Comments (8)
By second approach, I mean: first, directly load a normal ImageNet pre-trained model (e.g. pytorch ResNet checkpoint) and then, perform supernet training and gate training on the downstream tasks.
I did not mention this in the paper. This is a work we are currently working on.
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Hi, @NoLookDefense
Thanks for your interest. Our experiments on object detection is conducted base on another Github repo (https://github.com/lzx1413/PytorchSSD). Please also refer to issue #4
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Thank you.
I noticed that the code is recently updated two years ago. Is it just an SSD implementation, without your algorithms like slimmable NN and gating implementation?
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Yes, we currently don't have plans to release the detailed code for object detection. Sorry for any inconvenience.
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Do we need to use the pretrained weight trained from slimmer algorithm if we want to train object detection?
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Do we need to use the pretrained weight trained from slimmer algorithm if we want to train object detection?
Hi @twmht
We used the dynamic slimmable pretrained weight for our object detection results in the paper. But I expect performing slimmable training on object detection task with a normally pretrained network is also possible. (We are getting good results with the second training approach on action recognition tasks)
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What is your second training approach? Did you mention in the paper?
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did you also use in-place distilling with object detection? Which loss did you use for the classification branch?
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Related Issues (19)
- The Approximate Date for Stage II training code HOT 6
- The usage of gumbel softmax in DS-Net HOT 7
- Actual acceleration on Resnet HOT 2
- Dynamic path for DS-mobilenet HOT 1
- 运行问题 HOT 1
- MAdds of Pretrained Supernet HOT 2
- why not set ensemble_ib to True? HOT 2
- Can we futher improve autoalim without gate? HOT 3
- Softmax twice for SGS loss?
- Commands to perfrom Inference
- Question about calculating MAdds of dynamic network in the paper HOT 3
- Pretrained models HOT 2
- Object Detection HOT 2
- project environment HOT 1
- Why the num_choice in different yml is different? HOT 2
- Some issues about the gradients of slimNet HOT 6
- UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum. HOT 3
- Error of change the num_choice in mobilenetv1_bn_uniform_reset_bn.yml HOT 2
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