Some model parameters are not in the checkpoint

[CLF]

python demo.py --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_ResNeSt_50_FPN_syncBN_1x.yaml --input input.jpg --output output.jpg

[06/09 08:22:15 detectron2]: Arguments: Namespace(confidence_threshold=0.5, config_file='../configs/COCO-InstanceSegmentation/mask_rcnn_ResNeSt_50_FPN_syncBN_1x.yaml', input=['input.jpg'], opts=[], output='output.jpg', video_input=None, webcam=False)
[06/09 08:22:22 fvcore.common.checkpoint]: Loading checkpoint from https://hangzh.s3-us-west-1.amazonaws.com/encoding/models/resnest50_detectron-255b5649.pth
[06/09 08:22:22 fvcore.common.file_io]: Downloading https://hangzh.s3-us-west-1.amazonaws.com/encoding/models/resnest50_detectron-255b5649.pth ...
[06/09 08:22:22 fvcore.common.download]: Downloading from https://hangzh.s3-us-west-1.amazonaws.com/encoding/models/resnest50_detectron-255b5649.pth ...
resnest50_detectron-255b5649.pth: 102MB [15:26, 110kB/s]
[06/09 08:37:48 fvcore.common.download]: Successfully downloaded /root/.torch/fvcore_cache/encoding/models/resnest50_detectron-255b5649.pth. 102065124 bytes.
[06/09 08:37:48 fvcore.common.file_io]: URL https://hangzh.s3-us-west-1.amazonaws.com/encoding/models/resnest50_detectron-255b5649.pth cached in /root/.torch/fvcore_cache/encoding/models/resnest50_detectron-255b5649.pth
[06/09 08:37:49 fvcore.common.checkpoint]: Some model parameters are not in the checkpoint:
roi_heads.mask_head.mask_fcn2.norm.{running_mean, running_var, weight, bias}
roi_heads.mask_head.mask_fcn1.norm.{bias, weight, running_var, running_mean}
backbone.fpn_output2.norm.{running_var, weight, running_mean, bias}
roi_heads.box_head.conv1.norm.{running_var, running_mean, weight, bias}
roi_heads.mask_head.mask_fcn3.norm.{bias, running_mean, running_var, weight}
backbone.fpn_lateral5.norm.{running_mean, running_var, weight, bias}
proposal_generator.anchor_generator.cell_anchors.{4, 3, 1, 2, 0}
roi_heads.box_predictor.bbox_pred.{bias, weight}
roi_heads.mask_head.predictor.{bias, weight}
roi_heads.mask_head.mask_fcn4.norm.{bias, running_var, weight, running_mean}
roi_heads.box_predictor.cls_score.{bias, weight}
roi_heads.box_head.conv2.norm.{running_var, bias, running_mean, weight}
roi_heads.box_head.conv4.weight
backbone.fpn_lateral3.norm.{running_mean, running_var, bias, weight}
roi_heads.box_head.conv3.norm.{running_var, running_mean, weight, bias}
backbone.fpn_lateral5.weight
roi_heads.box_head.conv2.weight
roi_heads.box_head.fc1.{bias, weight}
proposal_generator.rpn_head.objectness_logits.{bias, weight}
backbone.fpn_lateral4.norm.{running_mean, running_var, weight, bias}
backbone.fpn_lateral2.norm.{running_var, weight, bias, running_mean}
backbone.fpn_output5.norm.{running_mean, weight, running_var, bias}
backbone.fpn_output2.weight
roi_heads.box_head.conv4.norm.{running_mean, weight, running_var, bias}
backbone.fpn_output3.weight
backbone.fpn_output4.norm.{weight, running_mean, bias, running_var}
roi_heads.mask_head.deconv.{weight, bias}
backbone.fpn_output3.norm.{running_mean, weight, bias, running_var}
proposal_generator.rpn_head.anchor_deltas.{weight, bias}
backbone.fpn_lateral4.weight
backbone.fpn_output5.weight
roi_heads.mask_head.mask_fcn3.weight
backbone.fpn_output4.weight
roi_heads.mask_head.mask_fcn2.weight
roi_heads.box_head.conv1.weight
proposal_generator.rpn_head.conv.{bias, weight}
backbone.fpn_lateral3.weight
roi_heads.box_head.conv3.weight
backbone.fpn_lateral2.weight
roi_heads.mask_head.mask_fcn4.weight
roi_heads.mask_head.mask_fcn1.weight

[frischzenger]

i also meet this problem, how do you solved it?