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grounded-diffusion's Issues

Inference Speed

Thanks a lot for your great work! May I know what is the inference speed for generating grounded images?

model cannot be found in train.py

when I run the code

python train.py --class_split 1 --train_data random --save_name pascal_1_random

FileNotFoundError: mmdetection/checkpoint/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco_20210903_104808-b92c91f1.pth can not be found.

Release of COCO training script

Thanks for the great work!

At the moment, the provided train.py seems to be hardwired to train on the Pascal VOC dataset. Is there a plan to release the COCO training script that can be used to reproduce results in the paper?

The confused definition of open-vocabulary segmentation

Thanks for your excellent work!

I am confused about the definition of open-vocabulary segmentation from two aspects:

  1. I note that the segmentation model (i.e., maskformer in the paper) is trained on full categories of PASCAL VOC and COCO while the data are synthetic from the Stable Diffusion.
  2. Can open-vocabulary segmentation protocol access the complete categories during training? In my opinion, the unseen(novel) class name should only be available at the test instead of training time. Otherwise, it is not really open-vocabulary.

Hope the authors could give me some help to make me better understand this paper!

Thanks!

how to evaluate the checkpoint after train?

I follow the readme use python train.py --class_split 1 --train_data random --save_name pascal_1_random ' to train the model and generate the checkpoints;now how to evaluate them? I dont find the evalution code in you project.

teaser image

Hello, Thank you for your excellent work.I am very interested in the teaser input images, it is the following pictures,
image

Can you post them?

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