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161 lines
7.8 KiB
Markdown
161 lines
7.8 KiB
Markdown
## Partial-FC
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Partial FC is a distributed deep learning training framework for face recognition. The goal of Partial FC is to facilitate large-scale classification task (e.g. 10 or 100 million identities). It is much faster than the model parallel solution and there is no performance drop.
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## Contents
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[Partial FC](https://arxiv.org/abs/2203.15565)
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- [Largest Face Recognition Dataset: **Glint360k**](#Glint360K)
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- [Docker](#Docker)
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- [Performance On Million Identities](#Benchmark)
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- [FAQ](#FAQ)
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- [Citation](#Citation)
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## Glint360K
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We clean, merge, and release the largest and cleanest face recognition dataset Glint360K,
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which contains **`17091657`** images of **`360232`** individuals.
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By employing the Patial FC training strategy, baseline models trained on Glint360K can easily achieve state-of-the-art performance.
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Detailed evaluation results on the large-scale test set (e.g. IFRT, IJB-C and Megaface) are as follows:
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### 1. Evaluation on IFRT
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**`r`** denotes the sampling rate of negative class centers.
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| Backbone | Dataset | African | Caucasian | Indian | Asian | ALL |
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| ------------ | ----------- | ----- | ----- | ------ | ----- | ----- |
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| R50 | MS1M-V3 | 76.24 | 86.21 | 84.44 | 37.43 | 71.02 |
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| R124 | MS1M-V3 | 81.08 | 89.06 | 87.53 | 38.40 | 74.76 |
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| R100 | **Glint360k**(r=1.0) | 89.50 | 94.23 | 93.54 | **65.07** | **88.67** |
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| R100 | **Glint360k**(r=0.1) | **90.45** | **94.60** | **93.96** | 63.91 | 88.23 |
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### 2. Evaluation on IJB-C and Megaface
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We employ ResNet100 as the backbone and CosFace (m=0.4) as the loss function.
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TAR@FAR=1e-4 is reported on the IJB-C datasets, and TAR@FAR=1e-6 is reported on the Megaface dataset.
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|Test Dataset | IJB-C | Megaface_Id | Megaface_Ver |
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| :--- | :---: | :---: | :---: |
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| MS1MV2 | 96.4 | 98.3 | 98.6 |
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|**Glint360k** | **97.3** | **99.1** | **99.1** |
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### 3. License
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The Glint360K dataset (and the models trained with this dataset) are available for non-commercial research purposes only.
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### 4. Download
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- [x] [**Baidu Drive**](https://pan.baidu.com/s/1GsYqTTt7_Dn8BfxxsLFN0w) (code:o3az)
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- [x] **Magnet URI**: `magnet:?xt=urn:btih:E5F46EE502B9E76DA8CC3A0E4F7C17E4000C7B1E&dn=glint360k`
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Refer to the following command to unzip.
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```
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cat glint360k_* | tar -xzvf -
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# Don't forget the last '-'!
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# cf7433cbb915ac422230ba33176f4625 glint360k_00
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# 589a5ea3ab59f283d2b5dd3242bc027a glint360k_01
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# 8d54fdd5b1e4cd55e1b9a714d76d1075 glint360k_02
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# cd7f008579dbed9c5af4d1275915d95e glint360k_03
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# 64666b324911b47334cc824f5f836d4c glint360k_04
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# a318e4d32493dd5be6b94dd48f9943ac glint360k_05
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# c3ae1dcbecea360d2ec2a43a7b6f1d94 glint360k_06
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# md5sum:
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# 5d9cd9f262ec87a5ca2eac5e703f7cdf train.idx
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# 8483be5af6f9906e19f85dee49132f8e train.rec
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```
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Use [unpack_glint360k.py](./unpack_glint360k.py) to unpack.
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### 5. Pretrain models
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- [x] [**Baidu Drive**](https://pan.baidu.com/s/1sd9ZRsV2c_dWHW84kz1P1Q) (code:befi)
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- [x] [**Google Drive**](https://drive.google.com/drive/folders/1WLjDzEs1wC1K1jxDHNJ7dhEmQ3rOOILl?usp=sharing)
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| Framework | backbone | negative class centers sample_rate | IJBC@e4 | IFRT@e6 |
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| :--- | :--- | :--- | :--- | :--- |
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| mxnet | [R100](https://drive.google.com/drive/folders/1YPqIkOZWrmbli4GWfMJO2b0yiiZ7UCsP?usp=sharing) |1.0|97.3|-|
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| mxnet | [R100](https://drive.google.com/drive/folders/1-gF5sDwNoRcjwmpPSTNLpaZJi5N91BvL?usp=sharing) |0.1|97.3|-|
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| pytorch | [R50](https://drive.google.com/drive/folders/16hjOGRJpwsJCRjIBbO13z3SrSgvPTaMV?usp=sharing) |1.0|97.0|-|
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| pytorch | [R100](https://drive.google.com/drive/folders/19EHffHN0Yn8DjYm5ofrgVOf_xfkrVgqc?usp=sharing) |1.0|97.4|-|
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## Docker
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Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to
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install the CUDA Toolkit and other independence on the host system, but the NVIDIA driver needs to be installed.
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Because the CUDA version used in the image is 10.1,
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the graphics driver version on the physical machine must be greater than 418.
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### 1. Docker Getting Started
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You can use dockerhub or offline docker.tar to get the image of the Partial-fc.
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1. dockerhub
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```shell
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docker pull insightface/partial_fc:v1
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```
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2. offline images
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coming soon!
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### 2. Getting Started
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```shell
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sudo docker run -it -v /train_tmp:/train_tmp --net=host --privileged --gpus 8 --shm-size=1g insightface/partial_fc:v1 /bin/bash
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```
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`/train_tmp` is where you put your training set (if you have enough RAM memory,
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you can turn it into `tmpfs` first).
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## Benchmark
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### 1. Train Glint360K Using MXNET
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| Backbone | GPU | FP16 | BatchSize / it | Throughput img / sec |
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| :--- | :--- | :--- | :--- | :--- |
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| R100 | 8 * Tesla V100-SXM2-32GB | False | 64 | 1748 |
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| R100 | 8 * Tesla V100-SXM2-32GB | True | 64 | 3357 |
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| R100 | 8 * Tesla V100-SXM2-32GB | False | 128 | 1847 |
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| R100 | 8 * Tesla V100-SXM2-32GB | True | 128 | 3867 |
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| R50 | 8 * Tesla V100-SXM2-32GB | False | 64 | 2921 |
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| R50 | 8 * Tesla V100-SXM2-32GB | True | 64 | 5428 |
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| R50 | 8 * Tesla V100-SXM2-32GB | False | 128 | 3045 |
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| R50 | 8 * Tesla V100-SXM2-32GB | True | 128 | 6112 |
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### 2. Performance On Million Identities
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We neglect the influence of IO. All experiments use mixed-precision training, and the backbone is ResNet50.
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#### 1 Million Identities On 8 RTX2080Ti
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|Method | GPUs | BatchSize | Memory/M | Throughput img/sec | W |
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| :--- | :---: | :---: | :---: | :---: | :---: |
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| Model Parallel | 8 | 1024 | 10408 | 2390 | GPU |
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| **Partial FC(Ours)** | **8** | **1024** | **8100** | **2780** | GPU |
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#### 10 Million Identities On 64 RTX2080Ti
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|Method | GPUs | BatchSize | Memory/M | Throughput img/sec | W |
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| :--- | :---: | :---: | :---: | :---: | :---: |
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| Model Parallel | 64 | 2048 | 9684 | 4483 | GPU |
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| **Partial FC(Ours)** | **64** | **4096** | **6722** | **12600** | GPU |
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## FAQ
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#### Glint360K's Face Alignment Settings?
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We use a same alignment setting with MS1MV2, code is [here](https://github.com/deepinsight/insightface/issues/1286).
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#### Why update Glint360K, is there a bug in the previous version?
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In the previous version of Glint360K, there is no bug when using softmax training, but there is a bug in triplet training.
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In the latest Glint360k, this bug has been fixed.
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#### Dataset in Google Drive or Dropbox?
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The torrent has been released.
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## Citation
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If you find Partial-FC or Glint360K useful in your research, please consider to cite the following related paper:
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[Partial FC](https://arxiv.org/abs/2203.15565)
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```
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@inproceedings{an2022pfc,
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title={Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC},
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author={An, Xiang and Deng, Jiangkang and Guo, Jia and Feng, Ziyong and Zhu, Xuhan and Jing, Yang and Tongliang, Liu},
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booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
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year={2022}
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}
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```
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