How to reproduce the results in Figure4(b)?
ry-jojo opened this issue · 1 comments
Hi there, thanks for sharing the codebase for such nice work. I was trying to reproduce the results of Figure 4(b) with the following config:
BASE:"../Base-RCNN.yaml"
MODEL:
WEIGHTS:"/Path/to/Base/Pretrain/Weight"
MASK_ON:False
BACKBONE:
FREEZE:True
RESNETS:
DEPTH:101
RPN:
ENABLE_DECOUPLE:True
BACKWARD_SCALE: to be tuned
FREEZE: False
ROI_HEADS:
ENABLE_DECOUPLE:True
BACKWARD_SCALE: to be tuned
NUM_CLASSES:20
FREEZE_FEAT:True
CLS_DROPOUT:True
DATASETS:
TRAIN:("coco14_trainval_novel_10shot_seed0",)
TEST:('coco14_test_novel',)
SOLVER:
IMS_PER_BATCH:16
BASE_LR:0.01
STEPS:(2000,)
MAX_ITER:2500
CHECKPOINT_PERIOD:100000
WARMUP_ITERS:0
TEST:
PCB_ENABLE:False
PCB_MODELPATH:"/Path/to/ImageNet/Pre-Train/Weight"
OUTPUT_DIR:"/Path/to/Output/Dir"
However, when tuning the RPN/ROI_HEAD backward_scale following Figure4(b), the AP seems to be non-sensitive to the scale change:
I think the GDL block is the core idea of this work. I am implementing this exps based on my understanding of the paper, thus I am not sure if my configuration setting is correct or not. Could you please help to give more config details on how to implement the exps for Figure4(b)? Thanks in advance!
I think I have found the answer. I should unfreeze the backbone. I would close this issue now. Thanks!