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Train on other dataset? #6
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@THTCheng
Hope this will help you. |
Thanks for the detailed instructions. I am able to train on my own dataset now. |
And for test net, change the last parameter in param_str of layer det_scorebbox_7/det_scorebbox_6/det_scorebbox_5/det_scorebbox_4 from 21 to your class number. |
Thanks for sharing the great work @taokong After I changed the parameters above, and changed the number of classes in the "pascal_voc.py" ('background', 'car','bus','truck'), I got the following results: |
@Duankaiwwen |
@taokong ,Hey, much thanks for your great work. During the train, I have some questions if you don't mind. |
Thanks for your reply! but I just evaluated the detection results on dataset, I only changed my dataset to the same format as voc. @taokong |
@taokong ,Hey, much thanks for your great work. After I trained the network and while test the model , I have some questions if you don't mind. I print the shape of all_scores_rpn, all_scores_det and find the shape of all_scores_rpn is (0,1) I wonder if i have trained the model correctly. |
@taokong My datasets only have two types of objects (backgroud car). Objectness prior and bbox regressing are all OK. I have one question, for my tense, Objectness prior can make me know foreground(car), Do I need class-detection layer? |
I think adding the class-detection module could further boost performance, you can have a comparison. |
First of all, thanks for sharing the great work! I am working on object detection on my own dataset but struggle a bit with some dimension parameter in .prototxt. Is is possible to can point out what are the related parameters to "number of classes" in .protxt. (Like https://github.com/deboc/py-faster-rcnn/tree/master/help)
Thank you
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