1. Object Detection with Bootstrapping Carlos Rubiano Mentor: Oliver Nina

2. Began Testing with Multiple Networks
Trained 3 Networks with 33%, 50%, 100% randomized dataset
Using the trained models from Cifar10
- Tested on same test batch with different networks to obtain predictions
Applied different techniques for voting
- Voting with labels
- Highest probability from 3 networks

3. Results
Possible randomization issue: networks ran with re- sampling score lower
Voting did increase accuracy

4. Used original data for highest scores
Tried another voting technique
- Adding, averaging, and picking highest
Tested with more than 3 networks

5. Modified code to improve runtime speed
- Allows getting probabilities for multiple networks much faster by implementing it into a function and then multi-threading with a bash script