Comments (2)
Thank you!
from pytorch-inpainting-with-partial-conv.
@wangcx2018 It's pretty sophisticated and huge dataset developed by the following paper. On downloading, please follow the instructions on their website.
I expect to have data_large
, val_large
, and test_large
under the root directory of Places2.
Specify the path of the Places2 dataset using an argument --root /path/to/Places2
in train.py
from pytorch-inpainting-with-partial-conv.
Related Issues (20)
- A problem in net.py
- Blurry problem in training HOT 1
- can not find the dataset Places2 HOT 2
- Problem while using net.py HOT 1
- Inquiry about LICENSE HOT 2
- test.py uses Places2 Class incorrectly
- Hi, where are the trained models in your project? HOT 1
- About Model Size HOT 1
- bad examples HOT 1
- Slight scaling issue in PartialConv function
- Blurry results HOT 3
- Generate Mask HOT 1
- 索引 HOT 2
- 版本不匹配 HOT 1
- 类型错误
- 类型错误
- Why the mask is convolved in partial conv?
- Loss values vary a lot
- http://places2.csail.mit.edu/ が開けません HOT 1
- Your Partial Conv is new in computer vision. However, if you use a ground truth image in your loss function for your model trining, your paper is worthless for image inpainting. In most cases, we only have a deteriorated image, and the the ground truth is an unknown target. HOT 3
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from pytorch-inpainting-with-partial-conv.