detection performance degrade seriously after Decent quantization
Hi everyone.I have encounter a problem when I use resnet50 as SSD backbone. When I trained the network after 20000 iterations in my own dataset(grayscale,11 classes) the detection eval reach 92%, but when I use Decent quantization, after calibration, the test detect_eval is only 8%. The carlibration dataset have 135 images.
I have use resnet50 as SSD backbone to train VOC dataset before, no such degradation happened in the process of Decent quantization, only +/- 5% around the caffe test result.
I have tried the decent weight_bit data_bit method and calib_iter parameters,but the result is still poor, can anyone tell me the reason?