Scenery detection using transfer learning.
The API uses the VGG19 convoution neural network, which is trained on a dataset of 10903 images belonging to 67 different classes. The classes (as used in the code) -
labels = { 0: "airport_inside", 1: "artstudio", 2: "auditorium", 3: "bakery", 4: "bar", 5: "bathroom", 6: "bedroom", 7: "bookstore", 8: "bowling", 9: "buffet", 10: "casino", 11: "children_room", 12: "church_inside", 13: "classroom", 14: "cloister", 15: "closet", 16: "clothingstore", 17: "computerroom", 18: "concert_hall", 19: "corridor", 20: "deli", 21: "dentaloffice", 22: "dining_room", 23: "elevator", 24: "fastfood_restaurant", 25: "florist", 26: "gameroom", 27: "garage", 28: "greenhouse", 29: "grocerystore", 30: "gym", 31: "hairsalon", 32: "hospitalroom", 33: "inside_bus", 34: "inside_subway", 35: "jewelleryshop", 36: "kindergarden", 37: "kitchen", 38: "laboratorywet", 39: "laundromat", 40: "library", 41: "livingroom", 42: "lobby", 43: "locker_room", 44: "mall", 45: "meeting_room", 46: "movietheater", 47: "museum", 48: "nursery", 49: "office", 50: "operating_room", 51: "pantry", 52: "poolinside", 53: "prisoncell", 54: "restaurant", 55: "restaurant_kitchen", 56: "shoeshop", 57: "stairscase", 58: "studiomusic", 59: "subway", 60: "toystore", 61: "trainstation", 62: "tv_studio", 63: "videostore", 64: "waitingroom", 65: "warehouse", 66: "winecellar", }- The API can be accessed through the URL - https://scene-net.herokuapp.com/
- To predict an image's class, use the
/predictendpoint - For the complete documentation refer to - https://scene-net.herokuapp.com/docs
- Fork and clone the repository
git clone https://github.com/<your_username>/SceneNet-Backend - Create a new virtual environment
python -m venve .venv - Activate the virtual environment
.venv/Scripts/activate - Install requirements for training
python -m pip install -r train_model/train_requirements.txt - Run the jupyter in the virtual environment
ipython kernel install --user --name=venv # select the kernel named after your virtual environment in jupyter notebook - Fork and clone the repository
git clone https://github.com/<your_username>/SceneNet-Backend - Create a new virtual environment
python -m venv .venv - Activate the virtual environment
.venv/Scripts/activate - Install requirements for training (the
Herokudeployment usestensorflow-cpuandopencv-python-headlessbecause of the memory limitations, but you can switch totensorflowandopencv-pythonif you are running this locally)
python -m pip install -r requirements.txt - Fire up the API
uvicorn backend.backend:app --reload