I can't run this code when I want to use multi-GPU. I only change os.environ["CUDA_VISIBLE_DEVICES"] = "2,3" Is there any question?DO I need change another code?

Is it possible to update the documentation or provide some instructions on how we can reproduce GradCAM like visualizations with the code that is provided?

大佬，我在用预训练的resent50训练mcloss网络之后，得到了最终模型，我用的是你在issues中推荐的复现链接，acc为86.6，可能是batch只有32的原因。 不过我的问题不在于此。如上图所示我在可视化特征图时，发现将近有一半的特征图把注意力放在了背景信息上。我的可视化代码应该没有任何问题。请问，这种问题你有尝试过解决吗？还是说我的训练结果有问题，亦或是代码有问题？

 heat = he.data.cpu().numpy()    # heat为最后一层卷积的输出特征图
 print('heat',heat.shape)
 heat = np.squeeze(heat, 0)
 heat = heat[52*10+9,:]
 # heat = heat[2:3, :]
 heatmap = np.maximum(heat, 0)  # heatmap与0比较，取其大者
 # heatmap = np.mean(heatmap, axis=0)
 heatmap /= np.max(heatmap)
 #plt.matshow(heatmap)
 #plt.show()

 # 用cv2加载原始图像
 img = cv2.imread('/home/jim/data/FGVC/CUB_200_2011/images/053.Western_Grebe/Western_Grebe_0007_36074.jpg')
 heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))  # 将热力图的大小调整为与原始图像相同
 heatmap = np.uint8(255 * heatmap)  # 将热力图转换为RGB格式
 print(type(heatmap))

 heatmap = cv2.applyColorMap(heatmap, cv2.cv2.COLORMAP_HSV )  # 将热力图应用于原始图像
 heat_img = cv2.addWeighted(img, 1, heatmap, 0.5, 0)
 superimposed_img = heatmap * 0.3 + img * 0.7  # 这里的0.4是热力图强度因子
 cv2.imwrite('./self/heat_9.jpg', heat_img)  # 将图像保存到硬盘
 cv2.imshow('heat_11.jpg', heat_img)
 cv2.waitKey()

Hi ! I have reproduced your paper and think it's a wonderful idea to make network learning diversity feature. I have a question that there is a small amont of categories in fined-grained recognition(eg. 200 classes), but how to deal with the task that has a big amount of categories(eg. 5000 classes)? And whether it work when i make a channel to represent more than one category？

Hi authors: Could you release codes for training with ResNet18 (trained from scratch)? I find it is hard to obtain the reported scores (45.7 with CE loss) training with the hyperparameters provided in your paper. Maybe I miss some critical issues.

Great work!But I have little question about CWA.In the origianl paper, I see M_i = diag(Mask_i), where diag is putting a vector on the princial diagonal of a diagonal matrix.But in the code below:

foo = [1] * 2 + [0] *  1
bar = []
for i in range(200):
    random.shuffle(foo)
    bar += foo
bar = [bar for i in range(nb_batch)]

I think bar is not a diagonal matrix. Please point out my problem if I misunderstood the operation here.Thanks a lot.

Hi, I added mc-loss to mnasnet (https://arxiv.org/abs/1807.11626v3) network and train it on a custom fine-grained dataset. The total epoches is 15, initial LR is 2e-2, and final LR is 1e-5 using cosine LR scheduler. But the validation accuracy at epoch 15 is 0.62, while the original mnasnet implementation reaches 0.78 val accuracy at epoch 15.

The training epoches in the paper is 300, is this the cause? (my GPU is slow, so I want to experiment for less epoches to determine whether mc-loss performs good on this dataset)

Thanks for your great work!

Wonderful work! But i have some question about Ldis. In your paper, i see specific channels belong to a class(eg. channel1 to channel3 is belong to class 1 ), but how to realize in code? you don't use targets when calculating the mask

Hello,

thanks again for your interesting work. I however, struggle to reproduce your reported results on CUB200 with the Resnet50 backbone (87.3 %). If I understand https://github.com/Kurumi233/Mutual-Channel-Loss/issues/1 correctly, then you used entirely different hyperparameters than those mentioned in the paper? I also saw that you deviated (as mentioned here:https://github.com/PRIS-CV/Mutual-Channel-Loss/issues/21) from the formulas of the paper, which impacts the hyperparameters. Could you point me to the correct combination of hyperparameters and code / formulas to reproduce your results? I appreciate your help.

Greetings

Hi,

I recently did some research on fine-grained classification, and I found that you mentioned you didn't divide a validation set on CUB_200_2011 in your paper. Does it mean that when I do some experiments on CUB_200_2011, I can just take the test set as the valid set and I can take the best validation performance as my final result?

Hope to get help from you, since I can't find descriptions about the validation set in other existing papers.

Thank you.

Kind regards.

Great work!But I have a little question about λ. In your code: loss = ce_loss + args["alpha_1"] * MC_loss[0] + args["beta_1"] * MC_loss[1] And in your paper:

So,I should set beta_1 as -10 in code? Please point out my problem if I misunderstood the operation here.Thanks a lot.