Comments (5)
你好,请问你也只是在cifar-10上得到的结果不太好而在其他数据集上结果正常吗?
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@dweiiiiiiiiii 是的,应该和你结果差不多。
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@caozhangjie Can you explain why HashNet behaves so badly on CIFAR10?
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Cifar 10 has 10 classes, I recommend replace the class_num to 10. Also, Cifar 10 needs a different backbone network. Please search for a proper backbone.
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Cifar 10 has 10 classes, I recommend replace the class_num to 10. Also, Cifar 10 needs a different backbone network. Please search for a proper backbone.
Since CIFAR 10 with 10 classes needs class_num to be 10, why COCO dataset with 80 classes is compatible with class_num=1.0? I've tried (64 bits are used.):
COCO dataset with class_num=80.0, mAP=0.53
COCO dataset with class_num=1.0, mAP=0.80 (which is higher than that in paper)
How to explain this?
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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 scale tanh is useless HOT 1
- a question about training set HOT 1
- The mAP of ImageNet is different from that on paper HOT 7
- 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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