Comments (3)
I use python3.6. Indeed, this code is copied from Clement Pinard's pytorch implementation of SfMLearner. I tried it many times, and it works well. Maybe the order is not critical. For more details on data construction, I suggest you opening issues on his repo.
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Thanks for your reply. I am very interesting in this work. I want to verify that the 'train.txt' and 'val.txt' differ when you prepare data every time. So it may result in the training set is different from other methods, like Sfmlearner and the np.random.seed(8964).
may be useless. And we do not use 'val.txt' in the training process. The result works well every time maybe because the 'train.txt' is similar every time. I will continue to follow your this setting.
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Thanks for your attention. Look forward to your new conclusions about the splits.
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Related Issues (20)
- about inverse_warp.py HOT 2
- 0.01 in pose decoder HOT 1
- General question (loss to constant 0) HOT 1
- How to use this on a Windows Machine? HOT 3
- Custom Dataset
- NYU V2 HOT 1
- Using mask in training HOT 2
- Stereo datasets needed for training? HOT 1
- pebble missing as a dependency
- unexpected loss curves on my own driving datasets
- About posenet HOT 1
- train only posenet HOT 2
- How to train monodepth2 with the rectified_nyu dataset? HOT 4
- Customized data sets HOT 1
- Pseudo-RGBD SLAM
- monodepth2 added ARN HOT 2
- Mask Visualization HOT 4
- How to Apply the Code to EUROC Dataset?
- About smoothing
- About Auto-Mask
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