Comments (5)
@ycszen Image resolutions are mentioned in the paper and you can find the corresponding FLOPS using this repository.
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I have read this paper. However, it just mentioned the input image resolution. It didn't mention the upsampled image resolution, which influence the performance heavily. In my experiment, my implementation has to upsample the prediction to half of the input image, which can get comparable run-time.
from linknet.
@ycszen in our code input and target have same resolution. eg. for cityscapes input and target resolution is 1024x512
.
from linknet.
Really? I find the speed of my re-implementation is far from the result of the paper. I am doubting why the LinkNet or ENet can run so fast.
from linknet.
Use our profiler to verify the timing values.
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Related Issues (15)
- Directory structure for CamVid dataset. HOT 2
- About IoU HOT 2
- Training on camvid dataset HOT 4
- Model input/output details? HOT 2
- Error while running linknet main file
- Model fails for input size other than multiples of 32(for depth of 4) HOT 1
- about resnet-18 HOT 1
- Fine Tuning HOT 3
- Error while training HOT 4
- linknet architecture HOT 7
- Torch: not enough memory (17GB) HOT 3
- memory consuming HOT 7
- Release model HOT 2
- is there a demo.lua ? HOT 1
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from linknet.