songdejia / dfl-cnn Goto Github PK

the train and test process is right on CUB200_2011. the test result is 78%

but the test result is wrong on my dataset, it seem to right rate between top1(33%) and top5(40%), i have no idea why top5 is always 40%? my dadaset have 20 classes.

我在两块Titan xp上，epoch120，batchsize14，效果不好,还有作者给的权重，怎么直接用？初学者，问题有点多，求解答~~

Hi, thanks for your kind work for implementing the CVPR18 paper DFL-CNN for the community. I notice that your code did not implement the non-random initialization part which the authors claimed it was very important. I found some similar issues like mine, and can I discuss some ideas about non-random initialization with your? In the original paper, the author first calculate the conv4_3 feature map and got C * H * W feature maps. Then they calculated l2-norm along the channel dimension and got H * W heat map. If I did not understand wrongly till now, how should I understand their following operations: for each class i, obtain the initialization weights for k 1 * 1 conv filters by non-maximium suppression and k-means? Does it mean the author first calculate the feature maps of all images in the training set and obtain C_k heat maps per class and performance non-maxium suppression and k-means over the peak regions in the C_k heat maps?

Furthermore, when I ran your code, I only got 56.6% test accuracy in the test set, I did not know what the problem was and it really confused me for a couple day, could you please help me tackle it?

Thanks for your job!

Hi @songdejia ,

First of all great repo 👍 I am a little confused in this part of code.
p_tl = (8*col, 8*row) p_br = (col*8+92, row*8+92)

I believe this comes from the paper "the output of the 10th convolutional layer conv4_3 represents patches as small as 92 × 92 with stride 8paper ". I didn't get how the author arrive here?
Maybe a novice question but your help is appreciated.
Thanks

Can someone share the weights via other media (via google drive/dropbox)? Not able to download from the Baidu site. Thanks

您好，请问单张1080ti可以跑这个网络吗，我看您用的是四张泰坦v，我应该如何修改参数，才能满足单张1080ti的显存使用？

hi there,
im quite interested in your work and i have try your code some days ago, however, i cant get the same result as the that in the paper.
These days, i notice that you are rebuilding your code these days, would u plz inform me how soon can you finish it? I`m just cant wait to try it.
Thanks a lot!

Ian

Hello, thank you very much for sharing the code, why I use your code training only 42%

Hello,

I cloned your repo and downloaded your dataset, but I could not get the same result as yours.

I train the model on 4 GeForce GTX 1080 Ti GPUs, and keep other arguments as the same. But it turns out to be overfitting.

After 100 epochs, I got prec@1 at 99% and prec@5 at 100% on the train set, but only prec@1 at 48% and prec@5 at 73% on the test set.

Here is part of the log:

DFL-CNN <==> Train <==> Epoch: [113][103/107]
Loss 0.5459 (0.5661)	Loss1 0.0756 (0.0739)	Loss2 0.0004 (0.0289)	Loss3 4.6985 (4.6324)
Prec@1 (99.880)	Prec@5 (100.000)
DFL-CNN <==> Train <==> Epoch: [113][104/107]
Loss 0.5188 (0.5656)	Loss1 0.0508 (0.0737)	Loss2 0.0076 (0.0287)	Loss3 4.6041 (4.6321)
Prec@1 (99.881)	Prec@5 (100.000)
DFL-CNN <==> Train <==> Epoch: [113][105/107]
Loss 0.5771 (0.5657)	Loss1 0.0774 (0.0737)	Loss2 0.0189 (0.0286)	Loss3 4.8082 (4.6338)
Prec@1 (99.882)	Prec@5 (100.000)
DFL-CNN <==> Train <==> Epoch: [113][106/107]
Loss 0.8110 (0.5672)	Loss1 0.3277 (0.0753)	Loss2 0.0100 (0.0285)	Loss3 4.7324 (4.6344)
Prec@1 (99.883)	Prec@5 (100.000)

DFL-CNN <==> Test <==> Epoch: [ 106] Top1:48.050% Top5:73.093%
DFL-CNN <==> Test <==> Epoch: [ 108] Top1:48.913% Top5:73.404% 
DFL-CNN <==> Test <==> Epoch: [ 110] Top1:47.515% Top5:72.575%
DFL-CNN <==> Test <==> Epoch: [ 112] Top1:48.205% Top5:72.765%

During training the model, overfitting problem is inevitable. Because there are only about 6000 images for training, but there are too many parameters in VGG16.

Have you ever met the overfitting problem? And how did you get rid of it?

Looking forward to your reply!

Thank you very much!

DFL-CNN <==> Part1 : prepare for parameters <==> Begin
DFL-CNN <==> Part1 : prepare for parameters <==> Done
DFL-CNN <==> Part2 : Load Network <==> Begin
Traceback (most recent call last):
File "main.py", line 180, in
main()
File "main.py", line 91, in main
model = DFL_VGG16(k = 10, nclass = 200)
File "/home/steven/Documents/test/DFL-CNN/model/DFL.py", line 13, in init
vgg16featuremap = torchvision.models.vgg16_bn(pretrained=True).features
File "/usr/local/lib/python3.5/dist-packages/torchvision/models/vgg.py", line 130, in vgg16_bn
return VGG(make_layers(cfg['D'], batch_norm=True), **kwargs)
TypeError: init() got an unexpected keyword argument 'pretrained'

Hello dejia
when I reappearance your code this error happened ,Where is the problem?

Thanks for clicking into this issue!

If you get 85.14% as the final accuracy, as reported in the homepage, would you please share the loss value when the program reached the best result, the initial learning rate, and the transform of the data?😊😊

I only get 78.6% at best. Every time I run my program, it seems like I will get random results, ranging from 74.0% ~ 78.6%.

The updates I have done:
Resnet-50 based model & no-random initialization

I think my pytorch version is not the same as your work, so I can not get your result of 85.14%. I just get 70%. Can you tell me the version of pytorch which you use for this code?

你好，这个代码得到了与论文中一致的结果吗？

have reading paper and the source code, i find initialization don't contain none-random initialization part. Does it exist? :-)
And how much accuracy will loss if don't do none-random initialization? Thank you ~

What are the training hyper parameters to get top1 acc>0.87, like batch, lr strategy? and how many epochs do you run?
I can only get 0.83 on the CUB_200_2001. I divided the data sets to 70% training, 30% validation.
Please do not close the issues so fast.
Thanks.