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Hi, I found that there is no config of large backbone and 4x resolution about ConQueR in the repo. Can you share the config? Thanks.
The text was updated successfully, but these errors were encountered:
I wrote a large config myself. Is it correct?
model: weights: null # common variables hidden_dim: 256 aux_loss: true loss: bbox_loss_coef: 4 giou_loss_coef: 2 class_loss_coef: 1 rad_loss_coef: 4 matcher: class_weight: ${model.loss.class_loss_coef} bbox_weight: ${model.loss.bbox_loss_coef} giou_weight: ${model.loss.giou_loss_coef} rad_weight: ${model.loss.rad_loss_coef} metrics: - type: accuracy params: {} sparse_resnets: # num_classes: 1000 depth: 18 out_features: [res2, res3, res4] num_groups: 1 # Options: FrozenBN, GN, "SyncBN", "BN" norm: BN1d activation: type: ReLU inplace: True # zero_init_residual: True width_per_group: 128 # stride_in_1x1: False # res5_dilation: 1 res1_out_channels: 128 stem_out_channels: 64 fpn: in_features: [res2, res3, res4] top_block_in_feature: "p4" out_channels: 256 norm: BN fuse_type: sum backbone: type: voxelnet hidden_dim: ${model.hidden_dim} position_encoding: sine out_features: [p2, ] reader: norm: BN extractor: resnet: ${model.sparse_resnets} fpn: ${model.fpn} out_channels: 256
Looking forward to your reply!
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Hi, I found that there is no config of large backbone and 4x resolution about ConQueR in the repo.
Can you share the config? Thanks.
The text was updated successfully, but these errors were encountered: