Could you please share some codes and data used to enable edge box proposals in your experiments (the last table in Sect. 4.1 in Page 8 of the NIPS 2016 paper)?
I modified the codes in script_rfcn_VOC0712_ResNet50_OHEM_ss.m, voc0712_trainval_ss.m, and voc2007_test_ss.m by simply substituting "with_selective_search" with "with_edge_box". As there is no "edge_box_data" folder made publicly available, I employed the data provided at https://github.com/gidariss/LocNet. I only have ~11..4% training accuracy ~9% test accuracy in training stage, a pretty large gap with 77.8% reported in your paper.
In LocNet, the edge_box data has the coordinate [y1, x1, y2, x2], but in the ss data used in Fast / Faster RCNN and R-FCN is [x1, y1, x2, y2].
Maybe this difference causes your performance reduce??
Could you please share some codes and data used to enable edge box proposals in your experiments (the last table in Sect. 4.1 in Page 8 of the NIPS 2016 paper)?
I modified the codes in script_rfcn_VOC0712_ResNet50_OHEM_ss.m, voc0712_trainval_ss.m, and voc2007_test_ss.m by simply substituting "with_selective_search" with "with_edge_box". As there is no "edge_box_data" folder made publicly available, I employed the data provided at https://github.com/gidariss/LocNet. I only have ~11..4% training accuracy ~9% test accuracy in training stage, a pretty large gap with 77.8% reported in your paper.