[InsightFace](https://github.com/deepinsight/insightface) is an open-source 2D&3D deep face analysis toolbox, mainly based on MXNet.
In InsightFace, it supports:
- Datasets typically used for face recognition, such as CASIA-Webface、MS1M、VGG2(Provided with the form of a binary file which could run in MXNet, [here](https://github.com/deepinsight/insightface/wiki/Dataset-Zoo) is more details about the datasets and how to download.
* Backbones of ResNet, MobilefaceNet, InceptionResNet_v2, and other deep-learning networks to apply in facial recognition.
* Implementation of different loss functions, including SphereFace Loss、Softmax Loss、SphereFace Loss, etc.
### Implementation in OneFlow
Based upon the currently existing work of Insightface, OneFlow ported basic models from it, and now OneFlow supports:
- Training datasets of MS1M、Glint360k, and validation datasets of Lfw、Cfp_fp and Agedb_30, scripts for training and validating.
- Backbones of ResNet100 and MobileFaceNet to recognize faces.
- Loss function, e.g. Softmax Loss and Margin Softmax Loss（including Arcface、Cosface and Combined Loss）.
- Model parallelism and [Partial FC](https://github.com/deepinsight/insightface/tree/760d6de043d7f654c5963391271f215dab461547/recognition/partial_fc#partial-fc) optimization.
- Model transformation via MXNet.
To be coming further:
- Additional datasets transformation.
- Plentiful backbones.
- Full-scale loss functions implementation.
- Incremental tutorial on the distributed configuration.
This project is open for every developer to PR, new implementation and animated discussion will be most welcome.
## Preparations
First of all, before execution, please make sure that:
1. Install OneFlow
2. Prepare training and validation datasets in form of OFRecord.
### Install OneFlow
According to steps in [Install OneFlow](https://github.com/Oneflow-Inc/oneflow#install-oneflow) install the newest release master whl packages.
According to [Load and Prepare OFRecord Datasets](https://docs.oneflow.org/en/extended_topics/how_to_make_ofdataset.html), datasets should be converted into the form of OFREcord, to test InsightFace.
It has provided a set of datasets related to face recognition tasks, which have been pre-processed via face alignment or other processions already in [InsightFace](https://github.com/deepinsight/insightface). The corresponding datasets could be downloaded from [here](https://github.com/deepinsight/insightface/wiki/Dataset-Zoo) and should be converted into OFRecord, which performs better in OneFlow. Considering the cumbersome steps, it is suggested to download converted OFrecord datasets:
And you will get one part of OFRecord(`part-0`) with all data in this way. Then you should use Spark to shuffle and partition.
1. Get jar package available
You can download Spark-oneflow-connector-assembly-0.1.0.jar via [Github](https://github.com/Oneflow-Inc/spark-oneflow-connector) or [OSS](https://oneflow-public.oss-cn-beijing.aliyuncs.com/spark-oneflow-connector/spark-oneflow-connector-assembly-0.1.1.jar)
2. Run in Spark
Assign that you have already installed and configured Spark.
Moreover, OneFlow offers a validation script to do verification separately, val.py, which facilitates you to check the precision of the pre-training model saved.
