Learn about Configs — MMDetection 2.21.0 documentation (2024)

{model}_[model setting]_{backbone}_{neck}_[norm setting]_[misc]_[gpu x batch_per_gpu]_{schedule}_{dataset}

# deprecatedmodel = dict( type=..., ...)train_cfg=dict(...)test_cfg=dict(...)

# recommendedmodel = dict( type=..., ... train_cfg=dict(...), test_cfg=dict(...),)

model = dict( type='MaskRCNN', # The name of detector backbone=dict( # The config of backbone type='ResNet', # The type of the backbone, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/backbones/resnet.py#L308 for more details. depth=50, # The depth of backbone, usually it is 50 or 101 for ResNet and ResNext backbones. num_stages=4, # Number of stages of the backbone. out_indices=(0, 1, 2, 3), # The index of output feature maps produced in each stages frozen_stages=1, # The weights in the first 1 stage are fronzen norm_cfg=dict( # The config of normalization layers. type='BN', # Type of norm layer, usually it is BN or GN requires_grad=True), # Whether to train the gamma and beta in BN norm_eval=True, # Whether to freeze the statistics in BN style='pytorch'， # The style of backbone, 'pytorch' means that stride 2 layers are in 3x3 conv, 'caffe' means stride 2 layers are in 1x1 convs. init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), # The ImageNet pretrained backbone to be loaded neck=dict( type='FPN', # The neck of detector is FPN. We also support 'NASFPN', 'PAFPN', etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/necks/fpn.py#L10 for more details. in_channels=[256, 512, 1024, 2048], # The input channels, this is consistent with the output channels of backbone out_channels=256, # The output channels of each level of the pyramid feature map num_outs=5), # The number of output scales rpn_head=dict( type='RPNHead', # The type of RPN head is 'RPNHead', we also support 'GARPNHead', etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/rpn_head.py#L12 for more details. in_channels=256, # The input channels of each input feature map, this is consistent with the output channels of neck feat_channels=256, # Feature channels of convolutional layers in the head. anchor_generator=dict( # The config of anchor generator type='AnchorGenerator', # Most of methods use AnchorGenerator, SSD Detectors uses `SSDAnchorGenerator`. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/anchor/anchor_generator.py#L10 for more details scales=[8], # Basic scale of the anchor, the area of the anchor in one position of a feature map will be scale * base_sizes ratios=[0.5, 1.0, 2.0], # The ratio between height and width. strides=[4, 8, 16, 32, 64]), # The strides of the anchor generator. This is consistent with the FPN feature strides. The strides will be taken as base_sizes if base_sizes is not set. bbox_coder=dict( # Config of box coder to encode and decode the boxes during training and testing type='DeltaXYWHBBoxCoder', # Type of box coder. 'DeltaXYWHBBoxCoder' is applied for most of methods. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py#L9 for more details. target_means=[0.0, 0.0, 0.0, 0.0], # The target means used to encode and decode boxes target_stds=[1.0, 1.0, 1.0, 1.0]), # The standard variance used to encode and decode boxes loss_cls=dict( # Config of loss function for the classification branch type='CrossEntropyLoss', # Type of loss for classification branch, we also support FocalLoss etc. use_sigmoid=True, # RPN usually perform two-class classification, so it usually uses sigmoid function. loss_weight=1.0), # Loss weight of the classification branch. loss_bbox=dict( # Config of loss function for the regression branch. type='L1Loss', # Type of loss, we also support many IoU Losses and smooth L1-loss, etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/smooth_l1_loss.py#L56 for implementation. loss_weight=1.0)), # Loss weight of the regression branch. roi_head=dict( # RoIHead encapsulates the second stage of two-stage/cascade detectors. type='StandardRoIHead', # Type of the RoI head. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/standard_roi_head.py#L10 for implementation. bbox_roi_extractor=dict( # RoI feature extractor for bbox regression. type='SingleRoIExtractor', # Type of the RoI feature extractor, most of methods uses SingleRoIExtractor. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/roi_extractors/single_level.py#L10 for details. roi_layer=dict( # Config of RoI Layer type='RoIAlign', # Type of RoI Layer, DeformRoIPoolingPack and ModulatedDeformRoIPoolingPack are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/roi_align/roi_align.py#L79 for details. output_size=7, # The output size of feature maps. sampling_ratio=0), # Sampling ratio when extracting the RoI features. 0 means adaptive ratio. out_channels=256, # output channels of the extracted feature. featmap_strides=[4, 8, 16, 32]), # Strides of multi-scale feature maps. It should be consistent to the architecture of the backbone. bbox_head=dict( # Config of box head in the RoIHead. type='Shared2FCBBoxHead', # Type of the bbox head, Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py#L177 for implementation details. in_channels=256, # Input channels for bbox head. This is consistent with the out_channels in roi_extractor fc_out_channels=1024, # Output feature channels of FC layers. roi_feat_size=7, # Size of RoI features num_classes=80, # Number of classes for classification bbox_coder=dict( # Box coder used in the second stage. type='DeltaXYWHBBoxCoder', # Type of box coder. 'DeltaXYWHBBoxCoder' is applied for most of methods. target_means=[0.0, 0.0, 0.0, 0.0], # Means used to encode and decode box target_stds=[0.1, 0.1, 0.2, 0.2]), # Standard variance for encoding and decoding. It is smaller since the boxes are more accurate. [0.1, 0.1, 0.2, 0.2] is a conventional setting. reg_class_agnostic=False, # Whether the regression is class agnostic. loss_cls=dict( # Config of loss function for the classification branch type='CrossEntropyLoss', # Type of loss for classification branch, we also support FocalLoss etc. use_sigmoid=False, # Whether to use sigmoid. loss_weight=1.0), # Loss weight of the classification branch. loss_bbox=dict( # Config of loss function for the regression branch. type='L1Loss', # Type of loss, we also support many IoU Losses and smooth L1-loss, etc. loss_weight=1.0)), # Loss weight of the regression branch. mask_roi_extractor=dict( # RoI feature extractor for mask generation. type='SingleRoIExtractor', # Type of the RoI feature extractor, most of methods uses SingleRoIExtractor. roi_layer=dict( # Config of RoI Layer that extracts features for instance segmentation type='RoIAlign', # Type of RoI Layer, DeformRoIPoolingPack and ModulatedDeformRoIPoolingPack are also supported output_size=14, # The output size of feature maps. sampling_ratio=0), # Sampling ratio when extracting the RoI features. out_channels=256, # Output channels of the extracted feature. featmap_strides=[4, 8, 16, 32]), # Strides of multi-scale feature maps. mask_head=dict( # Mask prediction head type='FCNMaskHead', # Type of mask head, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py#L21 for implementation details. num_convs=4, # Number of convolutional layers in mask head. in_channels=256, # Input channels, should be consistent with the output channels of mask roi extractor. conv_out_channels=256, # Output channels of the convolutional layer. num_classes=80, # Number of class to be segmented. loss_mask=dict( # Config of loss function for the mask branch. type='CrossEntropyLoss', # Type of loss used for segmentation use_mask=True, # Whether to only train the mask in the correct class. loss_weight=1.0)))) # Loss weight of mask branch. train_cfg = dict( # Config of training hyperparameters for rpn and rcnn rpn=dict( # Training config of rpn assigner=dict( # Config of assigner type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for many common detectors. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details. pos_iou_thr=0.7, # IoU >= threshold 0.7 will be taken as positive samples neg_iou_thr=0.3, # IoU < threshold 0.3 will be taken as negative samples min_pos_iou=0.3, # The minimal IoU threshold to take boxes as positive samples match_low_quality=True, # Whether to match the boxes under low quality (see API doc for more details). ignore_iof_thr=-1), # IoF threshold for ignoring bboxes sampler=dict( # Config of positive/negative sampler type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details. num=256, # Number of samples pos_fraction=0.5, # The ratio of positive samples in the total samples. neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples. add_gt_as_proposals=False), # Whether add GT as proposals after sampling. allowed_border=-1, # The border allowed after padding for valid anchors. pos_weight=-1, # The weight of positive samples during training. debug=False), # Whether to set the debug mode rpn_proposal=dict( # The config to generate proposals during training nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels. nms_pre=2000, # The number of boxes before NMS nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`. max_per_img=1000, # The number of boxes to be kept after NMS. nms=dict( # Config of NMS type='nms', # Type of NMS iou_threshold=0.7 # NMS threshold ), min_bbox_size=0), # The allowed minimal box size rcnn=dict( # The config for the roi heads. assigner=dict( # Config of assigner for second stage, this is different for that in rpn type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for all roi_heads for now. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details. pos_iou_thr=0.5, # IoU >= threshold 0.5 will be taken as positive samples neg_iou_thr=0.5, # IoU < threshold 0.5 will be taken as negative samples min_pos_iou=0.5, # The minimal IoU threshold to take boxes as positive samples match_low_quality=False, # Whether to match the boxes under low quality (see API doc for more details). ignore_iof_thr=-1), # IoF threshold for ignoring bboxes sampler=dict( type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details. num=512, # Number of samples pos_fraction=0.25, # The ratio of positive samples in the total samples. neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples. add_gt_as_proposals=True ), # Whether add GT as proposals after sampling. mask_size=28, # Size of mask pos_weight=-1, # The weight of positive samples during training. debug=False)) # Whether to set the debug mode test_cfg = dict( # Config for testing hyperparameters for rpn and rcnn rpn=dict( # The config to generate proposals during testing nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels. nms_pre=1000, # The number of boxes before NMS nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`. max_per_img=1000, # The number of boxes to be kept after NMS. nms=dict( # Config of NMS type='nms', #Type of NMS iou_threshold=0.7 # NMS threshold ), min_bbox_size=0), # The allowed minimal box size rcnn=dict( # The config for the roi heads. score_thr=0.05, # Threshold to filter out boxes nms=dict( # Config of NMS in the second stage type='nms', # Type of NMS iou_thr=0.5), # NMS threshold max_per_img=100, # Max number of detections of each image mask_thr_binary=0.5)) # Threshold of mask predictiondataset_type = 'CocoDataset' # Dataset type, this will be used to define the datasetdata_root = 'data/coco/' # Root path of dataimg_norm_cfg = dict( # Image normalization config to normalize the input images mean=[123.675, 116.28, 103.53], # Mean values used to pre-training the pre-trained backbone models std=[58.395, 57.12, 57.375], # Standard variance used to pre-training the pre-trained backbone models to_rgb=True) # The channel orders of image used to pre-training the pre-trained backbone modelstrain_pipeline = [ # Training pipeline dict(type='LoadImageFromFile'), # First pipeline to load images from file path dict( type='LoadAnnotations', # Second pipeline to load annotations for current image with_bbox=True, # Whether to use bounding box, True for detection with_mask=True, # Whether to use instance mask, True for instance segmentation poly2mask=False), # Whether to convert the polygon mask to instance mask, set False for acceleration and to save memory dict( type='Resize', # Augmentation pipeline that resize the images and their annotations img_scale=(1333, 800), # The largest scale of image keep_ratio=True ), # whether to keep the ratio between height and width. dict( type='RandomFlip', # Augmentation pipeline that flip the images and their annotations flip_ratio=0.5), # The ratio or probability to flip dict( type='Normalize', # Augmentation pipeline that normalize the input images mean=[123.675, 116.28, 103.53], # These keys are the same of img_norm_cfg since the std=[58.395, 57.12, 57.375], # keys of img_norm_cfg are used here as arguments to_rgb=True), dict( type='Pad', # Padding config size_divisor=32), # The number the padded images should be divisible dict(type='DefaultFormatBundle'), # Default format bundle to gather data in the pipeline dict( type='Collect', # Pipeline that decides which keys in the data should be passed to the detector keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])]test_pipeline = [ dict(type='LoadImageFromFile'), # First pipeline to load images from file path dict( type='MultiScaleFlipAug', # An encapsulation that encapsulates the testing augmentations img_scale=(1333, 800), # Decides the largest scale for testing, used for the Resize pipeline flip=False, # Whether to flip images during testing transforms=[ dict(type='Resize', # Use resize augmentation keep_ratio=True), # Whether to keep the ratio between height and width, the img_scale set here will be suppressed by the img_scale set above. dict(type='RandomFlip'), # Thought RandomFlip is added in pipeline, it is not used because flip=False dict( type='Normalize', # Normalization config, the values are from img_norm_cfg mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict( type='Pad', # Padding config to pad images divisible by 32. size_divisor=32), dict( type='ImageToTensor', # convert image to tensor keys=['img']), dict( type='Collect', # Collect pipeline that collect necessary keys for testing. keys=['img']) ])]data = dict( samples_per_gpu=2, # Batch size of a single GPU workers_per_gpu=2, # Worker to pre-fetch data for each single GPU train=dict( # Train dataset config type='CocoDataset', # Type of dataset, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/coco.py#L19 for details. ann_file='data/coco/annotations/instances_train2017.json', # Path of annotation file img_prefix='data/coco/train2017/', # Prefix of image path pipeline=[ # pipeline, this is passed by the train_pipeline created before. dict(type='LoadImageFromFile'), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, poly2mask=False), dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict( type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) ]), val=dict( # Validation dataset config type='CocoDataset', ann_file='data/coco/annotations/instances_val2017.json', img_prefix='data/coco/val2017/', pipeline=[ # Pipeline is passed by test_pipeline created before dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( # Test dataset config, modify the ann_file for test-dev/test submission type='CocoDataset', ann_file='data/coco/annotations/instances_val2017.json', img_prefix='data/coco/val2017/', pipeline=[ # Pipeline is passed by test_pipeline created before dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ], samples_per_gpu=2 # Batch size of a single GPU used in testing ))evaluation = dict( # The config to build the evaluation hook, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7 for more details. interval=1, # Evaluation interval metric=['bbox', 'segm']) # Metrics used during evaluationoptimizer = dict( # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch type='SGD', # Type of optimizers, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/optimizer/default_constructor.py#L13 for more details lr=0.02, # Learning rate of optimizers, see detail usages of the parameters in the documentation of PyTorch momentum=0.9, # Momentum weight_decay=0.0001) # Weight decay of SGDoptimizer_config = dict( # Config used to build the optimizer hook, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8 for implementation details. grad_clip=None) # Most of the methods do not use gradient cliplr_config = dict( # Learning rate scheduler config used to register LrUpdater hook policy='step', # The policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9. warmup='linear', # The warmup policy, also support `exp` and `constant`. warmup_iters=500, # The number of iterations for warmup warmup_ratio= 0.001, # The ratio of the starting learning rate used for warmup step=[8, 11]) # Steps to decay the learning raterunner = dict( type='EpochBasedRunner', # Type of runner to use (i.e. IterBasedRunner or EpochBasedRunner) max_epochs=12) # Runner that runs the workflow in total max_epochs. For IterBasedRunner use `max_iters`checkpoint_config = dict( # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation. interval=1) # The save interval is 1log_config = dict( # config to register logger hook interval=50, # Interval to print the log hooks=[ # dict(type='TensorboardLoggerHook') # The Tensorboard logger is also supported dict(type='TextLoggerHook') ]) # The logger used to record the training process.dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set.log_level = 'INFO' # The level of logging.load_from = None # load models as a pre-trained model from a given path. This will not resume training.resume_from = None # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.workflow = [('train', 1)] # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. The workflow trains the model by 12 epochs according to the total_epochs.work_dir = 'work_dir' # Directory to save the model checkpoints and logs for the current experiments.