MODEL.md

Training

cd tools
sh scripts/dist_train.sh ${NUM_GPUS} --cfg_file ${CONFIG_FILE} ${PY_ARGS}

For instance,

• train B15/N4 semantic segmentation on ScanNet:

  cd tools
  sh scripts/dist_train.sh 8 --cfg_file cfgs/scannet_models/spconv_clip_base15_caption_adamw.yaml --extra_tag exp_tag
  

• train B13/N4 instance segmentation on ScanNet:

  cd tools
  sh scripts/dist_train.sh 8 --cfg_file cfgs/scannet_models/inst/softgroup_clip_base13_caption_adamw.yaml --extra_tag exp_tag
  

Inference

cd tools
sh scripts/dist_test.sh ${NUM_GPUS} --cfg_file ${CONFIG_FILE} --ckpt ${CKPT_PATH}

For instance,

• to test a B15/N4 model on ScanNet:

  cd tools
  sh scripts/dist_test.sh 8 --cfg_file cfgs/scannet_models/spconv_clip_base15_caption_adamw.yaml --ckpt output/scannet_models/spconv_clip_base15_caption/exp_tag/ckpt/checkpoint_ep128.pth
  

Model Zoo

• semantic segmentation

  Dataset	Partition	hIoU / mIoU(B) / mIoU(N)	Path
  ScanNet	B15/N4	64.9 / 67.8 / 62.2	ckpt
  ScanNet	B12/N7	55.9 / 70.4 / 46.4	ckpt
  ScanNet	B10/N9	52.8 / 76.6 / 40.3	ckpt
  S3DIS	B8/N4	35.6 / 58.3 / 25.6	ckpt
  S3DIS	B6/N6	38.4 / 53.9 / 29.8	ckpt

• instance segmentation

  Dataset	Partition	hAP50 / mAP50(B) / mAP50(N)	Path
  ScanNet	B13/N4	57.8 / 58.7 / 56.9	ckpt
  ScanNet	B10/N7	31.6 / 54.8 / 22.2	ckpt
  ScanNet	B8/N9	36.9 / 63.1 / 26.2	ckpt
  S3DIS	B8/N4	17.2 / 60.9 / 10.0	ckpt
  S3DIS	B6/N6	15.8 / 48.2 / 9.5	ckpt