Where to get facenet.pb, mobilenet.pb files ??? about ds

[oylz]

@RonakDedhiya
ok, thank you focusing DS, models as below(maybe I changed the filename):
(1)
facenet:
https://drive.google.com/open?id=1EXPBSXwTaqrSC0OhUdXNmKSh9qJUQ55-
from:https://github.com/davidsandberg/facenet

mobilenet:
https://1drv.ms/u/s!AvkGtmrlCEhDhy1YqWPGTMl1ybee
from:https://github.com/Zehaos/MobileNet

(2)
when I runned DS yesteryear, I use tensorflow-1.4.0-rc0.

[RonakDedhiya]

when i tried using facenet model. Model is getting loaded but session get into some error:
What eigen library version would be suitable to use?
Sources suggest that some initialization is not done or size is exceeding limit which causing this error? Any Suggestions ??

node name:InceptionResnetV1/Bottleneck/BatchNorm/Reshape_1
node name:embeddings/Square
node name:embeddings/Sum/reduction_indices
node name:embeddings/Sum
node name:embeddings/Maximum/y
node name:embeddings/Maximum
node name:embeddings/Rsqrt
node name:embeddings
session.run----rcs.size():1, ftm2-ftm1:1276216
begin====
,0.012666,-0.024307,-0.018235,0.054043,0.014510,0.054372,-0.069407,0.040447,-0.043297,-0.026317,-0.042192,0.076982,-0.043722,-0.028249,0.040141,-0.046485,0.045378,0.030071,0.022885,-0.092012,-0.043738,0.037885,0.067773,-0.020175,0.014743,0.011296,0.054080,0.020241,-0.025313,-0.017755,-0.019522,0.003134,0.020840,-0.023381,0.010793,0.043542,-0.047592,0.000124,-0.112817,0.045167,0.003669,0.045575,0.045404,-0.089537,0.034387,-0.028690,0.071568,0.048034,-0.113509,-0.024765,0.067637,0.091971,0.088102,-0.027536,-0.051581,0.032378,-0.005507,0.118291,-0.011519,0.017051,-0.045119,0.017441,0.008524,-0.036602,-0.004113,0.064810,0.015924,0.006759,0.002600,-0.021543,0.083886,0.037567,-0.022661,-0.032561,0.016954,-0.025419,0.014042,0.001886,-0.001394,0.038865,0.030672,0.042601,0.011721,-0.029743,-0.004074,0.019295,-0.011598,0.042982,-0.018859,0.042705,0.016109,0.028036,0.047609,-0.129481,0.030479,0.018293,-0.012281,0.031982,-0.024046,-0.007123,0.045696,-0.039232,-0.010257,0.015022,0.044601,0.091538,0.075012,0.003670,0.02286DS: /home/aitoe/.cache/bazel/_bazel_aitoe/84ac4d8c70e16781a4920ba7d9cd7a15/external/eigen_archive/Eigen/src/Core/DenseCoeffsBase.h:425: Eigen::DenseCoeffsBase<Derived, 1>::Scalar& Eigen::DenseCoeffsBase<Derived, 1>::operator()(Eigen::Index) [with Derived = Eigen::Matrix<float, 1, 128, 1>; Eigen::DenseCoeffsBase<Derived, 1>::Scalar = float; Eigen::Index = long int]: Assertion `index >= 0 && index < size()' failed.
9,-0.029495,0.030450,-0.011528,-0.017425,-0.007744,0.022199,0.007393,0.035167,-0.012692,-0.000504,0.056904,-0.098226,-0.018546,0.007893,-0.037520,-0.012851,-0.029646,0.003403,0.022024Aborted (core dumped)

[oylz]

It seems like the returned vector size is not 128. may be the model of FaceNet has been changed at now.
As you can think, FaceNet module updatime is:20180402, later than I used it.

you can printf the [len] variable whitch in:https://github.com/oylz/DS/blob/master/deepsort/FeatureGetter/FeatureGetter.cpp#L148
if it's not 128, Can be sure that the model has changed.

I should tell you, the FaceNet model is not good for tracking, as mine testing.

[RonakDedhiya]

yes, Length is not 128 but 512 and that the cause of problem.
Can you recommend which models worked best for you ??
I tried using tt1.pb provided in repo but its not giving identical result as got from python deep-sort implementation.
I was trying to run faceNet model so then i can write similar code for using reidentification model as in python deep-sort implementation.

[oylz]

I use deepsort model best(tt1.pb:which I translated from original checkpoint file in deepsort).In you case,Can you show detail data&result?

[RonakDedhiya]

Hello, thanks for quick reply.
I have a surveillance scenario like tracking in ATMs. I have initially used deep sort python implementation and got some reasonable good performance on ATM scenario. but our requirement is to run in cpp code.
I have few questions:

1. How far this code implements deep-sort python implementation. ( whether deep association metric is implemented ? )
2. We are using deep features , can i directly use person re-identification model instead of tt1.pb. What necessary changes would be required at higher level ?
3. In deep-sort paper,cosine metric learning is used to get best nearest neightbour result. Does any knn implementation is used for feature selection?
4. What are the hyperparameters in current code which i can use to get better result

[oylz]

1 all the codes are translated from original python version in a low level implemention，nearly close，very small difference.
2 of course, you can use any feature getter，remember the feature size is 128.
3 yes,as like [1]，it cached 100 history features every id, calculate by cosine metric and select the best id.
4 no

[RonakDedhiya]

Thanks for quick response!!!
I am currently closing the issue. Would raise issue if got stuck.