Issue about performance

[sangrockEG]

Thank you for your work and for sharing the implementation.
But there are some issues, so please share your insight!

(1) Protocol of the whole experiment
Actually, it isn't easy for me to catch up on the proper reproduction protocol.
Please check whether I understand correctly or not.

1. Train SimpleShot point cloud classifier only.
   (bash training_scripts/train_dgcnn_simpleshot.sh 0)
2. Extract point cloud embeddings using the trained one.
   (python lssb/feat_extract/extract_simpleshot_pc_feature_modelnet.py --ckpt_path=[])
3. Train SimpleShot image classifier using the extracted embeddings.
   (bash training_scripts/train_resnet18_joint_simpleshot.sh 0)
   Here, the original code uses the config "modelnet-joint-simpleshot-resnet18-cfg", but it makes error. I think this should be "modelnet-joint-simpleshot-resnet18-w-pc-cfg" (or "modelnet-joint-simpleshot-resnet18-wo-pc-cfg"). Is it right?
4. Test trained image classifier.
   (bash testing_scripts/test_simpleshot_modelnet.sh)

(2) Performance
The number in the table in ReadMe is different from the table in the main paper.
For example, in the case of ModelNet 1shot-5way, LSSB achieves 61.91 in the main paper.
But in ReadMe, it is written as 57.57, which is much lower than the baseline (SimpleShot, 58.99).
What makes this huge difference?

Further, when I tested the officially provided checkpoints, it returns even lower performance (54~55).
I run five times as the paper recommended.
For this, I used "bash testing_scripts/test_joint_simpleshot_modelnet.sh".
Here, I slightly changed the sh file as follows, since there is no pretrained_models/simpleshot/modelnet/shape-biased directory.
python lssb/lowshot/test.py --log_dir=pretrained_models/simpleshot/modelnet/shape-biased-w-pc/
--name=joint-modelnet-pairwise-simpleshot-w-pc
--version=0
--gpu=1 \

It would be greatly appreciated if you could help me with these issues.