- Lalit Jain https://www.linkedin.com/in/lalit7jain/
- Rohit Agarwal https://www.linkedin.com/in/rohitag04/
- Shivam Goel https://www.linkedin.com/in/goelshi/
- Hina Gandhi: https://www.linkedin.com/in/hina-gandhi-52834356
This Assignment consist of Object Classification Using CNN
- Clone the Repository to System.Github link
- Create Google API keyGoogle Api Key
- Create Gpu Instance Using Amazon Aws Service and Install Django and related libraries to run Python Link of Document for Reference
- SCP the Django files with name mysite in an AWS GPU Instance under directory '/home/Ubuntu'
Run the Server using below command
python manage.py runserver 0.0.0.0:8000
We have dataset of Natural Calamity Namely Hurricane, Volcanic Eruption, Earthquake, Tornado. The dataset of images was collected using Google API. We collected dataset of 400 images equally distributed among 4 classes from google which are jpeg images and relevant to project All images had 3 channels, Red, Green and Blue and were 150x150 pixels large. Figure 1 shows sample data from our collected dataset. There were many grayscale images that would most likely only introduce noise to our model, as such, we had to filter them out.
Additionally, many pictures did not clearly correspond to the class presented. There was lot of corrupted images while downloading from API where were filtered out at later stages before preprocessing starts.
- The image data-set is available at Object Classification Dataset
We preprocessed the images to increase the accuracy of our models. The technique that we used were data augmentation on the dataset to make a new dataset.
A CNN is a neural network that typically contains several types of layers
convolutional layer
pooling layer
activation layers
The final architecture retained can be described as follows:
• 3 × 3 Conv - ReLU - 2×2 Max-Pool with 32 filters
• 3 × 3 Conv - ReLU - 2×2 Max-Pool with 32 filters
• 3 × 3 Conv - ReLU - 2×2 Max-Pool with 64 filters
• FC layer to 4 class