Deploying a FastAi image classification Model trained on 35 classes of Nigerian food using Flask API
Food classes include:
'Yam and egg sauce', 'Yam pottage', 'Rice and stew', 'fufu pounded yam and egusi', 'Jollof rice', 'fried rice', 'eba-fufu and afang-vegetable soup', 'eba-fufu and draw-okra soup','eba-fufu and ogbono soup', 'eba-fufu and oha soup', 'yam_potato and beans porridge', 'beans and fried plantain', 'soaked garri, groundnut and sugar', 'moi moi', 'amala and ewedu', 'pap-custard-akamu', 'Indomie with vegetables and egg', 'pounded yam and ofe riro', 'akara', 'abacha-african salad', 'masa', 'fried potato-yam', 'ukwa', 'catfish pepper soup', 'boiled plantain and sauce', 'rice and beans', 'beans porridge', 'bread and egg', 'spaghetti', 'okpa', 'pounded yam-fufu and white soup- ofe nsala', 'Vegetable salad', 'plantain and egg sauce', 'boiled-roasted corn', 'No identified Nigerian dish'
The tmp folder contains the test image to be predicted: image.jpg
The model folder contains the pickcle file of the trained model
The Resources folder contains the jupyter notebook used for training the model
Download code
Change directory to code folder in cmd prompt
Create a python virtual env using pip
In the cmd prompt run;
pip install -r requirements.txt
python app.py
The training data used for is this project is accessible at https://drive.google.com/drive/folders/1YFRVRmWP4m0XkMM45YEXvavsNtkmMUPu?usp=sharing