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- # @package _global_
- # Model
- model:
- _target_: sam2.modeling.sam2_base.SAM2Base
- image_encoder:
- _target_: sam2.modeling.backbones.image_encoder.ImageEncoder
- scalp: 1
- trunk:
- _target_: sam2.modeling.backbones.hieradet.Hiera
- embed_dim: 112
- num_heads: 2
- neck:
- _target_: sam2.modeling.backbones.image_encoder.FpnNeck
- position_encoding:
- _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
- num_pos_feats: 256
- normalize: true
- scale: null
- temperature: 10000
- d_model: 256
- backbone_channel_list: [896, 448, 224, 112]
- fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features
- fpn_interp_model: nearest
- memory_attention:
- _target_: sam2.modeling.memory_attention.MemoryAttention
- d_model: 256
- pos_enc_at_input: true
- layer:
- _target_: sam2.modeling.memory_attention.MemoryAttentionLayer
- activation: relu
- dim_feedforward: 2048
- dropout: 0.1
- pos_enc_at_attn: false
- self_attention:
- _target_: sam2.modeling.sam.transformer.RoPEAttention
- rope_theta: 10000.0
- feat_sizes: [64, 64]
- embedding_dim: 256
- num_heads: 1
- downsample_rate: 1
- dropout: 0.1
- d_model: 256
- pos_enc_at_cross_attn_keys: true
- pos_enc_at_cross_attn_queries: false
- cross_attention:
- _target_: sam2.modeling.sam.transformer.RoPEAttention
- rope_theta: 10000.0
- feat_sizes: [64, 64]
- rope_k_repeat: True
- embedding_dim: 256
- num_heads: 1
- downsample_rate: 1
- dropout: 0.1
- kv_in_dim: 64
- num_layers: 4
- memory_encoder:
- _target_: sam2.modeling.memory_encoder.MemoryEncoder
- out_dim: 64
- position_encoding:
- _target_: sam2.modeling.position_encoding.PositionEmbeddingSine
- num_pos_feats: 64
- normalize: true
- scale: null
- temperature: 10000
- mask_downsampler:
- _target_: sam2.modeling.memory_encoder.MaskDownSampler
- kernel_size: 3
- stride: 2
- padding: 1
- fuser:
- _target_: sam2.modeling.memory_encoder.Fuser
- layer:
- _target_: sam2.modeling.memory_encoder.CXBlock
- dim: 256
- kernel_size: 7
- padding: 3
- layer_scale_init_value: 1e-6
- use_dwconv: True # depth-wise convs
- num_layers: 2
- num_maskmem: 7
- image_size: 1024
- # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask
- sigmoid_scale_for_mem_enc: 20.0
- sigmoid_bias_for_mem_enc: -10.0
- use_mask_input_as_output_without_sam: true
- # Memory
- directly_add_no_mem_embed: true
- no_obj_embed_spatial: true
- # use high-resolution feature map in the SAM mask decoder
- use_high_res_features_in_sam: true
- # output 3 masks on the first click on initial conditioning frames
- multimask_output_in_sam: true
- # SAM heads
- iou_prediction_use_sigmoid: True
- # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
- use_obj_ptrs_in_encoder: true
- add_tpos_enc_to_obj_ptrs: true
- proj_tpos_enc_in_obj_ptrs: true
- use_signed_tpos_enc_to_obj_ptrs: true
- only_obj_ptrs_in_the_past_for_eval: true
- # object occlusion prediction
- pred_obj_scores: true
- pred_obj_scores_mlp: true
- fixed_no_obj_ptr: true
- # multimask tracking settings
- multimask_output_for_tracking: true
- use_multimask_token_for_obj_ptr: true
- multimask_min_pt_num: 0
- multimask_max_pt_num: 1
- use_mlp_for_obj_ptr_proj: true
- # Compilation flag
- compile_image_encoder: False
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