visinf / dense-ulearn-vos Goto Github PK

in Line 413-425: I think the "for" loop is unnecessary. Could you tell me about its specific function? Thanks!

Any solution please ?

After downloading the filelist.txt for YoutubeVOS, I found that the images are sampled per 5 frames, which is the fully supervised setting, since only one out of every five frames will be annotated.
But under self-supervised setting, previous methods (like MAST), are using the full version with all training frames.
Have you tried this later one ?

Thanks for your great work! Could you provide the performance deviation when training with YouTube-VOS? By the way, I'd like to know whether the number reported in the paper is produced by the last training checkpoint or not. Thanks!

In infer_vos.py, Line 211 and 237, I've got the TypeError: forward() missing 1 required positional argument: 'frames' , after I change them as keyward arguments (frames=frames[:1]), the error solved. Is it a bug or something related to my environment? I haven't found the reason yet since the frames is not a keyward arguments in framework.py

By the way, does your evaluation script support Multi-GPU inference? It seems that inference on YouTube-VOS will take a very long time ?

Best,

the inference result is black

Available threads: 12
Loaded 2 sequences
Dataloader: filelists/val_ytvos2018_test # 271
filelists/val_ytvos2018_test: no augmentation
Sequence 00 | 0062f687f1

..........................................................................................<
Sequence 01 | 00f88c4f0a
...................................................................................................................................................................................<
984.928 elapsed: Inference completed

I am trying to run bash ./launch/infer_vos.sh ytvos, but am getting errors of "GPU out of memory". Trying to reduce the batch_size down to 8, 4, 2, 1, but still getting the error. I have nVidia K2000, with only 4G GPU memory. Any suggestions/advice how to get around the issue? Thanks.

Grear work! Thanks for sharing your code!

I use the default training configure of ytvos to train the network.
But I only got best performance J&F=65.5 at epoch 490.
Is the default configure supposed to have this performance?
How can I get the best performance like your provided checkpoint?
I'd appreciate it if you could point out what I did wrong.

In the readme file you're training the data on resnet, what about your own model ? , Are you contributing in the data preprocessing level or you created your own framework.? If so, why it appears that you are training the data not with you own model(framework), but with the pretrained model (resnet)?

Thanks in advance.

please add a google colab for inference

https://github.com/visinf/dense-ulearn-vos/blob/88e5e3518cb0c4a636ad652a3c0f544a370f4c6c/labelprop/crw.py#L234C7-L234C7

Hi,

Could you add indexing='ij' in torch.meshgrid() to suppress the warning?

/home/user/anaconda3/envs/dense-ulearn-vos/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1670525552843/work/aten/src/ATen/native/TensorShape.cpp:3190.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]

See : https://pytorch.org/docs/stable/generated/torch.meshgrid.html for reason behind warning.

Regards
Akshit Maurya

I uploaded the training data and put it in the path that exists on the data/filelists, everytime I am facing this error : (this is one example: AssertionError: cfg.DATASET.ROOT/ytvos/train/JPEGImages/003234408d/00000.jpg not found. I also brought a new data that I wanted to train the model with and it keeps giving this error. it seems that it is considering that the data is not there where it is there and I am pretty sure of the path. Any suggestions please ?

What kind of preprocessing and preparation are required if I want to train from scratch on my own video data? Thanks