Devlog – Training Complete!
After all the dataset struggles, I finally completed the training!

Training Results
I trained a ResNet50 model for 20 epochs on 7 Mars terrain categories (bright dune, crater, dark dune, impact ejecta, slope streak, spider, swiss cheese).

Final metrics:

Training Accuracy: 96.41%

Validation Accuracy: 96.80%

Best Validation Accuracy: 97.00%

Training Loss: 0.1261

Validation Loss: 0.0968

The model achieved 97% validation accuracy which is solid for a 7-class classifier on orbital Mars imagery.

What Worked Well
ResNet50 pretrained on ImageNet was a great base – even with limited data, it learned quickly

Freezing early layers and only training layer4 + fc prevented overfitting

Dropout (0.4 and 0.3) helped with generalization

Data augmentation (random flips, rotations) made the model more robust

Next Steps
Now that I have a working model at 97% accuracy, here’s what’s next:

1. Test on completely unseen validation data

The 97% was measured on the validation split (20% of the original dataset)

Next, I need to test on brand new images that the model has never seen – not even in validation

1. Real-world testing

Find fresh Mars orbiter images online (not from the original dataset)

See if the model can correctly identify terrain types in the wild

1. Identify weak spots

Which categories is the model struggling with?

Spider and swiss cheese have fewer training samples – likely lower accuracy

Confusion matrix will show where mistakes happen

1. Deploy as a demo

Build a simple interface

Upload any Mars image and get instant terrain prediction

Goal
Take the 97% validation model and prove it works on real-world, never-before-seen Mars images – not just the test split from the dataset.