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Training

1. Train camera-based baseline with 8 GPUs.

bash run.sh ./projects/baselines/CAM-R50_img1600_128x128x10.py 8

2. Train LiDAR-based baseline with 8 GPUs.

bash run.sh ./projects/baselines/LiDAR_128x128x10.py 8

3. Train multimodal baseline with 8 GPUs.

bash run.sh ./projects/baselines/Multimodal-R50_img1600_128x128x10.py 8

4. Train camera-based CONet with 8 GPUs.

bash run.sh ./projects/Cascade-Occupancy-Network/CAM-R50_img1600_cascade_x4.py 8

5. Train LiDAR-based CONet with 8 GPUs.

bash run.sh ./projects/Cascade-Occupancy-Network/LiDAR_cascade_x4.py 8

6. Train multimodal CONet with 8 GPUs.

bash run.sh ./projects/Cascade-Occupancy-Network/Multimodal-R50_img1600_cascade_x4.py 8

Evaluation

Evaluation example.

bash run_eval.sh $PATH_TO_CFG $PATH_TO_CKPT $GPU_NUM

Visualization

Temporarily only support saving occupancy predictions (refer to MonoScene for visualization tools)

bash run_eval.sh $PATH_TO_CFG $PATH_TO_CKPT $GPU_NUM --show --show-dir $PATH