Photo by rawpixel on Unsplash- In this project, we developed and trained two deep-learning models on a satellite image dataset from Kaggle.
- The first model is a regular CNN-based model with 17 layers and uses a cross-entropy loss function to learn the weights/kernels. (RegNet17 model)
- The second model is based on the residual architecture added to conventional CNNs. This model has 17 layers and uses a cross-entropy loss function for learning the weights/kernels. (ResNet17 model)
- Finally, both models were compared for speed and accuracy.
- Although RegNet17 is more accurate, ResNet17 has low inference time, which is vital for real-time object classification.
