Source code for our work "Semantic Terrain Classification for Off-Road Autonomous Driving"
Datasets should be put inside data/
. For example, data/semantic_kitti_4class_100x100
.
SemanticKITTI
RELLIS
First extract the model weights
cd /path/to/bevnet/experiments
unzip /path/to/zip/file
To run the models on the validation set, cd
to bevnet/bevnet
, then run
# Single-frame model
python test_single.py --model_file ../experiments/kitti4_100/single/include_unknown/default-logs/model.pth.4 --test_env kitti4
# Recurrent model
python test_recurrent.py --model_file ../experiments/kitti4_100/recurrent/include_unknown/default-logs/model.pth.2 --test_env kitti4
Example:
cd experiments
bash train_kitti4-unknown_single.sh kitti4_100/single/include_unknown/default.yaml <tag> arg1 arg2 ...
Logs and model weights will be stored in a subdirectory of the config file like this:
experiments/kitti4_100/single/include_unknown/default-<tag>-logs/
<tag>
is useful when you want to use the same config file but different hyperparameters. For example, if you
want to do some debugging you can use set <tag>
to debug
.arg1 arg2 ...
are command line arguments supported by train_single.py
. For example, you can pass
--batch_size=4 --log_interval=100
, etc.The command line formats are the same as BEVNet-S Example:
cd experiments
bash train_kitti4-unknown_recurrent.sh kitti4_100/recurrent/include_unknown/default.yaml <tag> \
--n_frame=6 --seq_len=20 --frame_strides 1 10 20 \
--resume kitti4_100/single/include_unknown/default-logs/model.pth.4 \
--resume_epoch 0
Logs and model weights will be stored in a subdirectory of the config file
experiments/kitti4_100/recurrent/include_unknown/default-<tag>-logs/
.