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About the link to the AOGNets paper in the readme, it currently points to the PDF version. It would be better if it were to point to the HTML version instead, i.e. https://arxiv.org/abs/1711.05847 . Please consider making this change. Readers can always click on the PDF link themselves.

Secondly, the link to "Mixture Normalization: A Lightweight Integration of Feature Normalization and Attention" is broken. What should it be pointing to?

./examples/train_fp16.sh aognet_s configs/aognet_imagenet_12M.yaml first_try

we use 4 P40 GPUs, so change the "train_fp16.sh" like this:

Change accordingly

GPUS=0,1,2,3
NUM_GPUS=4
NUM_WORKERS=4

the log is below:
FP16_Optimizer received torch.cuda.FloatTensor with torch.Size([206])
FP16_Optimizer received torch.cuda.HalfTensor with torch.Size([824, 206, 1, 1])
FP16_Optimizer received torch.cuda.FloatTensor with torch.Size([824])
FP16_Optimizer received torch.cuda.FloatTensor with torch.Size([824])
FP16_Optimizer received torch.cuda.FloatTensor with torch.Size([824])
FP16_Optimizer received torch.cuda.FloatTensor with torch.Size([824])
FP16_Optimizer received torch.cuda.FloatTensor with torch.Size([824])
FP16_Optimizer received torch.cuda.HalfTensor with torch.Size([1000, 824])
FP16_Optimizer received torch.cuda.FloatTensor with torch.Size([824])
FP16_Optimizer received torch.cuda.FloatTensor with torch.Size([824])
FP16_Optimizer received torch.cuda.FloatTensor with torch.Size([824])
FP16_Optimizer received torch.cuda.HalfTensor with torch.Size([1000, 824])
FP16_Optimizer received torch.cuda.HalfTensor with torch.Size([1000])
FP16_Optimizer received torch.cuda.HalfTensor with torch.Size([1000])
torch.Size([256, 3, 224, 224])
torch.Size([256, 3, 224, 224])
torch.Size([256, 3, 224, 224])
torch.Size([256, 3, 224, 224])
Epoch: [0][0/1252] Time 12.503 (12.503) Speed 81.903 (81.903) Data 0.984 (0.984) Loss 6.9218750000 (6.9219) Prec@1 0.195 (0.195) Prec@5 0.586 (0.586) lr 0.000064
Epoch: [0][100/1252] Time 2.100 (2.206) Speed 487.515 (464.191) Data 0.001 (0.011) Loss nan (nan) Prec@1 0.000 (0.003) Prec@5 0.000 (0.009) lr 0.006454
Epoch: [0][200/1252] Time 2.116 (2.155) Speed 483.939 (475.125) Data 0.001 (0.006) Loss nan (nan) Prec@1 0.000 (0.001) Prec@5 0.000 (0.004) lr 0.012843
Epoch: [0][300/1252] Time 2.103 (2.137) Speed 487.036 (479.160) Data 0.001 (0.004) Loss nan (nan) Prec@1 0.000 (0.001) Prec@5 0.000 (0.003) lr 0.019233

Hello! I'm interested in this work. I use AOGNets to faster R-CNN as your paper intorduced. I use the code from https://github.com/jwyang/faster-rcnn.pytorch/tree/pytorch-1.0. And I make a aog.py in lib/model/faster_rcnn you do. The issue is that when I run train_val to train the model end to end, The loss is so high normally. I want to know where is the problem. Is it related to FP-optimizer? If yes, what should I do for this issues?
I'm looking forward to your reply!
Best wishes!

I use the pretrained model in your google driver.

https://arxiv.org/pdf/1711.05847v3.pdf says:

The code and models are available at https://github.com/iVMCL/AOGNets

So where are they? The last update was over a month ago. It has been over 1.5 years since its first preprint, and there is evidently no code to show for it. Multiple grants have been used too.