Please check out the documentation here.
sergeyk / vislab Goto Github PK
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Home Page: http://sergeykarayev.com/vislab/
License: Other
Set of modules and datasets for visual recognition.
Home Page: http://sergeykarayev.com/vislab/
License: Other
Please check out the documentation here.
Hi Sergey - I noticed in /vislab/feature.py there is a compute command which appears to load the features. I'd just like to confirm the end-to-end procedure of adding my own dataset and getting features into the db.
So - follow the "Adding your own" section of the dataset documentation:
Hi, Sergey.
I have one question regarding this part of code in run_DARTS.m:
[~, ~, val_dec_values] = predict(val_labels, val_data.betas, model, '-b 0', 'col');
while executing predict it fails with:
predict(val_labels, val_data.betas, model, '-b 0', 'col');
Error using predict (line 84)
Systems of double class cannot be used with the "predict" command. Convert the system to an identified model first, such
as by using the "idss" command.
model has the following structure
>> model
model =
Parameters: 2
nr_class: 57
nr_feature: 100000
bias: -1
Label: [57x1 double]
w: [57x100000 double]
>> numel(val_labels)
ans =
2850
>> numel(val_data.betas)
ans =
285000000
What exactly should be done to avoid this issue?
Thanks in advance. Regards. Taras.
I try to download ava dataset by "python datasets/ava.py", but the error information is shown as "ImportError: No module named vislab". Where can i find the vislab module.
Thanks!
It seems the main method to load Caffe's CNN features is from the caffe function in vislab / features / misc.py. I have several questions on performance:
Thank you Sergey!
Hi Sergey,
A basic question here. In /vislab/features/misc.py, line 60+, you wrote:
# First, run the network fully forward by calling predict.
# Then, for whatever blob we want, max across image crops.
net.predict([caffe.io.load_image(image_filename)])
feats.append(net.blobs[layer].data.max(0).flatten())
I'd like to understand why you chose the "max" crop. Firstly, what does it mean to be the "max across image crops"? In the Caffe feature visualization tutorial it indicates one can just select the center crop. In other discussions people have mentioned that one should "average the crops." Would you mind to compare your "max across image crops" with the aforementioned alternatives and why is it the best way to go and / or pros/cons?
Thanks!
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