Image classification using cascaded boosting in scikit-learn - why would classification terminate early?

I've pasted all my code here in case you'd need that to understand my question: Plotting a graph on axes but getting no results while trying to classify image based on HoG features

My question is: given approximately 500 images (the Caltech cars dataset) with 48 features each, what possible reasons can there be for the boosting to terminate early? What could cause a perfect fit, or a problem with the boosted sample weights, and how can such problems be solved? The specific algorithm I'm using is SAMME, a multiclass Adaboost classifier. I'm using Python 2.7 on Anaconda.

When I checked certain variables during the classification of my dataset, setting the n_estimators parameter to be 600, I found that:

• discrete test errors: consisted of 1 item instead of being an array of 600 values
• discrete estimator errors: was again one single value instead of of being an array of 600 values
• real test errors is just one item again instead of 600
• discrete estimator weights: array ([1.])
• n_ trees_ discrete and n_trees_real: 1 instead of 600

Why? What am I doing wrong? I'm using Cars 2001 from Caltech as my images, which I've resized to be 20x30. However when I tried them at 240x360, I had the same problem. The classification terminated early.