2020-08-31 01:35:36 -0600 | marked best answer | Improving Haar Cascade results Hey everyone, Second attempt at making a Haar Cascade; initial one I just followed a frontal face tutorial and it worked out pretty good. This time I've tried using Cascade Trainer GUI by amin-ahmadi, with CV2 version 4.1.2 installed, to try train a cascade for foxes. Using ImageNet, I gathered 1,851 positives (of foxes, vulpus vulpus) and 2,796 negatives (trees, cliffs, plants). Used 15 stages, sample width and height of 24, and feature type HAAR. Training roughly took ~6 hours. Results were interesting: The only cascading part of my classifier was the cascading boxes detecting the floor. What could I have done better, any stand out mistakes? I read that maybe I should have cropped the photos and sized positive photos to similar dimensions? Happy to use python and a server in the future rather than a GUI. Would love to just get some rudimentary fox detection working. Thanks in advance! Edit: Putting some more images through it - it seems to detect foxes not too badly; the issue is it does think general background objects are foxes too, like in the photo above. |
2020-08-31 01:35:36 -0600 | received badge | ● Scholar (source) |
2020-08-29 06:09:13 -0600 | commented answer | Improving Haar Cascade results After a lot of reading, I think you're right and I agree. I'm going to go ahead and try train a YOLOv3 model to detect f |
2020-08-27 02:10:44 -0600 | asked a question | Improving Haar Cascade results Improving Haar Cascade results Hey everyone, Second attempt at making a Haar Cascade; initial one I just followed a fro |