1. Image Classification via Attribute Detection
Kylie McCarty
Dr. Gong
Abdullah Jamal

2. New Challenges

3. What this means... ​
Getting good accuracy just learning to always predict 0 for every attribute – despite a seemingly good score, not meaningful results if we want to go on to use these attribute predictions for classification
Proposed solutions:
Find more meaningful metric to judge performance
Tweak the model to better learn to predict 1's
AUC = area under

4. How? Weighted Sigmoid Cross Entropy Loss Layer
Give more weight to the positive examples to encourage the net to learn meaningful positive predictions
Additional performance metrics:
AUC: Area Under the ROC Curve
Plots false positive and true positive rate
MAP: Mean Average Precision Score
Area under the precision-recall curve