The prepared will be save in the directory assigned by the item preprocess.dst
in the configuration file. Run the command:
python tools/preprocess.py --config=<configuration file> --split=<split>
For example,
python tools/preprocess.py --config=TAB_Pillar256x512x20_UNet.yaml --split=train
The training will use the GPU devices as much as can be gotten. You can limit and assign the device(s) via CUDA_VISIBLE_DEVICES
. Checkpoints will be save in ./checkpoints/
.
torchrun --nproc_per_node=2 tools/train_ddp.py --config=TAB_Pillar256x512x20_UNet.yaml --split=train --batch_size=16 --num_worker=10 --end_epoch=200
A loss curve figure will be generated and saved in ./eval/
. We have not done evaluation when training. You can modify the tools/train_ddp.py
and try to do evaluate during training.
python tools/eval.py --config=TAB_Pillar256x512x20_UNet.yaml --batch_size=64 --num_worker=10
Prediction results will be visualized and save in ./vis
.
python tools/vis_tab.py --config=TAB_Pillar256x512x20_UNet.yaml --split=test --batch_size=64 --num_worker=10 --checkpoint=199
Assess the well-trained models. Results will be saved in ./results/
.
python tools/test.py --config=TAB_Pillar256x512x20_UNet.yaml --split=test --batch_size=64 --num_worker=10 --checkpoint=199
Backbone | Straight-going side | Turning | Ignoring semantics | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
mF_p | F_0.3 | F_0.5 | F_0.8 | mF_p | F_0.3 | F_0.5 | F_0.8 | mF_p | F_0.3 | F_0.5 | F_0.8 | |
UNet | 0.55 | 0.50 | 0.41 | 0.07 | 0.71 | 0.68 | 0.63 | 0.41 | 0.75 | 0.70 | 0.67 | 0.32 |
HRNet-w18 | 0.69 | 0.66 | 0.63 | 0.56 | 0.73 | 0.75 | 0.69 | 0.54 | 0.82 | 0.83 | 0.77 | 0.62 |
DeepLabV3+ | 0.62 | 0.57 | 0.49 | 0.35 | 0.71 | 0.67 | 0.61 | 0.43 | 0.78 | 0.73 | 0.67 | 0.46 |
NOTE: The checkpoint of the DeepLabV3+ has not been uploaded because it is larger than 2 GB. You can rise up an issue and leave your email address. The checkpoint will be sent to you as soon as possible.
You can download well-trained checkpoints and move them into checkpoint directories such as ./checkpoints/TAB_Pillar256x512x20_UNet.yaml/
.