JiawangBian
Could not reproduce the kitti odometry results
I have run CUDA_VISIBLE_DEVICES=3 bash scripts/train_resnet50_pose_256.sh which contains:
TRAIN_SET=data/kitti/kitti_vo_256/
python train.py $TRAIN_SET \
--resnet-layers 50 \
--num-scales 1 \
-b4 -s0.1 -c0.5 --epoch-size 1000 --sequence-length 3 \
--with-ssim 1 \
--with-mask 1 \
--with-auto-mask 1 \
--with-pretrain 1 \
--log-output \
--name resnet50_pose_256
The training process seems good: Eventually we have Total loss: 1.130, photo loss: 1.130, smooth loss: 0.029, consistesh scriptsncy loss: 0.092. Then I following your intruction to run sh scripts/test_kitti_vo.sh where I modify the variables to fit my training path
DATASET_DIR=data/kitti/kitti_odom_test/sequences/
OUTPUT_DIR=vo_results_tmp/
POSE_NET=checkpoints/resnet50_pose_256/07-20-18:24/exp_pose_model_best.pth.tar
However, the evaluation results are quite far away from the reported ones:
Sequence: 9
Tranlsational error (%): 32.49381948479178
Rotational error (deg/100m): 8.876555343671876
ATE (m): 168.36036295068504
RPE (m): 0.2413781835893739
RPE (deg): 0.13960476261442967
Sequence: 10
Translational error (%): 12.888807673314357
Rotational error (deg/100m): 5.588445130500064
ATE (m): 31.397818060533346
RPE (m): 0.0859026892970541
RPE (deg): 0.11769662553343758
while the reported numbers of t_err(%)/r_err(degree/100m) for seq 9 and 10 are 7.13/3.05 and 7.79/4.90 respectively.
May I ask whether you have any suggestions on correcting the results? Many thanks!
Odometry results in long sequences are very sensitive, but should not be so bad. Could you check the depth visualization in tensorboard to make sure that the training is correct?
