Pose Estimation Model (PEM) for SAM-6D
Requirements
The code has been tested with
- python 3.9.6
- pytorch 2.0.0
- CUDA 11.3
Other dependencies:
sh dependencies.sh
Data Preparation
Please refer to [link] for more details.
Model Download
Our trained model is provided [here], and could be downloaded via the command:
python download_sam6d-pem.py
Training on MegaPose Training Set
To train the Pose Estimation Model of SAM-6D, please prepare the training data and run the folowing command:
python train.py --gpus 0,1,2,3 --model pose_estimation_model --config config/base.yaml
By default, we use four GPUs of 3090ti to train the model with batchsize set as 28.
Evaluation on BOP Datasets
To evaluate the model on BOP datasets, please run the following command:
python test_bop.py --gpus 0 --model pose_estimation_model --config config/base.yaml --dataset $DATASET --view 42
The string "DATASET" could be set as lmo, icbin, itodd, hb, tless, tudl, ycbv, or all. Before evaluation, please refer to [link] for rendering the object templates of BOP datasets, or download our rendered templates. Besides, the instance segmentation should be done following [link]; to test on your own segmentation results, you could change the "detection_paths" in the test_bop.py file.
One could also download our trained model for evaluation:
python test_bop.py --gpus 0 --model pose_estimation_model --config config/base.yaml --checkpoint_path checkpoints/sam-6d-pem-base.pth --dataset $DATASET --view 42