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scripts_training.sh
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scripts_training.sh
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#===============================================================================
# Training Scripts
#===============================================================================
# ==== KITTI Val 1 Split ====
# GUP Net
CUDA_VISIBLE_DEVICES=0 python -u tools/train_val.py --config=experiments/config_run_201_a100_v0_1.yaml
# DEVIANT
CUDA_VISIBLE_DEVICES=0 python -u tools/train_val.py --config=experiments/run_221.yaml
# ==== KITTI Full Split ====
# DEVIANT
CUDA_VISIBLE_DEVICES=0 python -u tools/train_val.py --config=experiments/run_250.yaml
# ==== Waymo Val Split ====
# Change val_split_name from 'val' to 'val_small' in waymo configs for quicker validation performance. We had used val_small.
# GUP Net
CUDA_VISIBLE_DEVICES=0 python -u tools/train_val.py --config=experiments/run_1050.yaml
# DEVIANT
CUDA_VISIBLE_DEVICES=0 python -u tools/train_val.py --config=experiments/run_1051.yaml
#===============================================================================
# Ablation Studies
#===============================================================================
# GUP Net without Scale Augmentation
CUDA_VISIBLE_DEVICES=0 python -u tools/train_val.py --config=experiments/run_246.yaml
# DEVIANT without Scale Augmentation
CUDA_VISIBLE_DEVICES=0 python -u tools/train_val.py --config=experiments/run_247.yaml
# DCNN
CUDA_VISIBLE_DEVICES=0 python -u tools/train_val.py --config=experiments/run_260_1.yaml
# GUP Net with (bigger) DLA102 and DLA169 backbones
CUDA_VISIBLE_DEVICES=0 python -u tools/train_val.py --config=experiments/gup_dla102.yaml
CUDA_VISIBLE_DEVICES=0 python -u tools/train_val.py --config=experiments/gup_dla169.yaml
# GUP Net vs DEVIANT on ResNet-18 backbone
CUDA_VISIBLE_DEVICES=0 python -u tools/train_val.py --config=experiments/gup_resnet18.yaml
CUDA_VISIBLE_DEVICES=0 python -u tools/train_val.py --config=experiments/dev_resnet18.yaml