Comments (3)
It is done with a lot of experiments. Heuristics!!!
This may also help you:
http://culurciello.github.io/tech/2016/06/20/training-enet.html
from enet-training.
Hi @culurciello, Thank you for your reply. That blog post was helpful. Another part I'm confused at is the concatenation part. One of the differences in the inception v4 model compared to the inception-resnet-v1 paper is the identity shortcut connection as seen in the above picture as opposed to filter concatenation in inception v4. I think ENet uses the torch operation JoinTable to achieve the concatenation. My question is do they essentially do the same thing - filter concatenation vs identity shortcut connection?
from enet-training.
yeah it is the same
from enet-training.
Related Issues (20)
- What is the learning rate decay and preprocessing you used in your training? HOT 13
- Consider hosting the pretrained model on Github HOT 1
- model-cityscapes.net released HOT 1
- what is the test time per image on cpu HOT 1
- implementation different from paper? HOT 1
- Do you freeze weights of encoder while training the whole model HOT 1
- encoder weights HOT 5
- "assertion failed!" When trying to run demo.lua
- In frameimage.lua:17: module 'fastimage' not found: HOT 2
- Not able to reproduce the fps on tx2 HOT 3
- Confused about the camVid dataset used to train encoder HOT 5
- Is the error the same as the loss HOT 1
- lua/5.2/nn/JoinTable.lua:38: bad argument #1 to 'copy' (sizes do not match at /home/user/torch/extra/cutorch/lib/THC/THCTensorCopy.cu:31) HOT 1
- add one more class to pre-trained model
- Which encoder weights should I use as CNNEncoder?
- Cityscapes Test Result HOT 1
- Assertion `t >= 0 && t < n_classes` failed, HOT 1
- Training ENet using own Dataset
- there is no mask when i try the visualization
- input and target should be of same size
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