First Thesis Results
I ran the first full test of the base detection algorithm for my thesis. Against a validation set of 25 positive examples and 25 negative examples (of cows), it achieved 88% recall (12% false negatives) and 100% precision (no false positives). Also, the localization accuracy of the detections was very good: an average of 2% error in object centroid estimate and 90% bounding box overlap.
The validation images, used in training the model, do not make for a very “challenging” set for testing the model, but it is encouraging to see that the model handles these images well. Since this was the very first test, and changes were likely necessary to the thresholds and parameters used in the algorithm—assuming the algorithm itself was indeed written correctly—I was prepared for significantly worse performance.
October 31st, 2006 at 4:58 pm
Nice
The dark ages were caused by the Y1K problem.