-
Notifications
You must be signed in to change notification settings - Fork 217
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
About run_cifar10 #22
Comments
It's coarse-level search. In MNIST and CIFAR10 experiments, we evaluate the performance with only binary codes in order to fairly compare with other hashing approaches. |
In the And when I used Netscope to view
Thank you. @kevinlin311tw |
We first resize images to 256x256, and then center-crop 227x227 as network input.
… On 2017年7月28日, at 上午2:17, 111Moderato ***@***.***> wrote:
name: "KevinNet_CIFAR10" layers { layer { name: "data" type: "data" source: "cifar10_train_leveldb" meanfile: "../../data/ilsvrc12/imagenet_mean.binaryproto" batchsize: 32 cropsize: 227 mirror: true det_context_pad: 16 det_crop_mode: "warp" det_fg_threshold: 0.5 det_bg_threshold: 0.5 det_fg_fraction: 0.25 } top: "data" top: "label" }
In the train_CIFAR10_48.prototxt, why cropsize is 227? Is the size of cifar 32*32 ?
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.
|
Do you have an experiment on NUS-WIDE ? @kevinlin311tw |
Yes. You can take a look the extension of this work here:
https://arxiv.org/abs/1507.00101v2
Our workshop version can be seen as our proposed SSDH with the hyper
parameters: alpha = 1, beta = 0, gamma = 0.
2017-09-05 22:02 GMT-07:00 111Moderato <[email protected]>:
… Do you have an experiment on NUS-WIDE ? @kevinlin311tw
<https://github.com/kevinlin311tw>
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<#22 (comment)>,
or mute the thread
<https://github.com/notifications/unsubscribe-auth/AJgtMLoJ5dPSNUOnpX0R3ARU-3yxQBmMks5sfid_gaJpZM4OhMvK>
.
--
Best regards,
林可昀
Kevin Lin
|
Uh.. Is it correct for me to use SigmoidCrossEntropyLoss on multi-label dataset? @kevinlin311tw |
Oh.. I forgot that NUS-WIDE is a multi-label dataset..
Yes. You are right. Since this workshop version is trained with softmax
loss, we cannot directly apply it on multi-label dataset. In this case,
since the label is the n-hot vector, we should use the loss function you
mentioned.
2017-09-07 10:02 GMT-07:00 111Moderato <[email protected]>:
… Uh.. Is it correct for me to use SigmoidCrossEntropyLoss on multi-label
dataset? @kevinlin311tw <https://github.com/kevinlin311tw>
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<#22 (comment)>,
or mute the thread
<https://github.com/notifications/unsubscribe-auth/AJgtMPYJSnq4dfm_th_70-dnObyZFLRyks5sgCGrgaJpZM4OhMvK>
.
--
Best regards,
林可昀
Kevin Lin
|
@111Moderato I also meet this mistick. Do you fix it? Warning |
I forgot whether I had met the mistake. I'm using another version of Caffe, so I can run it properly after the prototxt changes |
@111Moderato Thanks a lot! |
The baidu link of model and image resource is invalid, can anyone share it? |
After we execute the command
>> run_cifar10
, we get the>> MAP = 0.897373
Is this the result after reranking ? I mean the Fine-level Search.
@kevinlin311tw
The text was updated successfully, but these errors were encountered: