Day-Night Classifier

The model is built in pytorch and deployed as a REST API using Flask

Dependencies

pip install -r requirements.txt

Dataset Collection

Scraped 1200+ samples for model training, validation & testing using selenium which then filterd to 1200 samples

Dataset is collected for 13 different countries from Europe, USA & Australia

Inputs

Input data are resized, normalized accross the RGB channels & transformed to pytorch tensors:

transforms.Compose([
    transforms.Resize((224,224)),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

Model Training

Model is finetuned on resnet-101 with Adam optimizer and hyperparameters:

batch_size = 32
n_epochs = 32
learning_rate = 0.0001
betas = (0.9, 0.999)

Model Outputs

Train-Val Loss

Train-Val Accuracy

File Structure

app.py: Flask API application
build_model.py: Utilities for building the inference model
scraping_data.ipynb: Jupyter notebook for scraping the data
train.classifier.ipynb: Jupyter notebook for training the model
lib/models: Should contain saved_weights for the trained model
- You should make a new directory lib/models in the current working directory
- ```
mkdir lib && cd lib && mkdir models
```

Please download the model weights from this: link also, you'll find the scraped dataset through this link

For model training on colab notebook

Testing the API

Run the Flask API locally for testing. Go to directory with app.py

python app.py

In a new terminal window, use HTTPie to make a GET request at the URL of the API or use curl command for the same purpose

curl -X GET http://127.0.0.1:5000/ -d img="img_path" 

-X option for "Specifing request command to use"

Example of curl request

curl -X GET http://127.0.0.1:5000/ -d img='/home/ayman/scraped_dataset/day/9ea6801b53.jpg'

Example of successful output.

Serving Flask app "app" (lazy loading)
Environment: production
WARNING: This is a development server. Do not use it in a production deployment.
Use a production WSGI server instead.
Debug mode: on
Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
Restarting with inotify reloader
Debugger is active!
Debugger PIN: 167-245-170
127.0.0.1 - - [15/Feb/2021 20:35:31] "GET / HTTP/1.1" 200 -

{
    "prediction: ": "Day",
    "confidence: ": 0.9999970197677612
}

AymanNasser/Day-Night-Classifier