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https://github.com/auroua/InsightFace_TF
Insight Face on TensorFlow
https://github.com/auroua/InsightFace_TF
facerecognition tensorflow tensorlayer
Last synced: 5 days ago
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Insight Face on TensorFlow
- Host: GitHub
- URL: https://github.com/auroua/InsightFace_TF
- Owner: auroua
- License: mit
- Created: 2018-03-03T01:10:35.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-08-14T03:36:46.000Z (about 5 years ago)
- Last Synced: 2024-08-02T20:44:29.480Z (3 months ago)
- Topics: facerecognition, tensorflow, tensorlayer
- Language: Python
- Homepage:
- Size: 357 KB
- Stars: 697
- Watchers: 45
- Forks: 253
- Open Issues: 76
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-tensorlayer - InsignFace - Additive Angular Margin Loss for Deep Face Recognition (2. General Computer Vision / 1.2 DatasetAPI and TFRecord Examples)
README
## Insight Face in TensorFlow
#### Tasks
* ~~mxnet dataset to tfrecords~~
* ~~backbone network architectures [vgg16, vgg19, resnet]~~
* ~~backbone network architectures [resnet-se, resnext]~~
* ~~LResNet50E-IR~~
* ~~LResNet100E-IR~~
* ~~Additive Angular Margin Loss~~
* ~~CosineFace Loss~~
* ~~train network code~~
* ~~add validate during training~~
* ~~multi-gpu training~~
* ~~combine losses~~ contributed by RogerLo.
* evaluate code
#### Training Tips(Continual updates)
* If you can't use large batch size(>128), you should use small learning rate
* If you can't use large batch size(>128), you can try batch renormalization(file `L_Resnet_E_IR_RBN.py`)
* If use multiple gpus, you should keep at least 16 images each gpu.
* Try [Group Normalization](https://arxiv.org/pdf/1803.08494.pdf), you can use the code `L_Resnet_E_IR_GBN.py`
* Using the current model, and the lr schedule in `train_nets.py`, you can get the results as `model c`
* The bug about model size is 1.6G have fixed based on issues #9. If you want to get a small model, you should use `L_Resnet_E_IR_fix_issues9.py`
* multi-gpu training code's bug have fixed. If you want to use the correct version, you should use `train_nets_mgpu_new.py`
#### Training models (Continual updates)
##### model A
| model name | depth| normalization layer |batch size| total_steps | download | password |
| ----- |:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
| model A | 50 |group normalization|16| 1060k |[model a](https://pan.baidu.com/s/1qWrDCTFlQXlFcBR-dqR-6A)|2q72|
###### accuracy
| dbname | accuracy |
| ----- |:-----:|
| lfw |0.9897|
| cfp_ff |0.9876|
| cfp_fp |0.84357|
| age_db30 |0.914|
##### model B
| model name | depth| normalization layer |batch size| total_steps| download | password |
| ----- |:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
| model B | 50 |batch normalization|16| 1100k |[model_b](https://pan.baidu.com/s/11KDqOkF4ThO7mnQQaNO9bA) |h6ai|
###### accuracy
| dbname | accuracy |
| ----- |:-----:|
| lfw |0.9933|
| cfp_ff |0.99357|
| cfp_fp |0.8766|
| age_db30 |0.9342|
##### model C
| model name | depth| normalization layer |batch size| total_steps| download | password |
| ----- |:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
| model C | 50 |batch normalization|16| 1950k |[model_c](https://pan.baidu.com/s/1ZlDcQPBh0znduSH6vQ_Q8Q) |8mdi|
###### accuracy
| dbname | accuracy |
| ----- |:-----:|
| lfw |0.9963|
| cfp_ff |0.99586|
| cfp_fp |0.9087|
| age_db30 |0.96367|
##### model D
| model name | depth| normalization layer |batch size| total_steps| model_size| download | password |
| ----- |:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
| model D | 50 |batch normalization|136| 710k | 348.9MB |[model_d](https://pan.baidu.com/s/1tQYMqfbG36wg1cXKHVoMVw) |amdt|
###### accuracy
| dbname | accuracy |
| ----- |:-----:|
| lfw |0.9968|
| cfp_ff |0.9973|
| cfp_fp |0.9271|
| age_db30 |0.9725|
#### Requirements
1. TensorFlow 1.4 1.6
2. TensorLayer 1.7
3. cuda8&cudnn6 or cuda9&cudnn7
4. Python3
#### Max Batch Size Test
###### Environment
| GPU | cuda| cudnn | TensorFlow |TensorLayer|Maxnet |Gluon|
| ----- |:-----:|:-----:|:------:|:---:|:------:|:---:|
| Titan xp | 9.0 |7.0|1.6|1.7 |1.1.0|1.1.0 |
###### Results
| DL Tools | Max BatchSize(without bn and prelu)| Max BatchSize(with bn only) | Max BatchSize(with prelu only) |Max BatchSize(with bn and prelu)|
| ------------- |:-------------:|:--------------:|:------------:|:------------:|
| TensorLayer | (8000, 9000) |(5000, 6000)|(3000, 4000)|(2000, 3000) |
| Mxnet | (40000, 50000) |(20000, 30000)|(20000, 30000)|(10000, 20000) |
| Gluon | (7000, 8000) |(3000, 4000)|no official method| None |
> (8000, 9000) : 8000 without OOM, 9000 OOM Error
###### Test Code
|TensorLayer| Maxnet | Gluon |
| ----- |:-----:|:-----:|
| [tensorlayer_batchsize_test.py](https://github.com/auroua/InsightFace_TF/blob/master/test/benchmark/tensorlayer_batchsize_test.py) | [mxnet_batchsize_test.py](https://github.com/auroua/InsightFace_TF/blob/master/test/benchmark/mxnet_batchsize_test.py) |[gluon_batchsize_test.py](https://github.com/auroua/InsightFace_TF/blob/master/test/benchmark/gluon_batchsize_test.py)|
#### pretrained model download link
* [resnet_v1_50](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)
* [resnet_v1_101](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz)
* [resnet_v1_152](http://download.tensorflow.org/models/resnet_v1_152_2016_08_28.tar.gz)
* [vgg16](http://www.cs.toronto.edu/~frossard/post/vgg16/)
* [vgg19](https://github.com/machrisaa/tensorflow-vgg)
#### References
1. [InsightFace mxnet](https://github.com/deepinsight/insightface)
2. [InsightFace : Additive Angular Margin Loss for Deep Face Recognition](https://arxiv.org/abs/1801.07698)
3. [Group Normalization](https://arxiv.org/pdf/1803.08494.pdf)
3. [tensorlayer_vgg16](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_vgg16.py)
4. [tensorlayer_vgg19](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_vgg19.py)
5. [tf_slim](https://github.com/tensorflow/models/tree/master/research/slim)
6. [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
7. [Very Deep Convolutional Networks For Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)
8. [Squeeze-and-Excitation Networks](https://arxiv.org/pdf/1709.01507.pdf)