qidiso / mobilefacenet-v2 Goto Github PK

why your emb size is 512? the result is unfair compared to the orginal paper.and the model size is too large

The project insightface has changed so much. What is the loss corresponding to this loss type?

I want to detect my custom face with name on the result window with a pre-trained model of mobilefacenets

thnx you best work!!
Why is the modification of the network model bottleneck so different from the original text? Is there any basis?some basis is importance.

MXNetError: [13:28:27] /home/travis/build/dmlc/mxnet-distro/mxnet-build/3rdparty/mshadow/../../src/operator/tensor/../elemwise_op_common.h:135: Check failed: assign(&dattr, vec.at(i)): Incompatible attr in node at 0-th output: expected [85164,512], got [85742,512]

how to find the peretrain models,i cannot find in this:
https://github.com/aidlearning/AidLearning-FrameWork/tree/master/examples/facencnn
cannot find weight files?

I was looking at the models repo and was confused. What is the difference between det1, det2, and det3 as I am trying to use caffe and are they all for mobilefacenet V2.

Could you please upload your highest accuracy pretrained models? @qidiso

I suggest you add symbol_utils.py to your code. Thank you so much.

I used train_softmax,py in https://github.com/deepinsight/insightface/blob/master/src/train_softmax.py
The parameters are set like this：
CUDA_VISIBLE_DEVICES='0' python -u train_softmax.py --network y1 --ckpt 2 --loss-type 4 --lr 0.00001 --emb-size 512 --per-batch-size 80 --wd 0.0000001 --data-dir /home/mi5/insightface/src/data/faces_vgg --pretrained /home/mi5/insightface/models/model-y1/model,00 --prefix ../models/MobileFaceNet/model-y1
Why is acc always zero？Can you help me?

As the title , can you tell me the difference between yours and mobilefacenet?
Some details，such as LeakyReLU and wd

extremely grateful |

Why I use your training model with low initial accuracy? Am I wrong?

cmd:CUDA_VISIBLE_DEVICES='0' python -u train_softmax.py --network y1 --ckpt 2 --loss-type 4 --lr 0.01 --lr-steps 40000,60000,70000 --wd 0.00004 --fc7-wd-mult 10 --emb-size 512 --per-batch-size 90 --margin-s 128 --data-dir ../datasets/faces_ms1m_112x112 --pretrained ../models/MF/model-y1-softmax12,31 --prefix ../models/MF/model-y1-arcface >>& file.txt &

first test:
testing verification..
(12000, 512)
infer time 154.665504
[lfw][2000]XNorm: 22.648476
[lfw][2000]Accuracy-Flip: 0.98717+-0.00563
testing verification..
(14000, 512)
infer time 175.866512
[cfp_fp][2000]XNorm: 19.110292
[cfp_fp][2000]Accuracy-Flip: 0.83271+-0.01969
testing verification..
(12000, 512)
infer time 157.715533
[agedb_30][2000]XNorm: 22.118053
[agedb_30][2000]Accuracy-Flip: 0.90683+-0.01981
saving 1
INFO:root:Saved checkpoint to "../models/MF/model-y1-arcface-0001.params"

Thank you very much for your work. Could you give me the link to the test dataset,such as lfw,agedb_30......

It seems that you only change output dimension from 128 to 512?

Hi, how do you resume the training with the last saved checkpoint after the training was stopped?

whatever the pair face image that I input ,the cosine sim always in around 0.60.

I see https://github.com/deepinsight/insightface/wiki/Model-Zoo the MobileFaceNet size only 4.1M, This model is too large

Thanks for your great work! Could you share me the code to evaluate the accuracy with my model for dataset LFW，CFP and AgeDB! .Thanks a million.

You have suggested three steps of training rght?, for the first step, what should be the expected model size? I am getting 180 MB, but you final model is 5 MB.
I keyed in the command you provided in the README. Is 180 MB expected or am I doing anything wrong?

Hello, you can train such a good model, what parameters do you mainly adjust, can you share your experience?