Comments (7)
The pytorch version of the code perform badly on ImageNet. We do not find the reason yet. Please use the caffe version if you want to run on ImageNet.
from hashnet.
check caffe directory
from hashnet.
I just directly use your pretrained caffe's model to evaluate the mAP on ImageNet, and some results are as follows;
- 16bit: 0.4643
- 32bit: 0.5934
However, the results on your paper are as follows:
What makes this difference?
from hashnet.
Maybe it is because the caffe framework changes or other reason such as initialization. I'm sure it can reproduce the results at publishing.
from hashnet.
I retrained the model and obtain an ideal result. Thank you~
from hashnet.
I retrained the model and obtain an ideal result. Thank you~
请问是如何训练的?我用PyTorch在DSH、DHN和HashNet三个算法上复现,均是在Imagenet数据集上表现奇差。
from hashnet.
I retrained the model and obtain an ideal result. Thank you~
请问是如何训练的?我用PyTorch在DSH、DHN和HashNet三个算法上复现,均是在Imagenet数据集上表现奇差。
我之前跑过HashNet的作者提供的PyTorch版本,也自己复现过,但是在ImageNet上表现都不好,但我感觉你的表现好像比我的还更差。不过如果直接跑caffe的版本的话,结果和论文差不多。
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Related Issues (20)
- Problem with MAP@k
- 你好,非常感谢分享的NUS数据集,但是下载解压发现数据的图片比原始要少 HOT 2
- Why take two batches as input HOT 4
- the mAP on cifar10 is only 0.30 HOT 5
- the scale tanh is useless HOT 1
- a question about training set HOT 1
- How did you generate the "train.txt" of MS-COCO? HOT 1
- The pretrained model HOT 1
- nuswide dataset HOT 1
- @bfan @caozhangjie HOT 1
- question on nuswide dataset
- Pytorch with Resnet50 HOT 6
- HashNet on CUB200 HOT 1
- Anybody willing to share a pretrained model (from ImageNet or CoCo or similar)?
- AlexNet backbone result (COCO) HOT 3
- False sampling of data HOT 1
- test.py for CIFAR HOT 1
- number of training images for imagenet HOT 1
- question on nuswide81 dataset HOT 1
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