Comments (2)
lucaskyle
commented on September 26, 2024
[Val_Accuracy] epoch:210, top1:50.11000%, top5:93.61000%
maybe 600 epochs by default are not enough...
from single-path-one-shot-nas.
ShunLu91
commented on September 26, 2024
The main problem is that CIFAR-10 only has 32x32 inputs which will downsample to 1x1 after 5 downsamples in this supernet. There are two solutions for this: one is that shrink this supernet and 2 downsample layers are enough，another is that resize CIFAR-10 images to (224,224,3) just like transfer learning in EfficientNet and so on. The accuracy will increase a lot.
from single-path-one-shot-nas.
Related Issues (16)
- Why do i maintain a 10% accuracy rate when training this model? HOT 4
- 请问最终的最高精度能达到多少？ HOT 1
- 采样出一条路径后，需要对其他的路径冻结梯度更新吗？ HOT 2
- 根据经验，cifar10和imagenet对应的哪些超参需要调整？ HOT 1
- Randomly sample and save models HOT 4
- 运行完 supernet.py 后，如何进行模型加载校验(val)？ HOT 4
- BN校准 HOT 2
- 关于Mixed-Precision Quantization HOT 1
- How to do channel search? HOT 1
- 请问下，在block selection时，不同的branch之间会共享权重么？ HOT 1
- model test HOT 1
- net output dimension mismatch on cifar10 HOT 7
- 您好，关于精度复现 HOT 3
- Can not find cifar_train.py HOT 2
- Why shuffle_channels function is not used? HOT 2
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from single-path-one-shot-nas.