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![digit](https://user-images.githubusercontent.com/16487352/91674153-13d4df00-eb6a-11ea-9e3a-99ab85305f3d.png)
![densenet](https://user-images.githubusercontent.com/16487352/91674156-1e8f7400-eb6a-11…
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WARNING: Logging before InitGoogleLogging() is written to STDERR
W0805 10:26:38.720525 3101 _caffe.cpp:139] DEPRECATION WARNING - deprecated use of Python interface
W0805 10:26:38.720546 3101 _caf…
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WARNING: Logging before InitGoogleLogging() is written to STDERR
W0805 10:26:38.720525 3101 _caffe.cpp:139] DEPRECATION WARNING - deprecated use of Python interface
W0805 10:26:38.720546 3101 _caffe…
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Each widget interaction with Streamlitt causes the script to rerun from top to bottom. This execution model leads to the creation of a new TensorBoard server for every interaction and new connection t…
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def forward(self,x):
features = [x[:,self.csp_num_features1:,...]] of line 31 stores features as a tuple because of the additional brackets this breaks the code you have to undo the bracket…
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I am able to reproduce the results reported in the paper when I use the pretrained models provided in the repo.
But when I train a densenet from scratch the results are poorer compared to the report…
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I am trying to run the model, but there's a error in this line:
features = np.concatenate([features_resnet, features_densenet, features_inception], axis=-1)
Error is caused by the inconsistent dimen…
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So far, I cannot find any memory efficient implementation DenseNet implementation on Tensorflow. In the Torch codes, there are explicit assignment of shared memory. Would you please provide some hints…
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I compared the forward pass speed of the larger ImageNet model with DenseNet-121 and the latter actually works faster. After benchmarking my guess is that CondenseConv layer is the cause of the slowdo…
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Hi, To accelerate your pose estimation, I tried training mobilenet_v2. So first, I made I copy densenet.py and changed word 'densenet' to 'mobilenet_v2' including what's in vi editor. and second, I wa…