Issues
- 1
Excuse me, what is the cause of this problem?
#77 opened by SzLSzC - 1
- 1
will the inference memory reduced too?
#75 opened by emergencyd - 1
- 2
- 2
- 0
网络内存消耗?
#63 opened by long2double - 8
- 1
How can I apply this to my own model?
#70 opened by CXMANDTXW - 1
test error interpretation
#69 opened by abduallahmohamed - 2
- 1
Is this really memory efficient?
#66 opened by leonardishere - 8
pretrained densenet169 weights
#40 opened by Kexiii - 1
New adaptive pooling layer.
#56 opened by CielAl - 3
What is bn_size?
#62 opened by w32zhong - 0
dropout not in 3x3 convolutional layer
#61 opened by lizhenstat - 1
- 1
- 0
Unable to run demo.
#58 opened by frost26k - 1
Inference time issue
#55 opened by youngwanLEE - 1
Can we test using the trained model.
#54 opened by TJstory - 0
- 1
What is the minimum GPU memory required? Still breaks for me in a single GPU
#52 opened by PabloRR100 - 1
Validation dataset is being augmented as well
#51 opened by garyfanhku - 3
does this code support pytorch1.0 and the jit feature for c++ online deployment?
#50 opened by mmxuan18 - 1
Could you add a License?
#48 opened by csrhddlam - 14
MultiGPU efficient densenets are slow
#36 opened by wandering007 - 5
- 6
- 3
- 1
Is there any option to run ImageNet in this demo?
#45 opened by jugol - 1
- 2
Softmax layer is missing in the code.
#43 opened by srinidhiPY - 2
Can't not train when using a 256*256 dataset
#42 opened by nessieyang - 6
The final test accuracy
#34 opened by ustctf-zz - 8
Multi-GPU model in pytorch0.3 consumes much more memory than pytorch0.1 version
#31 opened by ZhengRui - 5
FP become slower after upgrade to 0.4
#38 opened by DesertsP - 3
not worked in python3 environment
#37 opened by tengshaofeng - 11
Compatibility with PyTorch 0.4
#32 opened by seyiqi - 3
Test failed on PyTorch 0.3.1 with CUDA 9.0
#33 opened by DesertsP - 3
Input data size
#30 opened by g5996706 - 4
Could it be MORE memory efficient?
#22 opened by zhiqiangdon - 9
- 4
storage resize_ function
#26 opened by mingminzhen - 1
Pre-trained weight
#27 opened by soon-will - 2
Pretrained models
#23 opened by EliasVansteenkiste - 2
efficient seams not so efficient.
#21 opened by tengshaofeng - 2
Cannot reproduce the cifar100 results using models/densenet.py (not efficient)
#16 opened by wandering007 - 3
Why the Error is very high?
#20 opened by tryerrorman - 3
`DenseNetEfficientMult` not giving same forwarding result as `DenseNetEfficient`
#17 opened by ZhengRui