| _base_ = './faster-rcnn_r50_fpn_1x_coco.py' | |
| model = dict( | |
| data_preprocessor=dict( | |
| type='DetDataPreprocessor', | |
| mean=[103.530, 116.280, 123.675], | |
| std=[1.0, 1.0, 1.0], | |
| bgr_to_rgb=False, | |
| pad_size_divisor=32), | |
| backbone=dict( | |
| norm_cfg=dict(requires_grad=False), | |
| norm_eval=True, | |
| style='caffe', | |
| init_cfg=dict( | |
| type='Pretrained', | |
| checkpoint='open-mmlab://detectron2/resnet50_caffe'))) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=_base_.backend_args), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='RandomChoiceResize', | |
| scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768), | |
| (1333, 800)], | |
| keep_ratio=True), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PackDetInputs') | |
| ] | |
| # MMEngine support the following two ways, users can choose | |
| # according to convenience | |
| # train_dataloader = dict(dataset=dict(pipeline=train_pipeline)) | |
| _base_.train_dataloader.dataset.pipeline = train_pipeline | |