Comments (3)
We have given timing values for tx1 and not tx2. Also make sure you are running tx2 at its full capacity.
from enet-training.
I'm running it at full capacity. and I know it's tx1. tx2 should ideally do better than tx1. But I would be happy if it even matches it...
from enet-training.
I performed these steps to get maximum performance out of TX1. Once I get to run it on TX2, will let you know its performance on that.
Btw if you are using our visualization script to check the fps value then I must tell you that it includes so many other overheads apart from forward pass time. If you want to get only the forward pass time then use -v
verbose mode.
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
- 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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