<reponame>Andrew-Zhu/DyFPN
# optimizer
# optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
# optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)


optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001) #coco
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8, 11])

total_epochs = 12

# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
# optimizer_config = dict(grad_clip=None)
# # learning policy
# lr_config = dict(
#     policy='step',
#     warmup='linear',
#     warmup_iters=500,
#     warmup_ratio=0.001,
#     step=[16, 22])
# total_epochs = 24
# optimizer
# optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
# optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)


optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001) #coco
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8, 11])

total_epochs = 12

# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
# optimizer_config = dict(grad_clip=None)
# # learning policy
# lr_config = dict(
#     policy='step',
#     warmup='linear',
#     warmup_iters=500,
#     warmup_ratio=0.001,
#     step=[16, 22])
# total_epochs = 24
# optimizer # optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) # optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001) #coco # learning policy # optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) # optimizer_config = dict(grad_clip=None) # # learning policy # lr_config = dict( #     policy='step', #     warmup='linear', #     warmup_iters=500, #     warmup_ratio=0.001, #     step=[16, 22]) # total_epochs = 24