mmdetection/configs/pascal_voc/faster-rcnn_r50_fpn_1x_voc0712-cocofmt.py

_base_ = [
    '../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
    '../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))

METAINFO = {
    'classes':
    ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
     'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person',
     'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'),
    # palette is a list of color tuples, which is used for visualization.
    'palette': [(106, 0, 228), (119, 11, 32), (165, 42, 42), (0, 0, 192),
                (197, 226, 255), (0, 60, 100), (0, 0, 142), (255, 77, 255),
                (153, 69, 1), (120, 166, 157), (0, 182, 199), (0, 226, 252),
                (182, 182, 255), (0, 0, 230), (220, 20, 60), (163, 255, 0),
                (0, 82, 0), (3, 95, 161), (0, 80, 100), (183, 130, 88)]
}

# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/VOCdevkit/'

train_pipeline = [
    dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', scale=(1000, 600), keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
    dict(type='Resize', scale=(1000, 600), keep_ratio=True),
    # avoid bboxes being resized
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
train_dataloader = dict(
    dataset=dict(
        type='RepeatDataset',
        times=3,
        dataset=dict(
            _delete_=True,
            type=dataset_type,
            data_root=data_root,
            ann_file='annotations/voc0712_trainval.json',
            data_prefix=dict(img=''),
            metainfo=METAINFO,
            filter_cfg=dict(filter_empty_gt=True, min_size=32),
            pipeline=train_pipeline,
            backend_args={{_base_.backend_args}})))
val_dataloader = dict(
    dataset=dict(
        type=dataset_type,
        ann_file='annotations/voc07_test.json',
        data_prefix=dict(img=''),
        metainfo=METAINFO,
        pipeline=test_pipeline))
test_dataloader = val_dataloader

val_evaluator = dict(
    type='CocoMetric',
    ann_file=data_root + 'annotations/voc07_test.json',
    metric='bbox',
    format_only=False,
    backend_args={{_base_.backend_args}})
test_evaluator = val_evaluator

# training schedule, the dataset is repeated 3 times, so the
# actual epoch = 4 * 3 = 12
max_epochs = 4
train_cfg = dict(
    type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

# learning rate
param_scheduler = [
    dict(
        type='MultiStepLR',
        begin=0,
        end=max_epochs,
        by_epoch=True,
        milestones=[3],
        gamma=0.1)
]

# optimizer
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))

# Default setting for scaling LR automatically
#   - `enable` means enable scaling LR automatically
#       or not by default.
#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)