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https://github.com/saransh-cpp/scenenet-backend
Scenery detection using transfer learning. (Backend)
https://github.com/saransh-cpp/scenenet-backend
api-rest fastapi heroku python3 transfer-learning vgg19
Last synced: 13 days ago
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Scenery detection using transfer learning. (Backend)
- Host: GitHub
- URL: https://github.com/saransh-cpp/scenenet-backend
- Owner: Saransh-cpp
- Created: 2021-12-04T21:06:37.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2021-12-20T15:33:41.000Z (about 3 years ago)
- Last Synced: 2024-12-30T22:29:43.922Z (24 days ago)
- Topics: api-rest, fastapi, heroku, python3, transfer-learning, vgg19
- Language: Jupyter Notebook
- Homepage:
- Size: 98.1 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## SceneNet Backend
Scenery detection using transfer learning.
## Description
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) -
```py
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",
}
```
## Usage
- The API can be accessed through the URL - https://scene-net.herokuapp.com/
- To predict an image's class, use the `/predict` endpoint
- For the complete documentation refer to - https://scene-net.herokuapp.com/docs
## Running locally
### To train the model locally -
1. Fork and clone the repository
```
git clone https://github.com//SceneNet-Backend
```
2. Create a new virtual environment
```
python -m venve .venv
```
3. Activate the virtual environment
```
.venv/Scripts/activate
```
4. Install requirements for training
```
python -m pip install -r train_model/train_requirements.txt
```
5. Run the jupyter in the virtual environment
```
ipython kernel install --user --name=venv
# select the kernel named after your virtual environment in jupyter notebook
```
### To run the API locally-
1. Fork and clone the repository
```
git clone https://github.com//SceneNet-Backend
```
2. Create a new virtual environment
```
python -m venv .venv
```
3. Activate the virtual environment
```
.venv/Scripts/activate
```
4. Install requirements for training (the `Heroku` deployment uses `tensorflow-cpu` and `opencv-python-headless` because of the memory limitations, but you can switch to `tensorflow` and `opencv-python` if you are running this locally)
```
python -m pip install -r requirements.txt
```
5. Fire up the API
```
uvicorn backend.backend:app --reload
```
## Dataset used
https://www.kaggle.com/itsahmad/indoor-scenes-cvpr-2019