Comments (2)
Hi, We didn't follow the traditional definition of an "epoch"; in what I'm about to say below, an "epoch" is not a full pass of all the data, but rather a pass of 10K datapoints.
I took a look at the intermediate visualizations produced by our network training. When we trained MarrNet2 on cars, chairs, and airplanes, 100 epochs gave us reasonable mean shapes of cars, and 200 epochs gave us cars with some coarse details, such as car wheels. For fine details such as car ears, etc., you would need to finetune MarrNet2 with a GAN loss (our ShapeHD paper).
from genre-shapehd.
Hi, Xiuming
We trained 300 epoches for MarrNet2 on Chairs and it has converged.
We picked the best.pt and wrote code of IOU to evaluate the MarrNet2 without finetuning, but only get 0.077 IoU on the whole validation set. This is too low. Did you get the similar results for 2.5D to 3D without finetuning? and then get much better IoU after finetuning? if not could you probably share the best marrnet2.pt without finetune.
I'm not sure what goes wrong, I attached the IoU code as below. Did you also set the threshold as 0.5 to binarize voxel values after sigmoid?
from genre-shapehd.
Related Issues (20)
- Can I read numpy data from the depth,normal and silhouette image? HOT 4
- How to obtain surface normal images from the raw depth data? HOT 1
- I want to use the wgangp which is in the shapehd HOT 2
- ground truth images that give rise to the demo latent inputs
- MarrNet w/o Reprojection Consistency Weights HOT 1
- how to get the intrinsic and extrinsic parameters when i use the GenRE HOT 3
- how can I make my own data set? HOT 1
- 128.npz voxel files are not TDF HOT 4
- Evaluation code HOT 2
- Failure when running test_genre.sh HOT 1
- Full Dataset? HOT 1
- Fail to compile. HOT 2
- Poor Genre results on demo images when compiled with Cuda10 HOT 6
- Generate distance field based on depth image by using render_spherical function HOT 2
- Different prediction results by using different data loader code for GenRe HOT 3
- Could you provide state_dicts for the discriminator of the pretrained models HOT 3
- how to
- how to train marrnet of shapehd? HOT 1
- GenRe docker environment for 2080Ti and 1080Ti HOT 1
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