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facebook-github-bot
539
sam3/train/configs/roboflow_v100/roboflow_v100_eval.yaml
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539
sam3/train/configs/roboflow_v100/roboflow_v100_eval.yaml
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# @package _global_
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defaults:
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- _self_
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# ============================================================================
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# Paths Configuration (Chage this to your own paths)
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# ============================================================================
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paths:
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roboflow_vl_100_root: <YOUR_DATASET_DIR>
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experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>
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bpe_path: <BPE_PATH> # This should be under assets/bpe_simple_vocab_16e6.txt.gz
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# Roboflow dataset configuration
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roboflow_train:
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num_images: 100 # Note: This is the number of images used for training. If null, all images are used.
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supercategory: ${all_roboflow_supercategories.${string:${submitit.job_array.task_index}}}
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# Training transforms pipeline
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train_transforms:
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- _target_: sam3.train.transforms.basic_for_api.ComposeAPI
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transforms:
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- _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries
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query_filter:
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_target_: sam3.train.transforms.filter_query_transforms.FilterCrowds
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- _target_: sam3.train.transforms.point_sampling.RandomizeInputBbox
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box_noise_std: 0.1
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box_noise_max: 20
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- _target_: sam3.train.transforms.segmentation.DecodeRle
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- _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI
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sizes:
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_target_: sam3.train.transforms.basic.get_random_resize_scales
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size: ${scratch.resolution}
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min_size: 480
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rounded: false
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max_size:
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_target_: sam3.train.transforms.basic.get_random_resize_max_size
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size: ${scratch.resolution}
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square: true
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consistent_transform: ${scratch.consistent_transform}
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- _target_: sam3.train.transforms.basic_for_api.PadToSizeAPI
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size: ${scratch.resolution}
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consistent_transform: ${scratch.consistent_transform}
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- _target_: sam3.train.transforms.basic_for_api.ToTensorAPI
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- _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries
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query_filter:
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_target_: sam3.train.transforms.filter_query_transforms.FilterEmptyTargets
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- _target_: sam3.train.transforms.basic_for_api.NormalizeAPI
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mean: ${scratch.train_norm_mean}
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std: ${scratch.train_norm_std}
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- _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries
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query_filter:
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_target_: sam3.train.transforms.filter_query_transforms.FilterEmptyTargets
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- _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries
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query_filter:
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_target_: sam3.train.transforms.filter_query_transforms.FilterFindQueriesWithTooManyOut
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max_num_objects: ${scratch.max_ann_per_img}
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# Validation transforms pipeline
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val_transforms:
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- _target_: sam3.train.transforms.basic_for_api.ComposeAPI
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transforms:
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- _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI
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sizes: ${scratch.resolution}
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max_size:
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_target_: sam3.train.transforms.basic.get_random_resize_max_size
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size: ${scratch.resolution}
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square: true
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consistent_transform: False
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- _target_: sam3.train.transforms.basic_for_api.ToTensorAPI
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- _target_: sam3.train.transforms.basic_for_api.NormalizeAPI
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mean: ${scratch.train_norm_mean}
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std: ${scratch.train_norm_std}
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# loss config (no mask loss)
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loss:
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_target_: sam3.train.loss.sam3_loss.Sam3LossWrapper
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matcher: ${scratch.matcher}
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o2m_weight: 2.0
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o2m_matcher:
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_target_: sam3.train.matcher.BinaryOneToManyMatcher
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alpha: 0.3
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threshold: 0.4
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topk: 4
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use_o2m_matcher_on_o2m_aux: false # Another option is true
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loss_fns_find:
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- _target_: sam3.train.loss.loss_fns.Boxes
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weight_dict:
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loss_bbox: 5.0
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loss_giou: 2.0
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- _target_: sam3.train.loss.loss_fns.IABCEMdetr
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weak_loss: False
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weight_dict:
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loss_ce: 20.0 # Another option is 100.0
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presence_loss: 20.0
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pos_weight: 10.0 # Another option is 5.0
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alpha: 0.25
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gamma: 2
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use_presence: True # Change
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pos_focal: false
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pad_n_queries: 200
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pad_scale_pos: 1.0
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loss_fn_semantic_seg: null
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scale_by_find_batch_size: ${scratch.scale_by_find_batch_size}
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# NOTE: Loss to be used for training in case of segmentation
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# loss:
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# _target_: sam3.train.loss.sam3_loss.Sam3LossWrapper
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# matcher: ${scratch.matcher}
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# o2m_weight: 2.0
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# o2m_matcher:
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# _target_: sam3.train.matcher.BinaryOneToManyMatcher
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# alpha: 0.3
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# threshold: 0.4
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# topk: 4
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# use_o2m_matcher_on_o2m_aux: false
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# loss_fns_find:
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# - _target_: sam3.train.loss.loss_fns.Boxes
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# weight_dict:
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# loss_bbox: 5.0
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# loss_giou: 2.0
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# - _target_: sam3.train.loss.loss_fns.IABCEMdetr
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# weak_loss: False
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# weight_dict:
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# loss_ce: 20.0 # Another option is 100.0
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# presence_loss: 20.0
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# pos_weight: 10.0 # Another option is 5.0
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# alpha: 0.25
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# gamma: 2
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# use_presence: True # Change
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# pos_focal: false
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# pad_n_queries: 200
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# pad_scale_pos: 1.0
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# - _target_: sam3.train.loss.loss_fns.Masks
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# focal_alpha: 0.25
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# focal_gamma: 2.0
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# weight_dict:
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# loss_mask: 200.0
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# loss_dice: 10.0
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# compute_aux: false
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# loss_fn_semantic_seg:
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# _target_: sam3.losses.loss_fns.SemanticSegCriterion
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# presence_head: True
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# presence_loss: False # Change
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# focal: True
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# focal_alpha: 0.6
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# focal_gamma: 2.0
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# downsample: False
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# weight_dict:
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# loss_semantic_seg: 20.0
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# loss_semantic_presence: 1.0
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# loss_semantic_dice: 30.0
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# scale_by_find_batch_size: ${scratch.scale_by_find_batch_size}
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# ============================================================================
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# Different helper parameters and functions
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# ============================================================================
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scratch:
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enable_segmentation: False # NOTE: This is the number of queries used for segmentation
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# Model parameters
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d_model: 256
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pos_embed:
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_target_: sam3.model.position_encoding.PositionEmbeddingSine
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num_pos_feats: ${scratch.d_model}
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normalize: true
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scale: null
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temperature: 10000
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# Box processing
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use_presence_eval: True
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original_box_postprocessor:
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_target_: sam3.eval.postprocessors.PostProcessImage
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max_dets_per_img: -1 # infinite detections
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use_original_ids: true
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use_original_sizes_box: true
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use_presence: ${scratch.use_presence_eval}
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# Matcher configuration
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matcher:
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_target_: sam3.train.matcher.BinaryHungarianMatcherV2
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focal: true # with `focal: true` it is equivalent to BinaryFocalHungarianMatcher
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cost_class: 2.0
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cost_bbox: 5.0
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cost_giou: 2.0
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alpha: 0.25
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gamma: 2
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stable: False
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scale_by_find_batch_size: True
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# Image processing parameters
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resolution: 1008
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consistent_transform: False
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max_ann_per_img: 200
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# Normalization parameters
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train_norm_mean: [0.5, 0.5, 0.5]
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train_norm_std: [0.5, 0.5, 0.5]
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val_norm_mean: [0.5, 0.5, 0.5]
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val_norm_std: [0.5, 0.5, 0.5]
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# Training parameters
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num_train_workers: 10
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num_val_workers: 0
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max_data_epochs: 20
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target_epoch_size: 1500
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hybrid_repeats: 1
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context_length: 2
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gather_pred_via_filesys: false
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# Learning rate and scheduler parameters
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lr_scale: 0.1
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lr_transformer: ${times:8e-4,${scratch.lr_scale}}
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lr_vision_backbone: ${times:2.5e-4,${scratch.lr_scale}}
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lr_language_backbone: ${times:5e-5,${scratch.lr_scale}}
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lrd_vision_backbone: 0.9
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wd: 0.1
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scheduler_timescale: 20
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scheduler_warmup: 20
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scheduler_cooldown: 20
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val_batch_size: 1
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collate_fn_val:
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_target_: sam3.train.data.collator.collate_fn_api
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_partial_: true
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repeats: ${scratch.hybrid_repeats}
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dict_key: roboflow100
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with_seg_masks: ${scratch.enable_segmentation} # Note: Set this to true if using segmentation masks!
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gradient_accumulation_steps: 1
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train_batch_size: 1
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collate_fn:
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_target_: sam3.train.data.collator.collate_fn_api
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_partial_: true
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repeats: ${scratch.hybrid_repeats}
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dict_key: all
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with_seg_masks: ${scratch.enable_segmentation} # Note: Set this to true if using segmentation masks!
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# ============================================================================
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# Trainer Configuration
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# ============================================================================
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trainer:
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_target_: sam3.train.trainer.Trainer
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skip_saving_ckpts: true
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empty_gpu_mem_cache_after_eval: True
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skip_first_val: True
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max_epochs: 20
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accelerator: cuda
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seed_value: 123
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val_epoch_freq: 10
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mode: val
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gradient_accumulation_steps: ${scratch.gradient_accumulation_steps}
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distributed:
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backend: nccl
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find_unused_parameters: True
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gradient_as_bucket_view: True
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loss:
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all: ${roboflow_train.loss}
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default:
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_target_: sam3.train.loss.sam3_loss.DummyLoss
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data:
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train:
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_target_: sam3.train.data.torch_dataset.TorchDataset
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dataset:
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_target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset
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limit_ids: ${roboflow_train.num_images}
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transforms: ${roboflow_train.train_transforms}
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load_segmentation: ${scratch.enable_segmentation}
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max_ann_per_img: 500000
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multiplier: 1
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max_train_queries: 50000
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max_val_queries: 50000
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training: true
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use_caching: False
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img_folder: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/train/
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ann_file: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/train/_annotations.coco.json
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shuffle: True
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batch_size: ${scratch.train_batch_size}
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num_workers: ${scratch.num_train_workers}
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pin_memory: True
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drop_last: True
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collate_fn: ${scratch.collate_fn}
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val:
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_target_: sam3.train.data.torch_dataset.TorchDataset
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dataset:
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_target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset
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load_segmentation: ${scratch.enable_segmentation}
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coco_json_loader:
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_target_: sam3.train.data.coco_json_loaders.COCO_FROM_JSON
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include_negatives: true
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category_chunk_size: 2 # Note: You can increase this based on the memory of your GPU.
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_partial_: true
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img_folder: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/test/
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ann_file: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/test/_annotations.coco.json
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transforms: ${roboflow_train.val_transforms}
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max_ann_per_img: 100000
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multiplier: 1
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training: false
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shuffle: False
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batch_size: ${scratch.val_batch_size}
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num_workers: ${scratch.num_val_workers}
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pin_memory: True
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drop_last: False
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collate_fn: ${scratch.collate_fn_val}
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model:
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_target_: sam3.model_builder.build_sam3_image_model
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bpe_path: ${paths.bpe_path}
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device: cpus
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eval_mode: true
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enable_segmentation: ${scratch.enable_segmentation} # Warning: Enable this if using segmentation.
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meters:
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val:
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roboflow100:
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detection:
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_target_: sam3.eval.coco_writer.PredictionDumper
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iou_type: "bbox"
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dump_dir: ${launcher.experiment_log_dir}/dumps/roboflow/${roboflow_train.supercategory}
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merge_predictions: True
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postprocessor: ${scratch.original_box_postprocessor}
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gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}
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maxdets: 100
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pred_file_evaluators:
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- _target_: sam3.eval.coco_eval_offline.CocoEvaluatorOfflineWithPredFileEvaluators
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gt_path: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/test/_annotations.coco.json
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tide: False
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||||
iou_type: "bbox"
|
||||
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optim:
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||||
amp:
|
||||
enabled: True
|
||||
amp_dtype: bfloat16
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||||
|
||||
optimizer:
|
||||
_target_: torch.optim.AdamW
|
||||
|
||||
gradient_clip:
|
||||
_target_: sam3.train.optim.optimizer.GradientClipper
|
||||
max_norm: 0.1
|
||||
norm_type: 2
|
||||
|
||||
param_group_modifiers:
|
||||
- _target_: sam3.train.optim.optimizer.layer_decay_param_modifier
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||||
_partial_: True
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||||
layer_decay_value: ${scratch.lrd_vision_backbone}
|
||||
apply_to: 'backbone.vision_backbone.trunk'
|
||||
overrides:
|
||||
- pattern: '*pos_embed*'
|
||||
value: 1.0
|
||||
|
||||
options:
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||||
lr:
|
||||
- scheduler: # transformer and class_embed
|
||||
_target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler
|
||||
base_lr: ${scratch.lr_transformer}
|
||||
timescale: ${scratch.scheduler_timescale}
|
||||
warmup_steps: ${scratch.scheduler_warmup}
|
||||
cooldown_steps: ${scratch.scheduler_cooldown}
|
||||
- scheduler:
|
||||
_target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler
|
||||
base_lr: ${scratch.lr_vision_backbone}
|
||||
timescale: ${scratch.scheduler_timescale}
|
||||
warmup_steps: ${scratch.scheduler_warmup}
|
||||
cooldown_steps: ${scratch.scheduler_cooldown}
|
||||
param_names:
|
||||
- 'backbone.vision_backbone.*'
|
||||
- scheduler:
|
||||
_target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler
|
||||
base_lr: ${scratch.lr_language_backbone}
|
||||
timescale: ${scratch.scheduler_timescale}
|
||||
warmup_steps: ${scratch.scheduler_warmup}
|
||||
cooldown_steps: ${scratch.scheduler_cooldown}
|
||||
param_names:
|
||||
- 'backbone.language_backbone.*'
|
||||
|
||||
weight_decay:
|
||||
- scheduler:
|
||||
_target_: fvcore.common.param_scheduler.ConstantParamScheduler
|
||||
value: ${scratch.wd}
|
||||
- scheduler:
|
||||
_target_: fvcore.common.param_scheduler.ConstantParamScheduler
|
||||
value: 0.0
|
||||
param_names:
|
||||
- '*bias*'
|
||||
module_cls_names: ['torch.nn.LayerNorm']
|
||||
|
||||
checkpoint:
|
||||
save_dir: ${launcher.experiment_log_dir}/checkpoints
|
||||
save_freq: 0 # 0 only last checkpoint is saved.
|
||||
|
||||
logging:
|
||||
tensorboard_writer:
|
||||
_target_: sam3.train.utils.logger.make_tensorboard_logger
|
||||
log_dir: ${launcher.experiment_log_dir}/tensorboard
|
||||
flush_secs: 120
|
||||
should_log: True
|
||||
wandb_writer: null
|
||||
log_dir: ${launcher.experiment_log_dir}/logs/${roboflow_train.supercategory}
|
||||
log_freq: 10
|
||||
|
||||
# ============================================================================
|
||||
# Launcher and Submitit Configuration
|
||||
# ============================================================================
|
||||
|
||||
launcher:
|
||||
num_nodes: 1
|
||||
gpus_per_node: 2
|
||||
experiment_log_dir: ${paths.experiment_log_dir}
|
||||
multiprocessing_context: forkserver
|
||||
|
||||
submitit:
|
||||
account: null
|
||||
partition: null
|
||||
qos: null
|
||||
timeout_hour: 72
|
||||
use_cluster: True
|
||||
cpus_per_task: 10
|
||||
port_range: [10000, 65000]
|
||||
constraint: null
|
||||
# Uncomment for job array configuration
|
||||
job_array:
|
||||
num_tasks: 100
|
||||
task_index: 0
|
||||
|
||||
# ============================================================================
|
||||
# Available Roboflow Supercategories (for reference)
|
||||
# ============================================================================
|
||||
|
||||
all_roboflow_supercategories:
|
||||
- -grccs
|
||||
- zebrasatasturias
|
||||
- cod-mw-warzone
|
||||
- canalstenosis
|
||||
- label-printing-defect-version-2
|
||||
- new-defects-in-wood
|
||||
- orionproducts
|
||||
- aquarium-combined
|
||||
- varroa-mites-detection--test-set
|
||||
- clashroyalechardetector
|
||||
- stomata-cells
|
||||
- halo-infinite-angel-videogame
|
||||
- pig-detection
|
||||
- urine-analysis1
|
||||
- aerial-sheep
|
||||
- orgharvest
|
||||
- actions
|
||||
- mahjong
|
||||
- liver-disease
|
||||
- needle-base-tip-min-max
|
||||
- wheel-defect-detection
|
||||
- aircraft-turnaround-dataset
|
||||
- xray
|
||||
- wildfire-smoke
|
||||
- spinefrxnormalvindr
|
||||
- ufba-425
|
||||
- speech-bubbles-detection
|
||||
- train
|
||||
- pill
|
||||
- truck-movement
|
||||
- car-logo-detection
|
||||
- inbreast
|
||||
- sea-cucumbers-new-tiles
|
||||
- uavdet-small
|
||||
- penguin-finder-seg
|
||||
- aerial-airport
|
||||
- bibdetection
|
||||
- taco-trash-annotations-in-context
|
||||
- bees
|
||||
- recode-waste
|
||||
- screwdetectclassification
|
||||
- wine-labels
|
||||
- aerial-cows
|
||||
- into-the-vale
|
||||
- gwhd2021
|
||||
- lacrosse-object-detection
|
||||
- defect-detection
|
||||
- dataconvert
|
||||
- x-ray-id
|
||||
- ball
|
||||
- tube
|
||||
- 2024-frc
|
||||
- crystal-clean-brain-tumors-mri-dataset
|
||||
- grapes-5
|
||||
- human-detection-in-floods
|
||||
- buoy-onboarding
|
||||
- apoce-aerial-photographs-for-object-detection-of-construction-equipment
|
||||
- l10ul502
|
||||
- floating-waste
|
||||
- deeppcb
|
||||
- ism-band-packet-detection
|
||||
- weeds4
|
||||
- invoice-processing
|
||||
- thermal-cheetah
|
||||
- tomatoes-2
|
||||
- marine-sharks
|
||||
- peixos-fish
|
||||
- sssod
|
||||
- aerial-pool
|
||||
- countingpills
|
||||
- asphaltdistressdetection
|
||||
- roboflow-trained-dataset
|
||||
- everdaynew
|
||||
- underwater-objects
|
||||
- soda-bottles
|
||||
- dentalai
|
||||
- jellyfish
|
||||
- deepfruits
|
||||
- activity-diagrams
|
||||
- circuit-voltages
|
||||
- all-elements
|
||||
- macro-segmentation
|
||||
- exploratorium-daphnia
|
||||
- signatures
|
||||
- conveyor-t-shirts
|
||||
- fruitjes
|
||||
- grass-weeds
|
||||
- infraredimageofpowerequipment
|
||||
- 13-lkc01
|
||||
- wb-prova
|
||||
- flir-camera-objects
|
||||
- paper-parts
|
||||
- football-player-detection
|
||||
- trail-camera
|
||||
- smd-components
|
||||
- water-meter
|
||||
- nih-xray
|
||||
- the-dreidel-project
|
||||
- electric-pylon-detection-in-rsi
|
||||
- cable-damage
|
||||
@@ -0,0 +1,539 @@
|
||||
# @package _global_
|
||||
defaults:
|
||||
- _self_
|
||||
|
||||
# ============================================================================
|
||||
# Paths Configuration (Chage this to your own paths)
|
||||
# ============================================================================
|
||||
paths:
|
||||
roboflow_vl_100_root: <YOUR_DATASET_DIR>
|
||||
experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>
|
||||
bpe_path: <BPE_PATH> # This should be under assets/bpe_simple_vocab_16e6.txt.gz
|
||||
|
||||
# Roboflow dataset configuration
|
||||
roboflow_train:
|
||||
num_images: 100 # Note: This is the number of images used for training. If null, all images are used.
|
||||
supercategory: ${all_roboflow_supercategories.${string:${submitit.job_array.task_index}}}
|
||||
|
||||
# Training transforms pipeline
|
||||
train_transforms:
|
||||
- _target_: sam3.train.transforms.basic_for_api.ComposeAPI
|
||||
transforms:
|
||||
- _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries
|
||||
query_filter:
|
||||
_target_: sam3.train.transforms.filter_query_transforms.FilterCrowds
|
||||
- _target_: sam3.train.transforms.point_sampling.RandomizeInputBbox
|
||||
box_noise_std: 0.1
|
||||
box_noise_max: 20
|
||||
- _target_: sam3.train.transforms.segmentation.DecodeRle
|
||||
- _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI
|
||||
sizes:
|
||||
_target_: sam3.train.transforms.basic.get_random_resize_scales
|
||||
size: ${scratch.resolution}
|
||||
min_size: 480
|
||||
rounded: false
|
||||
max_size:
|
||||
_target_: sam3.train.transforms.basic.get_random_resize_max_size
|
||||
size: ${scratch.resolution}
|
||||
square: true
|
||||
consistent_transform: ${scratch.consistent_transform}
|
||||
- _target_: sam3.train.transforms.basic_for_api.PadToSizeAPI
|
||||
size: ${scratch.resolution}
|
||||
consistent_transform: ${scratch.consistent_transform}
|
||||
- _target_: sam3.train.transforms.basic_for_api.ToTensorAPI
|
||||
- _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries
|
||||
query_filter:
|
||||
_target_: sam3.train.transforms.filter_query_transforms.FilterEmptyTargets
|
||||
- _target_: sam3.train.transforms.basic_for_api.NormalizeAPI
|
||||
mean: ${scratch.train_norm_mean}
|
||||
std: ${scratch.train_norm_std}
|
||||
- _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries
|
||||
query_filter:
|
||||
_target_: sam3.train.transforms.filter_query_transforms.FilterEmptyTargets
|
||||
- _target_: sam3.train.transforms.filter_query_transforms.FlexibleFilterFindGetQueries
|
||||
query_filter:
|
||||
_target_: sam3.train.transforms.filter_query_transforms.FilterFindQueriesWithTooManyOut
|
||||
max_num_objects: ${scratch.max_ann_per_img}
|
||||
|
||||
# Validation transforms pipeline
|
||||
val_transforms:
|
||||
- _target_: sam3.train.transforms.basic_for_api.ComposeAPI
|
||||
transforms:
|
||||
- _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI
|
||||
sizes: ${scratch.resolution}
|
||||
max_size:
|
||||
_target_: sam3.train.transforms.basic.get_random_resize_max_size
|
||||
size: ${scratch.resolution}
|
||||
square: true
|
||||
consistent_transform: False
|
||||
- _target_: sam3.train.transforms.basic_for_api.ToTensorAPI
|
||||
- _target_: sam3.train.transforms.basic_for_api.NormalizeAPI
|
||||
mean: ${scratch.train_norm_mean}
|
||||
std: ${scratch.train_norm_std}
|
||||
|
||||
# loss config (no mask loss)
|
||||
loss:
|
||||
_target_: sam3.train.loss.sam3_loss.Sam3LossWrapper
|
||||
matcher: ${scratch.matcher}
|
||||
o2m_weight: 2.0
|
||||
o2m_matcher:
|
||||
_target_: sam3.train.matcher.BinaryOneToManyMatcher
|
||||
alpha: 0.3
|
||||
threshold: 0.4
|
||||
topk: 4
|
||||
use_o2m_matcher_on_o2m_aux: false # Another option is true
|
||||
loss_fns_find:
|
||||
- _target_: sam3.train.loss.loss_fns.Boxes
|
||||
weight_dict:
|
||||
loss_bbox: 5.0
|
||||
loss_giou: 2.0
|
||||
- _target_: sam3.train.loss.loss_fns.IABCEMdetr
|
||||
weak_loss: False
|
||||
weight_dict:
|
||||
loss_ce: 20.0 # Another option is 100.0
|
||||
presence_loss: 20.0
|
||||
pos_weight: 10.0 # Another option is 5.0
|
||||
alpha: 0.25
|
||||
gamma: 2
|
||||
use_presence: True # Change
|
||||
pos_focal: false
|
||||
pad_n_queries: 200
|
||||
pad_scale_pos: 1.0
|
||||
|
||||
loss_fn_semantic_seg: null
|
||||
scale_by_find_batch_size: ${scratch.scale_by_find_batch_size}
|
||||
|
||||
|
||||
# NOTE: Loss to be used for training in case of segmentation
|
||||
# loss:
|
||||
# _target_: sam3.train.loss.sam3_loss.Sam3LossWrapper
|
||||
# matcher: ${scratch.matcher}
|
||||
# o2m_weight: 2.0
|
||||
# o2m_matcher:
|
||||
# _target_: sam3.train.matcher.BinaryOneToManyMatcher
|
||||
# alpha: 0.3
|
||||
# threshold: 0.4
|
||||
# topk: 4
|
||||
# use_o2m_matcher_on_o2m_aux: false
|
||||
# loss_fns_find:
|
||||
# - _target_: sam3.train.loss.loss_fns.Boxes
|
||||
# weight_dict:
|
||||
# loss_bbox: 5.0
|
||||
# loss_giou: 2.0
|
||||
# - _target_: sam3.train.loss.loss_fns.IABCEMdetr
|
||||
# weak_loss: False
|
||||
# weight_dict:
|
||||
# loss_ce: 20.0 # Another option is 100.0
|
||||
# presence_loss: 20.0
|
||||
# pos_weight: 10.0 # Another option is 5.0
|
||||
# alpha: 0.25
|
||||
# gamma: 2
|
||||
# use_presence: True # Change
|
||||
# pos_focal: false
|
||||
# pad_n_queries: 200
|
||||
# pad_scale_pos: 1.0
|
||||
# - _target_: sam3.train.loss.loss_fns.Masks
|
||||
# focal_alpha: 0.25
|
||||
# focal_gamma: 2.0
|
||||
# weight_dict:
|
||||
# loss_mask: 200.0
|
||||
# loss_dice: 10.0
|
||||
# compute_aux: false
|
||||
# loss_fn_semantic_seg:
|
||||
# _target_: sam3.losses.loss_fns.SemanticSegCriterion
|
||||
# presence_head: True
|
||||
# presence_loss: False # Change
|
||||
# focal: True
|
||||
# focal_alpha: 0.6
|
||||
# focal_gamma: 2.0
|
||||
# downsample: False
|
||||
# weight_dict:
|
||||
# loss_semantic_seg: 20.0
|
||||
# loss_semantic_presence: 1.0
|
||||
# loss_semantic_dice: 30.0
|
||||
# scale_by_find_batch_size: ${scratch.scale_by_find_batch_size}
|
||||
|
||||
# ============================================================================
|
||||
# Different helper parameters and functions
|
||||
# ============================================================================
|
||||
scratch:
|
||||
enable_segmentation: False # NOTE: This is the number of queries used for segmentation
|
||||
# Model parameters
|
||||
d_model: 256
|
||||
pos_embed:
|
||||
_target_: sam3.model.position_encoding.PositionEmbeddingSine
|
||||
num_pos_feats: ${scratch.d_model}
|
||||
normalize: true
|
||||
scale: null
|
||||
temperature: 10000
|
||||
|
||||
# Box processing
|
||||
use_presence_eval: True
|
||||
original_box_postprocessor:
|
||||
_target_: sam3.eval.postprocessors.PostProcessImage
|
||||
max_dets_per_img: -1 # infinite detections
|
||||
use_original_ids: true
|
||||
use_original_sizes_box: true
|
||||
use_presence: ${scratch.use_presence_eval}
|
||||
|
||||
# Matcher configuration
|
||||
matcher:
|
||||
_target_: sam3.train.matcher.BinaryHungarianMatcherV2
|
||||
focal: true # with `focal: true` it is equivalent to BinaryFocalHungarianMatcher
|
||||
cost_class: 2.0
|
||||
cost_bbox: 5.0
|
||||
cost_giou: 2.0
|
||||
alpha: 0.25
|
||||
gamma: 2
|
||||
stable: False
|
||||
scale_by_find_batch_size: True
|
||||
|
||||
# Image processing parameters
|
||||
resolution: 1008
|
||||
consistent_transform: False
|
||||
max_ann_per_img: 200
|
||||
|
||||
# Normalization parameters
|
||||
train_norm_mean: [0.5, 0.5, 0.5]
|
||||
train_norm_std: [0.5, 0.5, 0.5]
|
||||
val_norm_mean: [0.5, 0.5, 0.5]
|
||||
val_norm_std: [0.5, 0.5, 0.5]
|
||||
|
||||
# Training parameters
|
||||
num_train_workers: 10
|
||||
num_val_workers: 0
|
||||
max_data_epochs: 20
|
||||
target_epoch_size: 1500
|
||||
hybrid_repeats: 1
|
||||
context_length: 2
|
||||
gather_pred_via_filesys: false
|
||||
|
||||
# Learning rate and scheduler parameters
|
||||
lr_scale: 0.1
|
||||
lr_transformer: ${times:8e-4,${scratch.lr_scale}}
|
||||
lr_vision_backbone: ${times:2.5e-4,${scratch.lr_scale}}
|
||||
lr_language_backbone: ${times:5e-5,${scratch.lr_scale}}
|
||||
lrd_vision_backbone: 0.9
|
||||
wd: 0.1
|
||||
scheduler_timescale: 20
|
||||
scheduler_warmup: 20
|
||||
scheduler_cooldown: 20
|
||||
|
||||
val_batch_size: 1
|
||||
collate_fn_val:
|
||||
_target_: sam3.train.data.collator.collate_fn_api
|
||||
_partial_: true
|
||||
repeats: ${scratch.hybrid_repeats}
|
||||
dict_key: roboflow100
|
||||
with_seg_masks: ${scratch.enable_segmentation} # Note: Set this to true if using segmentation masks!
|
||||
|
||||
gradient_accumulation_steps: 1
|
||||
train_batch_size: 1
|
||||
collate_fn:
|
||||
_target_: sam3.train.data.collator.collate_fn_api
|
||||
_partial_: true
|
||||
repeats: ${scratch.hybrid_repeats}
|
||||
dict_key: all
|
||||
with_seg_masks: ${scratch.enable_segmentation} # Note: Set this to true if using segmentation masks!
|
||||
|
||||
# ============================================================================
|
||||
# Trainer Configuration
|
||||
# ============================================================================
|
||||
|
||||
trainer:
|
||||
|
||||
_target_: sam3.train.trainer.Trainer
|
||||
skip_saving_ckpts: true
|
||||
empty_gpu_mem_cache_after_eval: True
|
||||
skip_first_val: True
|
||||
max_epochs: 20
|
||||
accelerator: cuda
|
||||
seed_value: 123
|
||||
val_epoch_freq: 10
|
||||
mode: train
|
||||
gradient_accumulation_steps: ${scratch.gradient_accumulation_steps}
|
||||
|
||||
distributed:
|
||||
backend: nccl
|
||||
find_unused_parameters: True
|
||||
gradient_as_bucket_view: True
|
||||
|
||||
loss:
|
||||
all: ${roboflow_train.loss}
|
||||
default:
|
||||
_target_: sam3.train.loss.sam3_loss.DummyLoss
|
||||
|
||||
data:
|
||||
train:
|
||||
_target_: sam3.train.data.torch_dataset.TorchDataset
|
||||
dataset:
|
||||
_target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset
|
||||
limit_ids: ${roboflow_train.num_images}
|
||||
transforms: ${roboflow_train.train_transforms}
|
||||
load_segmentation: ${scratch.enable_segmentation}
|
||||
max_ann_per_img: 500000
|
||||
multiplier: 1
|
||||
max_train_queries: 50000
|
||||
max_val_queries: 50000
|
||||
training: true
|
||||
use_caching: False
|
||||
img_folder: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/train/
|
||||
ann_file: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/train/_annotations.coco.json
|
||||
|
||||
shuffle: True
|
||||
batch_size: ${scratch.train_batch_size}
|
||||
num_workers: ${scratch.num_train_workers}
|
||||
pin_memory: True
|
||||
drop_last: True
|
||||
collate_fn: ${scratch.collate_fn}
|
||||
|
||||
val:
|
||||
_target_: sam3.train.data.torch_dataset.TorchDataset
|
||||
dataset:
|
||||
_target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset
|
||||
load_segmentation: ${scratch.enable_segmentation}
|
||||
coco_json_loader:
|
||||
_target_: sam3.train.data.coco_json_loaders.COCO_FROM_JSON
|
||||
include_negatives: true
|
||||
category_chunk_size: 2 # Note: You can increase this based on the memory of your GPU.
|
||||
_partial_: true
|
||||
img_folder: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/test/
|
||||
ann_file: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/test/_annotations.coco.json
|
||||
transforms: ${roboflow_train.val_transforms}
|
||||
max_ann_per_img: 100000
|
||||
multiplier: 1
|
||||
training: false
|
||||
|
||||
shuffle: False
|
||||
batch_size: ${scratch.val_batch_size}
|
||||
num_workers: ${scratch.num_val_workers}
|
||||
pin_memory: True
|
||||
drop_last: False
|
||||
collate_fn: ${scratch.collate_fn_val}
|
||||
|
||||
|
||||
model:
|
||||
_target_: sam3.model_builder.build_sam3_image_model
|
||||
bpe_path: ${paths.bpe_path}
|
||||
device: cpus
|
||||
eval_mode: false
|
||||
enable_segmentation: ${scratch.enable_segmentation} # Warning: Enable this if using segmentation.
|
||||
|
||||
meters:
|
||||
val:
|
||||
roboflow100:
|
||||
detection:
|
||||
_target_: sam3.eval.coco_writer.PredictionDumper
|
||||
iou_type: "bbox"
|
||||
dump_dir: ${launcher.experiment_log_dir}/dumps/roboflow/${roboflow_train.supercategory}
|
||||
merge_predictions: True
|
||||
postprocessor: ${scratch.original_box_postprocessor}
|
||||
gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}
|
||||
maxdets: 100
|
||||
pred_file_evaluators:
|
||||
- _target_: sam3.eval.coco_eval_offline.CocoEvaluatorOfflineWithPredFileEvaluators
|
||||
gt_path: ${paths.roboflow_vl_100_root}/${roboflow_train.supercategory}/test/_annotations.coco.json
|
||||
tide: False
|
||||
iou_type: "bbox"
|
||||
|
||||
optim:
|
||||
amp:
|
||||
enabled: True
|
||||
amp_dtype: bfloat16
|
||||
|
||||
optimizer:
|
||||
_target_: torch.optim.AdamW
|
||||
|
||||
gradient_clip:
|
||||
_target_: sam3.train.optim.optimizer.GradientClipper
|
||||
max_norm: 0.1
|
||||
norm_type: 2
|
||||
|
||||
param_group_modifiers:
|
||||
- _target_: sam3.train.optim.optimizer.layer_decay_param_modifier
|
||||
_partial_: True
|
||||
layer_decay_value: ${scratch.lrd_vision_backbone}
|
||||
apply_to: 'backbone.vision_backbone.trunk'
|
||||
overrides:
|
||||
- pattern: '*pos_embed*'
|
||||
value: 1.0
|
||||
|
||||
options:
|
||||
lr:
|
||||
- scheduler: # transformer and class_embed
|
||||
_target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler
|
||||
base_lr: ${scratch.lr_transformer}
|
||||
timescale: ${scratch.scheduler_timescale}
|
||||
warmup_steps: ${scratch.scheduler_warmup}
|
||||
cooldown_steps: ${scratch.scheduler_cooldown}
|
||||
- scheduler:
|
||||
_target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler
|
||||
base_lr: ${scratch.lr_vision_backbone}
|
||||
timescale: ${scratch.scheduler_timescale}
|
||||
warmup_steps: ${scratch.scheduler_warmup}
|
||||
cooldown_steps: ${scratch.scheduler_cooldown}
|
||||
param_names:
|
||||
- 'backbone.vision_backbone.*'
|
||||
- scheduler:
|
||||
_target_: sam3.train.optim.schedulers.InverseSquareRootParamScheduler
|
||||
base_lr: ${scratch.lr_language_backbone}
|
||||
timescale: ${scratch.scheduler_timescale}
|
||||
warmup_steps: ${scratch.scheduler_warmup}
|
||||
cooldown_steps: ${scratch.scheduler_cooldown}
|
||||
param_names:
|
||||
- 'backbone.language_backbone.*'
|
||||
|
||||
weight_decay:
|
||||
- scheduler:
|
||||
_target_: fvcore.common.param_scheduler.ConstantParamScheduler
|
||||
value: ${scratch.wd}
|
||||
- scheduler:
|
||||
_target_: fvcore.common.param_scheduler.ConstantParamScheduler
|
||||
value: 0.0
|
||||
param_names:
|
||||
- '*bias*'
|
||||
module_cls_names: ['torch.nn.LayerNorm']
|
||||
|
||||
checkpoint:
|
||||
save_dir: ${launcher.experiment_log_dir}/checkpoints
|
||||
save_freq: 0 # 0 only last checkpoint is saved.
|
||||
|
||||
logging:
|
||||
tensorboard_writer:
|
||||
_target_: sam3.train.utils.logger.make_tensorboard_logger
|
||||
log_dir: ${launcher.experiment_log_dir}/tensorboard
|
||||
flush_secs: 120
|
||||
should_log: True
|
||||
wandb_writer: null
|
||||
log_dir: ${launcher.experiment_log_dir}/logs/${roboflow_train.supercategory}
|
||||
log_freq: 10
|
||||
|
||||
# ============================================================================
|
||||
# Launcher and Submitit Configuration
|
||||
# ============================================================================
|
||||
|
||||
launcher:
|
||||
num_nodes: 1
|
||||
gpus_per_node: 2
|
||||
experiment_log_dir: ${paths.experiment_log_dir}
|
||||
multiprocessing_context: forkserver
|
||||
|
||||
submitit:
|
||||
account: null
|
||||
partition: null
|
||||
qos: null
|
||||
timeout_hour: 72
|
||||
use_cluster: True
|
||||
cpus_per_task: 10
|
||||
port_range: [10000, 65000]
|
||||
constraint: null
|
||||
# Uncomment for job array configuration
|
||||
job_array:
|
||||
num_tasks: 100
|
||||
task_index: 0
|
||||
|
||||
# ============================================================================
|
||||
# Available Roboflow Supercategories (for reference)
|
||||
# ============================================================================
|
||||
|
||||
all_roboflow_supercategories:
|
||||
- -grccs
|
||||
- zebrasatasturias
|
||||
- cod-mw-warzone
|
||||
- canalstenosis
|
||||
- label-printing-defect-version-2
|
||||
- new-defects-in-wood
|
||||
- orionproducts
|
||||
- aquarium-combined
|
||||
- varroa-mites-detection--test-set
|
||||
- clashroyalechardetector
|
||||
- stomata-cells
|
||||
- halo-infinite-angel-videogame
|
||||
- pig-detection
|
||||
- urine-analysis1
|
||||
- aerial-sheep
|
||||
- orgharvest
|
||||
- actions
|
||||
- mahjong
|
||||
- liver-disease
|
||||
- needle-base-tip-min-max
|
||||
- wheel-defect-detection
|
||||
- aircraft-turnaround-dataset
|
||||
- xray
|
||||
- wildfire-smoke
|
||||
- spinefrxnormalvindr
|
||||
- ufba-425
|
||||
- speech-bubbles-detection
|
||||
- train
|
||||
- pill
|
||||
- truck-movement
|
||||
- car-logo-detection
|
||||
- inbreast
|
||||
- sea-cucumbers-new-tiles
|
||||
- uavdet-small
|
||||
- penguin-finder-seg
|
||||
- aerial-airport
|
||||
- bibdetection
|
||||
- taco-trash-annotations-in-context
|
||||
- bees
|
||||
- recode-waste
|
||||
- screwdetectclassification
|
||||
- wine-labels
|
||||
- aerial-cows
|
||||
- into-the-vale
|
||||
- gwhd2021
|
||||
- lacrosse-object-detection
|
||||
- defect-detection
|
||||
- dataconvert
|
||||
- x-ray-id
|
||||
- ball
|
||||
- tube
|
||||
- 2024-frc
|
||||
- crystal-clean-brain-tumors-mri-dataset
|
||||
- grapes-5
|
||||
- human-detection-in-floods
|
||||
- buoy-onboarding
|
||||
- apoce-aerial-photographs-for-object-detection-of-construction-equipment
|
||||
- l10ul502
|
||||
- floating-waste
|
||||
- deeppcb
|
||||
- ism-band-packet-detection
|
||||
- weeds4
|
||||
- invoice-processing
|
||||
- thermal-cheetah
|
||||
- tomatoes-2
|
||||
- marine-sharks
|
||||
- peixos-fish
|
||||
- sssod
|
||||
- aerial-pool
|
||||
- countingpills
|
||||
- asphaltdistressdetection
|
||||
- roboflow-trained-dataset
|
||||
- everdaynew
|
||||
- underwater-objects
|
||||
- soda-bottles
|
||||
- dentalai
|
||||
- jellyfish
|
||||
- deepfruits
|
||||
- activity-diagrams
|
||||
- circuit-voltages
|
||||
- all-elements
|
||||
- macro-segmentation
|
||||
- exploratorium-daphnia
|
||||
- signatures
|
||||
- conveyor-t-shirts
|
||||
- fruitjes
|
||||
- grass-weeds
|
||||
- infraredimageofpowerequipment
|
||||
- 13-lkc01
|
||||
- wb-prova
|
||||
- flir-camera-objects
|
||||
- paper-parts
|
||||
- football-player-detection
|
||||
- trail-camera
|
||||
- smd-components
|
||||
- water-meter
|
||||
- nih-xray
|
||||
- the-dreidel-project
|
||||
- electric-pylon-detection-in-rsi
|
||||
- cable-damage
|
||||
Reference in New Issue
Block a user