kshitizrimal / fast-scnn Goto Github PK
View Code? Open in Web Editor NEWImplementation of Fast-SCNN using Tensorflow 2.0
Implementation of Fast-SCNN using Tensorflow 2.0
From what I understand, droupout can only benefit the layers after it, since they must predict the same output with a subset of the input. In your implementation, dropout appears after the final conv layer, and is only succeeded by an upsampling and softmax layer (which do not have parameters to learn). It seems more logical to place the dropout layer(s) between the Classification layers i.e at line 110 and 114.
Lines 105 to 121 in fcb381d
Hi
I trained the network using your code, with the same augmentation author have been used, but the result is very poor.
did you train your code yourself? what was the result?
Hello, This looks great! Any chance for pre-trained weights?
Looks like nice work, could you please post your trained weights?
the number of trainable parameters is 12,240,761, while the paper said it is 1.11 million, who is wrong?
Hello sir.
I am testing your code as semantic segmentation algorithm.
As you mention,
On tensorflow 2.0.0 (beta version).
I run your code.
I found that this error can occur on tensorflow 2.0.0.
(please check below site)
tensorflow/benchmarks#300
If you don't mind, please send your sample code (of Fast-SCNN on tensorflow 2.0.0) to me.
([email protected])
dear author,
Great!
After that, how to train and what is data?
Which value in the code refers to number of classes?? t or s??
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