Thanks for your nice work. I am trying to reproduce your fine-tuning results on ShapeNetPart segmentation. I find that the model architecture for classification pertaining and segmentation pertaining are different. More specifically, in classification pertaining, the dgcnn model is adopted, while dgcnn_seg is utilized for the pre-training for part segmentation, as shown in the following:
|
python train_crosspoint.py --model dgcnn_seg --epochs 100 --lr 0.001 --exp_name crosspoint_dgcnn_seg --batch_size 20 --print_freq 200 --k 15 |
However, I can not find the definition of dgcnn_seg in your model library. I guess the dgcnn_seg should be the DGCNN_partseg model with pretrain=True, right?
In addition, in my opinion, other paper may adopt the same architecture in pre-training for both classification and part segmentation, such as OcCo. Such a difference may lead to unfair comparison. What's your opinion?
Thanks for your nice work. I am trying to reproduce your fine-tuning results on ShapeNetPart segmentation. I find that the model architecture for classification pertaining and segmentation pertaining are different. More specifically, in classification pertaining, the dgcnn model is adopted, while dgcnn_seg is utilized for the pre-training for part segmentation, as shown in the following:
CrossPoint/scripts/script.sh
Line 6 in 364987e
However, I can not find the definition of dgcnn_seg in your model library. I guess the dgcnn_seg should be the DGCNN_partseg model with pretrain=True, right?
In addition, in my opinion, other paper may adopt the same architecture in pre-training for both classification and part segmentation, such as OcCo. Such a difference may lead to unfair comparison. What's your opinion?