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GETTING_STARTED.md

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Getting Started

This document provides a brief intro of the usage of DAPS3D.

Training

  • Make shure to cteate conda environment and setup wandb.login for training (See Installation).

  • Select desired augmentation setup inside augmentation.yml. You can find more detailed description and different augmentation setups in our Paper.

Augmentations

Train Commands

This is default command structure for training:

./train.sh -d <path/to/dataset> \
           -f <path/to/configs> \
           -a <path/to/model_config> \
           -m <model_name> \
           -l <path/to/save/logs> \
           -p <path/to/pretrained/logs> \
           -c <gpu to run>

You need to choose <model_name> between: salsanet, salsanet_rec, salsanet_rec_lstm, salsanext, salsanext_rec_lstm, ddrnet or segformer.

SalsaNet

With default config requires 6 GB on GPU

./train.sh -d /Dataset/ -f cfgs/ -a cfgs/salsanext.yml -m salsanet -l ./logs/ -c 0

SalsaNetRec

With default config requires 7 GB on GPU

./train.sh -d /Dataset/ -f cfgs/ -a cfgs/salsanext.yml -m salsanet_rec -l ./logs/ -c 0

SalsaNetRecLSTM

With default config requires 9 GB on GPU

./train.sh -d /Dataset/ -f cfgs/ -a cfgs/salsanext.yml -m salsanet_rec_lstm -l ./logs/ -c 0

SalsaNext

With default config requires 10 GB on GPU

./train.sh -d /Dataset/ -f cfgs/ -a cfgs/salsanext.yml -m salsanext -l ./logs/ -c 0

SalsaNextRecLSTM

With default config requires 14 GB on GPU

./train.sh -d /Dataset/ -f cfgs/ -a cfgs/salsanext.yml -m salsanext_rec_lstm -l ./logs/ -c 0

DDRNet

Change MODEL.MOD inside ddrnet23_slim.yml to 'none', 'oc' or 'da' for different model configurations before run.

With default config requires 2, 3 & 10 GB on GPU for none, oc & da respectively

./train.sh -d /Dataset/ -f cfgs/ -a cfgs/ddrnet23_slim.yml -m ddrnet -l ./logs/ -c 0

SegFormer

With default config requires 11 GB on GPU

./train.sh -d /Dataset/ -f cfgs/ -a cfgs/segformer.yml -m segformer -l ./logs/ -c 0

Inference

This is default command structure for inference:

./infer.sh -d <path/to/dataset> \
          -f <path/to/configs> \
          -l <path/to/pretrained/logs> \
          -m <model_name> \
          -p <path/to/save/predictions> \
          -s <split to inference> \
          -c <gpu to run>

Simple example (for DDRNet model):

./infer.sh -d /Dataset/ -f cfgs/ -l ./logs/ddrnet_aug-set-5+t-z/ -m ddrnet  -p ./logs/infer/ddrnet_aug-set-5+t-z -s valid -c 0

In order to calculate metrics for your predicrions, run eval.sh. Metrics will be saved in iou.txt inside your <predictions folder>:

./eval.sh -d /Dataset/ -f cfgs/ -p ./logs/infer/ddrnet_aug-set-5+t-z -s valid

Pretrained Models

Segmentation models trained and evaluated on SemanticKITTI dataset

All models are trained with augmentation set 5 without T-Zone (see our Paper)

Model $mIoU$, % $IoU$[vehicle], % $IoU$[human], % $IoU$[surface], % $IoU$[obstacle], % Checkpoint
SalsaNet 0.787 0.882 0.412 0.929 0.924 model
SalsaNetRec 0.789 0.855 0.488 0.913 0.900 model
SalsaNetRecLSTM 0.751 0.887 0.271 0.927 0.920 model
SalsaNext 0.821 0.907 0.564 0.905 0.907 model
SalsaNextRecLSTM 0.835 0.914 0.600 0.911 0.914 model
DDRNet 0.692 0.750 0.225 0.901 0.893 model
DDRNetOC 0.687 0.739 0.222 0.900 0.889 model
DDRNetDA 0.696 0.754 0.232 0.903 0.895 model
SegFormer 0.539 0.437 0.048 0.893 0.777 model

Segmentation models trained and evaluated on SemanticKITTI and RELLIS-3D datasets

All models are trained with augmentation set 5 with T-Zone (see our Paper)

Model $mIoU$, % $IoU$[vehicle], % $IoU$[human], % $IoU$[surface], % $IoU$[obstacle], % Checkpoint
SalsaNet 0.832 0.869 0.886 0.789 0.782 model
SalsaNetRec 0.808 0.858 0.847 0.763 0.766 model
SalsaNetRecLSTM 0.828 0.889 0.880 0.770 0.774 model
SalsaNext 0.832 0.904 0.906 0.755 0.763 model
SalsaNextRecLSTM 0.833 0.908 0.916 0.748 0.760 model
DDRNet 0.706 0.759 0.654 0.685 0.725 model
DDRNetOC 0.705 0.749 0.652 0.693 0.728 model
DDRNetDA 0.658 0.715 0.471 0.716 0.731 model
SegFormer 0.533 0.448 0.423 0.616 0.643 model

Segmentation models trained and evaluated on DAPS-1 dataset

All models are trained with augmentation set 5 with T-Zone (see our Paper)

Model $mIoU$, % $IoU$[vehicle], % $IoU$[human], % $IoU$[surface], % $IoU$[obstacle], % Checkpoint
SalsaNet 0.867 0.880 0.646 0.989 0.953 model
SalsaNetRec 0.850 0.836 0.680 0.974 0.908 model
SalsaNetRecLSTM 0.862 0.878 0.632 0.988 0.949 model
SalsaNext 0.886 0.878 0.721 0.990 0.954 model
SalsaNextRecLSTM 0.932 0.929 0.830 0.994 0.974 model
DDRNet 0.690 0.773 0.126 0.977 0.886 model
DDRNetOC 0.694 0.769 0.138 0.978 0.889 model
DDRNetDA 0.691 0.770 0.129 0.977 0.886 model
Segformer 0.530 0.495 0.052 0.909 0.665 model

Segmentation models evaluated on DAPS-2 dataset

These are inference results on DAPS-2 for the models listed in the following sections (see our Paper)

Models trained on SemanticKITTI

Model $mIoU$, % $IoU$[human], % $IoU$[surface], % $IoU$[obstacle], % Checkpoint
SalsaNet 0.405 0.056 0.662 0.498 model
SalsaNetRec 0.267 0.034 0.478 0.290 model
SalsaNetRecLSTM 0.338 0.075 0.565 0.373 model
SalsaNext 0.258 0.074 0.423 0.277 model
SalsaNextRecLSTM 0.327 0.039 0.548 0.394 model
DDRNet 0.345 0.080 0.768 0.187 model
DDRNetOC 0.323 0.015 0.739 0.216 model
DDRNetDA 0.369 0.040 0.705 0.361 model
Segformer 0.230 0.166 0.182 0.343 model

Models trained on SemanticKITTI + RELLIS-3D

Model $mIoU$, % $IoU$[human], % $IoU$[surface], % $IoU$[obstacle], % Checkpoint
SalsaNet 0.712 0.733 0.760 0.643 model
SalsaNetRec 0.481 0.446 0.590 0.406 model
SalsaNetRecLSTM 0.689 0.770 0.761 0.537 model
SalsaNext 0.663 0.799 0.684 0.505 model
SalsaNextRecLSTM 0.736 0.813 0.784 0.612 model
DDRNet 0.580 0.697 0.535 0.509 model
DDRNetOC 0.616 0.724 0.586 0.539 model
DDRNetDA 0.541 0.614 0.507 0.503 model
Segformer 0.290 0.412 0.054 0.404 model

Models trained on DAPS-1

Model $mIoU$, % $IoU$[human], % $IoU$[surface], % $IoU$[obstacle], % Checkpoint
SalsaNet 0.575 0.479 0.741 0.504 model
SalsaNetRec 0.609 0.596 0.748 0.482 model
SalsaNetRecLSTM 0.624 0.532 0.783 0.557 model
SalsaNext 0.643 0.706 0.620 0.602 model
SalsaNextRecLSTM 0.759 0.709 0.746 0.823 model
DDRNet 0.516 0.354 0.737 0.458 model
DDRNetOC 0.562 0.403 0.705 0.577 model
DDRNetDA 0.531 0.360 0.718 0.515 model
Segformer 0.393 0.355 0.418 0.407 model