Comments (5)
Due to the strided convolutions and the simple upconv layers in our network, the height and width of the input should be multiples of 32.
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Thanks, I'll try training by making image size to be multiple of 32.
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Hi @cogaplex-bts,
It works really well on higher resolution images. I also want to know how do I apply Transfer learning in order to train bts on my custom dataset.
I tried training by using the pretrained encoder using NYU pretrained weights, instead of using ImageNet pretrained weights (and training the decoder from scratch). I did the following changes:
In bts.py
, to load pretrained NYU V2 weights of densenet121
encoder (here BtsModelOriginal
is the original bts
model required to load weights):
if True:
model = BtsModelOriginal(params)
model = torch.nn.DataParallel(model)
checkpoint = torch.load('/kharshit/DepthEstimation/bts/models/bts_nyu_v2_pytorch_densenet121/model')
model.load_state_dict(checkpoint['model'])
self.base_model = model.module.encoder.base_model
self.feat_names = ['relu0', 'pool0', 'transition1', 'transition2', 'norm5']
And, everything the same. Should I do some other modifications as well (in the decoder)?
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@kHarshit hi! What dataset are you using?
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@oljike It's a custom dataset prepared manually.
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Related Issues (20)
- Testing on single image from KITTI dataset
- Something wrong with kitti gt dataset HOT 1
- Inconsistent results on Online evaluation and Test (might caused by benchmark crop operation) HOT 3
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- Question about Ray-Plane Intersection in LPG
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- 解决运行extract_official_train_test_set_from_mat.py时only integer scalar arrays can be converted to a scalar index问题 HOT 3
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