qizekun / recon Goto Github PK

Hi，I download your pretain model from the google cloud and finetune on NVIDIA 3090，and achieve the 92.38% result on svm modelnet40 task ，and 94.49% 92.6% 89.62% on the ScanObjectNN task, and the random seed also be set to 0 .

Is it related to the server and Pytorch environment I'm using? Or I need to cancel the setting of the random seed and run multiple times.

Thank you！

When i try run own dataset，this error appears：dataset name is not in the dataset registry'

Hello, thanks for your wonderful work. Can you share the source code? [email protected]

First, thanks for sharing so good work!

How to download the ModelNet_10 dataset ?

Looking forward to your reply~

Thank you so much for your excellent work. When I run the pre-training code sh scripts/pretrain.sh <GPU> <exp_name>, I get an error，as shown：

[ WARN:[email protected]] global /io/opencv/modules/imgcodecs/src/loadsave.cpp (239) findDecoder imread_('/media/data/data01/wcs/data/ShapeNet55-34/shapenet_img/02747177-.png'): can't open/read file: check file path/integrity

I'm pretty sure it's a problem with the shapenet55/34 dataset. As you said “the image data is different from the pointcloud data in some samples, you need to update the meta-data "ShapeNet55-34/ShapeNet-55/train.txt & test.txt" from Our Google Drive.”

But i don't know what modification i need to do, can you tell me. Thanks.

Firstly , thanks for sharing the outstanding work!!!

Could you help me to know how to pretrain the Point-MAE† （not the Point-MAE）in the Table 1, and the architecture of the Point-MAE† ?

Look forword your reply, thanks!

Thanks for your previous reply!

In zero-shot classification on ScanObjectNN， are all dataset （i.e., train+test） or just test dataset used?

Hello, thank you for your great work. I encountered some issues while attempting to reproduce your experiment.

I downloaded your pretrained model from Google Cloud, fine-tuned it on an RTX 3090, and obtained the following results: 93.97% on OBJ_BG, 92.08% on OBJ_ONLY, and 89.97% on PB_T50_RS (without voting, seed = 0). However, I couldn't achieve comparable results to those reported in the paper, which are 95.18%, 93.63%, and 90.63%, respectively.

After reading this issue, I learned about the correct method to reproduce the results. I then attempted using seed 32174, but the results remained the same at 93.97% on OBJ_BG. In general, it seems unlikely that the seed alone would cause such a significant performance difference (e.g., 93.97% in my case vs. 95.18% in your reported results).

Could you please provide guidance on how to accurately reproduce the experiment? Thank you very much.

Thanks for your amazing work! Can you provide the pretain logs file.I want to check if I'm running it incorrectly.
Thank you!

In the given logs in Google Drive, there are some unknown args and configs that can't be found in the released code. For example, "args.pretrain_prompt : False", "config.model.cls_sample : 256" in hardest_90_63.log, and "config.model.cls_embeding : False" in "objbg_95_18.log". What are them? Are these args and configs related to the final result?

First, thanks for sharing so outstanding work!!

could you tell me how to render image using MacOS Preview?

Thanks!!

Thanks for your amazing work! I was trying to run zero-shot classification of ModelNet40 using the pipeline you provided. However, I only got 61.2% accuracy (66.8% reported in the paper) using the following script:

python main.py \
--config=cfgs/zeroshot/modelnet40.yaml \
--zeroshot \
--exp_name=zeroshot_modelnet \
--ckpts=ckpts/zeroshot_66_78.pth

Am I missing something here? Thanks!

When I run the program, I get the following error, is my data not downloaded, or my package is not installed？

Looking forward to your reply, thank you

First, thanks for sharing so good work!

Why is the Leaderboard result about zero-shot classification lower than paper?

zero-shot classification on MN10： 81.6（ in paper）> 75.6 (inLeaderboard )

zero-shot classification on MN40： 66.8（ in paper）> 61.7 (inLeaderboard )

Look forward to your reply~

I don't know what is it, can i cancel it?