I train the FSAF and RetinaNet model respectively with default config on single PC, but get a strange result which the mAP of RetinaNet is 28.7% and that of FSAF is 27.0%, but according to your paper, the mAP of FSAF might be 0.2% higher than RetinaNet model.
I train model like that: python tools/train.py configs/retinanet_r50_400_050x.py python tools/train.py configs/fsaf_r50_400_050x.py
run on unbuntu 18.04
python 3.7
Pillow version is 6.1.0
pytorch version is 1.1.0
Hi, I try to train and test on Windows 10, but the mmdetection don't support using on windows. Can you provide a pure pytorch version.
Very appreciate!
I run the command: python tools/test.py
but appear an error :ImportError: cannot import name 'init_dist' from 'mmdet.apis' (/home/yu/Downloads/mmdetection/mmdet/apis/init.py)
how to solve the problem Its very important for me.
TorchVision: 0.4.0a0
OpenCV: 4.2.0
MMCV: 0.4.4
MMDetection: 1.1.0+unknown
MMDetection Compiler: GCC 7.5
MMDetection CUDA Compiler: 10.0
where multi_apply() used the map() function to apply forward_single() to each feature map in feats. However the self.cls_conv, self.reg conv etc. should share their weights across different scale feature map, is this true ?
hi, ty for ur sharing. It's very helpful.
I have some question with lr.
I saw ur GPU_number is 4, num_images_per_GPU is 8. And lr rate is 0.01.
But according to the original mmdetection setting, shouldn't it to be 0.01*2?
(mmdetection GPU=8,num_images_per_GPU=2 lr is 0.01)