How to train more accurate Haar Cascades?
I'm developing an app which detects plagues on the agave, and also detects some fungus in microscopic view, such as Fusarium Oxysporum. So, I want to detect small objects as you see (insects and fungus in microscopic view), and I would like to read some advice.
I've created one haar cascade to detect Fusarium Oxysporum, following Sentdex tutorial: https://pythonprogramming.net/haar-ca... but I didn't get good results... it sometimes detects objects in the background, or simply doesn't recognize the object which is supossed to detect.
1.- I've trained it with 17 stages, and with 4000 positive images, and 2000 negative, take into consideration that I only had 18 original positive images, so I created 4000 using create_samples, and I resize negative images to 100x100, and positives to 50x50.
2.-At the moment I used create_samples, I put -w 20 -h 20, and I'm not sure if I should've use higher values
Thank you all :) sorry if some parts are not understandable, I'm still learning English
that's somehow expected. haar cascades are for rigid things only, probably the wrong tool for your job. synthesizing thousands of images from a few only never works here
ohh, that's that shitpost, where he can't detect his own watch in the end ? you should have been warned ....
btw, can you add an example image to your question ?