search

gaopengcuhk / Pretrained-Pix2Seq

Replication of Pix2Seq with Pretrained Model
60 stars 5 forks source link

About LargeScaleJitter #3

Open ShuaiBai623 opened 3 years ago

ShuaiBai623 commented 3 years ago

hi, great work! We also try to reimplement the Pix2Seq, we find the absolute coordinate is useful, which is similar to your LargeScaleJitter (pad or crop the image to the fix desired size), the absolute coordinate means that normalized the position by dividing the fix size. boxes = boxes / 1333. instead of boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32),Then, padding or croppinf the image to the fix desired size is not necessary.

hanqiu-hq commented 3 years ago

Thanks. We also use absolute coordinate as described in Pix2Seq. https://github.com/gaopengcuhk/Pretrained-Pix2Seq/blob/7d908d499212bfabd33aeaa838778a6bfb7b84cc/playground/pix2seq/pix2seq.py#L88-L90 The conversion of relative coordinate in transforms.py is because we use the same dataloader as DETR. And for the large Scale jittering, we basically follow the same pipeline proposed in CopyPaste which is cited in Pix2seq.

baiyongrui commented 3 years ago

Hello, does using absolute coordinate gets a better AP?

seanzhuh commented 2 years ago

The only difference between relative and absolute coordinate is the normalization factor ? Absolute will normalize by the longest image size instead of the actual image size, which relative coordinate does, am I right ?