Closed nzomi closed 8 months ago
@nzomi
Hi, I'm quite interested in the training code for DCVC-DC as well. However, the model trends to collapse after few epochs. Do you have similar problem? or could you share your training code for reference.
Thank you so mush.
@Cihsaing Hi Cihsaing! I simply used the training strategy provided by TCM v1 https://arxiv.org/abs/2111.13850v1
@nzomi Hi, Could I discuss some details with you personally?
I observe exactly the same issue as @nzomi. After cascaded training with hierarchical GOP (using the same weights mentioned in the paper), the BD-PSNR rate is higher than the officially trained models, if the test sequences are only 5 frames long. However the BD-rate rapidly deteriorates for sequences longed than 5 frames. At the end, when coding a sequence that is like 32 frames long, the coding performance is much worse than official models.
I use the vimeo90k, that is 7 frames long for training. could that be the issue here? Or is there any other tricks in the training to keep the PSNR more consistent?
I'm so interested in the DCVC-FM, could you please send me the traing codes. Thanks very much.
hedelong92@163.com
I'm quite interested in the training code for DCVC-DC, especially in understanding how the hierarchical weights influence the model during the training process. Specifically, I implemented the hierarchical weights in my own model, adopting the popular multi-stage training approach (IP, PP, PP, PP, ...) and then the cascade training for sequences like IPPPPP, PPPPPP, and so on.
I've observed a fascinating behavior in my model during the multi-stage training, and it appears to align with the trend you mentioned. However, when transitioning to the cascade stage, a peculiar shift in the curve by 2 frames caught my attention. After thoroughly inspecting my code, I'm confident there are no bugs. Did you incorporate any special techniques during your training, or do you have insights into this phenomenon? I'm eagerly anticipating your response.