The latest hog+svm #elephantAI doesn't look so bad just from 0.5*350 positives and 2000 negatives w/out hard negative mining ! Phew!pic.twitter.com/mhu0IJs2IE
• goth gremlin • computational cognitive/neuroscience modeling • geek & techish Cypriot • plant aficionada • came up with #bropenscience • http://neuroplausible.com •
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The latest hog+svm #elephantAI doesn't look so bad just from 0.5*350 positives and 2000 negatives w/out hard negative mining ! Phew!pic.twitter.com/mhu0IJs2IE
The first testing results from this (using n=10 testset of other animals) are 0% false-negatives and 20% false-positives ! #elephantAIpic.twitter.com/zYFTiS4Dq8
False positives are 26% with the n=50 animals testing set, with usual suspects causing problems, + an odd one w the man carrying grass!pic.twitter.com/2NaiwdhMJ0
False positives are 16% with the same testing set used w my object detector trained using 3500 negatives and 330 positives! #elephantAIpic.twitter.com/wrSUlS68Kl
I did a summary here https://hackaday.io/project/20448-elephant-ai/log/58448-summary-of-python-code-for-object-detector-using-histogram-of-oriented-gradients-hog-and-linear-support-vector-machines-svm … it's just python w scikit-learn, scipy, cv2 mainly.
I didn't add code for the HOG tho. I can send u the lot if are interested!
I'm interested but no pressure as I'm unlikely to have time.
ok cool! I will prob rewrite most in next few weeks anyway. That 16% is only from n=50 that I choose myself btw. It's still exciting tho
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