name
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/Vegetable_Recognizer(Data_Prep).ipynb

Training: Fine-tuned a resnet34 model for 5 epochs (3 times) and got upto ~85% accuracy.
Data Cleaning: This part took the highest time. Since I collected data from browser, there were many noises. Also, there were images that contained. I cleaned and updated data using fastai ImageClassifierCleaner. I cleaned the data each time after training or finetuning, except for the last time which was the final iteration of the model.

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

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