How could use?

[p00uya]

Hi, I'm very interested in using your code in my project. I was able to run demo scripts. Now I want to use your code for my own data, but unfortunately I do not know how I can do this on my data. I put them in files A, B But I think I need guidance to test
Please tell me how I can find a difference for my images
(I am a newcomer, thank you)

[wgcban]

Hi @p00uya,

Thank you for your interest in our work and welcome!

The easiest way to get predictions for your samples is to copy and paste your pre-change (A) and post-change (B) images to one of the samples_DSIFN or samples_LEVIR. Once you copied your images to a folder, update the demo.txt in the list folder which is placed inside the respective folder with your image names and run the corresponding demo file. The prediction will be saved in samples/predict_LEVIR'' or samples/predict_LEVIR''.

Please note that our model is trained on remote sensing images and it may not give correct predictions if you are testing it with images from a separate domain. In that case, I recommend you to tune our model for your dataset to get better results. To do that, you need to follow the training procedure mentioned in the latter part of the readme file. Please let me know if you need any further help and I'm happy to help you!

[p00uya]

Ok, I did this the name of img was t1.png , I got the following error
FileNotFoundError: [Errno 2] No such file or directory: './samples_LEVIR/label/t1.png'
Question: Is it like giving input and taking an output like object recognition tasks? So why ask for a label for each image?

[wgcban]

@p00uya the ground truth change mask is used to compute the performance metrics such as IoU and accuracy. Since you don’t have the ground truth labels, place a dummy black png there with same size as your pre-change and post-change imgaes. Or you can comment those lines in the code too.

[p00uya]

@wgcban
I did, but unfortunately the prediction is not correct
in second way would you please let me know where should I comment?

[wgcban]

Hi @p00uya,
Are you testing on the remote sensing data or some other images?
If your test images are different from the images that we used to train the model, we cannot expect good predictions from the model.
Also, please note that the LEVIR-CD is a building change detection dataset, so if you used a pre-trained model on the LEVIR-CD dataset, it will only detect building changes.
Can you clarify the above things because it will help me to get an idea about your project?
Also, if possible, please post a sample pre-change and post-change image here.

[p00uya]

Hi @wgcban thanks to replaying quickly
this my data, consist of A,B,label
the label size is same as A , B image

[wgcban]

Hi @p00uya
Please give me access to download the data.

[wgcban]

Hi @p00uya ,
I got the predictions for your data using the model trained on the LEVIR-CD
dataset. The predictions seem reasonable to me except val_29. This behavior can be expected because you are testing on out of distribution data. If you want better results I recommend you to train our model on your data.
You can find the prediction here. p00uya_predictions_ChangeFormer.zip
Best,

Best,
Chaminda.

[p00uya]

Yes, you are right
I got the same results
Thank a lot

[yuanjian2022]

Hi,wgcban
I try to reproduce your code ChangeFormerV6 on DSIFN, I use ''LEVIER-CD\CD_ChangeFormerV6_LEVIR_b16_lr0.0001_adamw_train_test_200_linear_ce_multi_train_True_multi_infer_False_shuffle_AB_False_embed_dim_256\best_ckpt.pt'' to initialize the model, and I have some problems.
Did you use ''from models.pixel_shuffel_up import PS_UP'', but your ''Changeformer'' did not call PS_UP. Your pre-trained model parameter size is 492671152. I found that the model I reproduced from your code is 492670048. The result I reproduced is very poor on DSIFN.
Thank you very much.

[wgcban]

Hey @yuanjian2022,

You are using the wrong model to initialize. As the name infer "LEVIER-CD\CD_ChangeFormerV6_LEVIR_b16_lr0.0001_adamw_train_test_200_linear_ce_multi_train_True_multi_infer_False_shuffle_AB_False_embed_dim_256\best_ckpt.pt" is the model trained on LEVIR-CD and not on the DSIFN CD. You should use: DSIFN-CD-Pretrained-Model.

Please use the model trained on DSIFN-CD to initialize the model if you are trying to reproduce results on DSIFN-CD. I have linked pre-trained models for each dataset in the readme file. Please see: Quick Start on DSFIN dataset.

Or you can simply run the demo file for the DSIFN dataset.

Please let me know if you have any questions!

Best,
Chaminda.

[yuanjian2022]

Hello,Chaminda
I downloaded the pre-trained model CD_ChangeFormerV6_DSIFN_b16_lr0.00006_adamw_train_test_200_linear_ce_multi_train_True_multi_infer_False_shuffle_AB_False_embed_dim_256/ from https://github.com/wgcban/ChangeFormer#quick-start-on-dsfin-dataset,I first tried demo_DSIFN.py, and got the prediction graph and yours /yj/yjcode/ChangeFormer-main/samples_DSIFN/predict_ChangeFormerV6 consistent.
Then start training from the 182nd epoch in your log. The size of the generated model parameters is 492671152 and the downloaded best_ckpt.pt and last_ckpt.pt are the same size, and F1 and iou are also similar to yours.
But I started training from the first epoch, the model will call CD_ChangeFormerV6_DSIFN_b16_lr0.00006_adamw_train_test_200_linear_ce_multi_train_True_multi_infer_False_shuffle_AB_False_embed_dim_256/best_ckpt.pt to initialize the model, the generated new model parameter is 492670448 and the model does not converge.
I also tried to train directly, initialize the model randomly, namely 'training from scratch... ', the generated model parameters are also 492670448 and also do not converge.
I think it is the loss of newly generated model parameters that is causing the non-convergence.
Best,
JianYuan

[wgcban]

@yuanjian2022,

When training on the DSIFN dataset, I initialized the ChangeFormerV6 with the model trained onLEVIR-CD for faster convergence. Since we have trained models for both LEVIR-CD and WHU-CD, I recommend you to do the same when training on DSIFN or another dataset.
Having said that, I'm not exactly sure about how starting from the 182 epoch and starting from the 0th epoch create two different models with different numbers of parameters. Please allow me some time - I will think about this and update you. At the same time, if you find the solution please post here.
Did you try what happens when you initialize the model with a model trained on LEVIR-CD?