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insightface/model_zoo/README.md
Jia Guo 8f1f7cada9 buffalo_l as default model pack
2021-09-21 10:52:19 +08:00

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# InsightFace Model Zoo
:bell: **ALL models are available for non-commercial research purposes only.**
## 0. Python Package models
To check the detail of insightface python package, please see [here](../python-package).
To install: ``pip install -U insightface``
To use the specific model pack:
```
model_pack_name = 'buffalo_l'
app = FaceAnalysis(name=model_pack_name)
```
Name in **bold** is the default model pack in latest version.
| Name | Detection Model | Recognition Model | Alignment | Attributes | Model-Size |
| -------------- | --------------- | ------------------- | ------------ | ---------- | ---------- |
| antelopev2 | RetinaFace-10GF | ResNet100@Glint360K | 2d106 & 3d68 | Gender&Age | 407MB |
| **buffalo_l** | RetinaFace-10GF | ResNet50@WebFace600K | 2d106 & 3d68 | Gender&Age | 326MB |
| buffalo_m | RetinaFace-2.5GF | ResNet50@WebFace600K | 2d106 & 3d68 | Gender&Age | 313MB |
| buffalo_s | RetinaFace-500MF | MBF@WebFace600K | 2d106 & 3d68 | Gender&Age | 159MB |
| buffalo_sc | RetinaFace-500MF | MBF@WebFace600K | - | - | 16MB |
### Recognition accuracy of python library model packs:
| Name | MR-ALL | African | Caucasian | South Asian | East Asian | LFW | CFP-FP | AgeDB-30 | IJB-C(E4) |
| :-------- | ------ | ------- | --------- | ----------- | ---------- | ------ | ------ | -------- | --------- |
| buffalo_l | 91.25 | 90.29 | 94.70 | 93.16 | 74.96 | 99.83 | 99.33 | 98.23 | 97.25 |
| buffalo_s | 71.87 | 69.45 | 80.45 | 73.39 | 51.03 | 99.70 | 98.00 | 96.58 | 95.02 |
*buffalo_m has the same accuracy with buffalo_l.*
*buffalo_sc has the same accuracy with buffalo_s.*
(Note that almost all ONNX models in our model_zoo can be called by python library.)
## 1. Face Recognition models.
### Definition:
The default training loss is margin based softmax if not specified.
``MFN``: MobileFaceNet
``MS1MV2``: MS1M-ArcFace
``MS1MV3``: MS1M-RetinaFace
``MS1M_MegaFace``: MS1MV2+MegaFace_train
``_pfc``: using Partial FC, with sample-ratio=0.1
``MegaFace``: MegaFace identification test, with gallery=1e6.
``IJBC``: IJBC 1:1 test, under FAR<=1e-4.
``BDrive``: BaiduDrive
``GDrive``: GoogleDrive
### List of models by MXNet and PaddlePaddle:
| Backbone | Dataset | Method | LFW | CFP-FP | AgeDB-30 | MegaFace | Link. |
| -------- | ------- | ------- | ----- | ------ | -------- | -------- | ------------------------------------------------------------ |
| R100 (mxnet) | MS1MV2 | ArcFace | 99.77 | 98.27 | 98.28 | 98.47 | [BDrive](https://pan.baidu.com/s/1wuRTf2YIsKt76TxFufsRNA), [GDrive](https://drive.google.com/file/d/1Hc5zUfBATaXUgcU2haUNa7dcaZSw95h2/view?usp=sharing) |
| MFN (mxnet) | MS1MV1 | ArcFace | 99.50 | 88.94 | 95.91 | - | [BDrive](https://pan.baidu.com/s/1If28BkHde4fiuweJrbicVA), [GDrive](https://drive.google.com/file/d/1RHyJIeYuHduVDDBTn3ffpYEZoXWRamWI/view?usp=sharing) |
| MFN (paddle) | MS1MV2 | ArcFace | 99.45 | 93.43 | 96.13 | - | [pretrained model](https://paddle-model-ecology.bj.bcebos.com/model/insight-face/MobileFaceNet_128_v1.0_pretrained.tar), [inference model](https://paddle-model-ecology.bj.bcebos.com/model/insight-face/mobileface_v1.0_infer.tar) |
| iResNet50 (paddle) | MS1MV2 | ArcFace | 99.73 | 97.43 | 97.88 | - | [pretrained model](https://paddle-model-ecology.bj.bcebos.com/model/insight-face/arcface_iresnet50_v1.0_pretrained.tar), [inference model](https://paddle-model-ecology.bj.bcebos.com/model/insight-face/arcface_iresnet50_v1.0_infer.tar) |
### List of models by various depth IResNet and training datasets:
| Backbone | Dataset | MR-ALL | African | Caucasian | South Asian | East Asian | Link(onnx) |
|----------|-----------|--------|---------|-----------|-------------|------------|-----------------------------------------------------------------------|
| R100 | Casia | 42.735 | 39.666 | 53.933 | 47.807 | 21.572 | [GDrive](https://drive.google.com/file/d/1WOrOK-qZO5FcagscCI3td6nnABUPPepD/view?usp=sharing) |
| R100 | MS1MV2 | 80.725 | 79.117 | 87.176 | 85.501 | 55.807 | [GDrive](https://drive.google.com/file/d/1772DTho9EG047KNUIv2lop2e7EobiCFn/view?usp=sharing) |
| R18 | MS1MV3 | 68.326 | 62.613 | 75.125 | 70.213 | 43.859 | [GDrive](https://drive.google.com/file/d/1dWZb0SLcdzr-toUzsVZ1zogn9dEIW1Dk/view?usp=sharing) |
| R34 | MS1MV3 | 77.365 | 71.644 | 83.291 | 80.084 | 53.712 | [GDrive](https://drive.google.com/file/d/1ON6ImX-AigDKAi4pelFPf12vkJVyGFKl/view?usp=sharing) |
| R50 | MS1MV3 | 80.533 | 75.488 | 86.115 | 84.305 | 57.352 | [GDrive](https://drive.google.com/file/d/1FPldzmZ6jHfaC-R-jLkxvQRP-cLgxjCT/view?usp=sharing) |
| R100 | MS1MV3 | 84.312 | 81.083 | 89.040 | 88.082 | 62.193 | [GDrive](https://drive.google.com/file/d/1fZOfvfnavFYjzfFoKTh5j1YDcS8KCnio/view?usp=sharing) |
| R18 | Glint360K | 72.074 | 68.230 | 80.575 | 75.852 | 47.831 | [GDrive](https://drive.google.com/file/d/1Z0eoO1Wqv32K8TdFHKqrlrxv46_W4390/view?usp=sharing) |
| R34 | Glint360K | 83.015 | 79.907 | 88.620 | 86.815 | 60.604 | [GDrive](https://drive.google.com/file/d/1G1oeLkp_b3JA_z4wGs62RdLpg-u_Ov2Y/view?usp=sharing) |
| R50 | Glint360K | 87.077 | 85.272 | 91.617 | 90.541 | 66.813 | [GDrive](https://drive.google.com/file/d/1MpRhM76OQ6cTzpr2ZSpHp2_CP19Er4PI/view?usp=sharing) |
| R100 | Glint360K | 90.659 | 89.488 | 94.285 | 93.434 | 72.528 | [GDrive](https://drive.google.com/file/d/1Gh8C-bwl2B90RDrvKJkXafvZC3q4_H_z/view?usp=sharing) |
### List of models by IResNet-50 and different training datasets:
| Dataset | MR-ALL | African | Caucasian | South Asian | East Asian | LFW | CFP-FP | AgeDB-30 | IJB-C(E4) | Link(onnx) |
| :-------- | ------ | ------- | ---- | ------ | -------- | ----- | ------ | -------- | --------- | --- |
| CISIA | 36.794 | 42.550 | 55.825 | 49.618 | 19.611 | 99.450| 95.214 | 94.900 | 87.220 | [GDrive](https://drive.google.com/file/d/1km-cVFvUAPU1UumLLi1fIRasdg6VA-vM/view?usp=sharing) |
| CISIA_pfc | 37.107 | 38.934 | 53.823 | 48.674 | 19.927 | 99.367| 95.429 | 94.600 | 84.970 | [GDrive](https://drive.google.com/file/d/1z8linstTZopL5Yy7NOUgVVtgzGtsu1LM/view?usp=sharing) |
| VGG2 | 38.578 | 35.259 | 54.304 | 44.081 | 24.095 | 99.550| 97.410 | 95.080 | 91.220 | [GDrive](https://drive.google.com/file/d/1UwyVIDSNDkHKClBANrWi8qpMU4nXizT6/view?usp=sharing) |
| VGG2_pfc | 40.673 | 36.767 | 60.180 | 49.039 | 24.255 | 99.683| 98.529 | 95.400 | 92.490 | [GDrive](https://drive.google.com/file/d/1uW0EsctVyPklSyXMXF39AniIhSRXCRtp/view?usp=sharing) |
| GlintAsia | 62.663 | 49.531 | 64.829 | 57.984 | 61.743 | 99.583| 93.186 | 95.400 | 91.500 | [GDrive](https://drive.google.com/file/d/1IyXh7m1HMwTZw4B5N1WMPIsN-S9kdS95/view?usp=sharing) |
| GlintAsia_pfc | 63.149 | 50.366 | 65.227 | 57.936 | 61.820 | 99.650| 93.029 | 95.233 | 91.140 | [GDrive](https://drive.google.com/file/d/1CTjalggNucgPkmpFi5ij-NGG1Fy9sL5r/view?usp=sharing) |
| MS1MV2 | 77.696 | 74.596 | 84.126 | 82.041 | 51.105 | 99.833| 98.083 | 98.083 | 96.140 | [GDrive](https://drive.google.com/file/d/1rd4kbiXtXBTWE8nP7p4OTv_CAp2FUa1i/view?usp=sharing) |
| MS1MV2_pfc | 77.738 | 74.728 | 84.883 | 82.798 | 52.507 | 99.783| 98.071 | 98.017 | 96.080 | [GDrive](https://drive.google.com/file/d/1ryrXenGQa-EGyk64mVaG136ihNUBmNMW/view?usp=sharing) |
| MS1M_MegaFace | 78.372 | 74.138 | 82.251 | 77.223 | 60.203 | 99.750| 97.557 | 97.400 | 95.350 | [GDrive](https://drive.google.com/file/d/1c2JG0StcTMDrL4ywz3qWTN_9io3lo_ER/view?usp=sharing) |
| MS1M_MegaFace_pfc | 78.773 | 73.690 | 82.947 | 78.793 | 57.566 | 99.800| 97.870 | 97.733 | 95.400 | [GDrive](https://drive.google.com/file/d/1BnG48LS_HIvYlSbSnP_LzpO3xjx0_rpu/view?usp=sharing) |
| MS1MV3 | 82.522 | 77.172 | 87.028 | 86.006 | 60.625 | 99.800| 98.529 | 98.267 | 96.580 | [GDrive](https://drive.google.com/file/d/1Tqorubgcl0qfjbjEM_Y9EDmjG5tCWzbr/view?usp=sharing) |
| MS1MV3_pfc | 81.683 | 78.126 | 87.286 | 85.542 | 58.925 | 99.800| 98.443 | 98.167 | 96.430 | [GDrive](https://drive.google.com/file/d/15jrHCqhEmoSZ93kKL9orVMhbKfNWAhp-/view?usp=sharing) |
| Glint360k | 86.789 | 84.749 | 91.414 | 90.088 | 66.168 | 99.817| 99.143 | 98.450 | 97.130 | [GDrive](https://drive.google.com/file/d/1gnt6P3jaiwfevV4hreWHPu0Mive5VRyP/view?usp=sharing) |
| Glint360k_pfc | 87.077 | 85.272 | 91.616 | 90.541 | 66.813 | 99.817| 99.143 | 98.450 | 97.020 | [GDrive](https://drive.google.com/file/d/164o2Ct42tyJdQjckeMJH2-7KTXolu-EP/view?usp=sharing) |
| WebFace600K | 90.566 | 89.355 | 94.177 | 92.358 | 73.852 | 99.800| 99.200 | 98.100 | 97.120 | [GDrive](https://drive.google.com/file/d/1N0GL-8ehw_bz2eZQWz2b0A5XBdXdxZhg/view?usp=sharing) |
| WebFace600K_pfc | 89.951 | 89.301 | 94.016 | 92.381 | 73.007 | 99.817| 99.143 | 98.117 | 97.010 | [GDrive](https://drive.google.com/file/d/11TASXssTnwLY1ZqKlRjsJiV-1nWu9pDY/view?usp=sharing) |
| Average | 69.247 | 65.908 | 77.121 | 72.819 | 52.014 | 99.706| 97.374 | 96.962 | 93.925 | |
| Average_pfc | 69.519 | 65.898 | 77.497 | 73.213 | 51.853 | 99.715| 97.457 | 96.965 | 93.818 | |
### List of models by MobileFaceNet and different training datasets:
**``FLOPS``:** 450M FLOPs
**``Model-Size``:** 13MB
| Dataset | MR-ALL | African | Caucasian | South Asian | East Asian | LFW | CFP-FP | AgeDB-30 | IJB-C(E4) | Link(onnx) |
| :-------- | ------ | ------- | ---- | ------ | -------- | ----- | ------ | -------- | --------- | --- |
| WebFace600K | 71.865 | 69.449 | 80.454 | 73.394 | 51.026 | 99.70 | 98.00 | 96.58 | 95.02 | - |
## 2. Face Detection models.
### 2.1 RetinaFace
In RetinaFace, mAP was evaluated with multi-scale testing.
``m025``: means MobileNet-0.25
| Impelmentation | Easy-Set | Medium-Set | Hard-Set | Link |
| ------------------------ | -------- | ---------- | -------- | ------------------------------------------------------------ |
| RetinaFace-R50 | 96.5 | 95.6 | 90.4 | [BDrive](https://pan.baidu.com/s/1C6nKq122gJxRhb37vK0_LQ), [GDrive](https://drive.google.com/file/d/1wm-6K688HQEx_H90UdAIuKv-NAsKBu85/view?usp=sharing) |
| RetinaFace-m025(yangfly) | - | - | 82.5 | [BDrive](https://pan.baidu.com/s/1P1ypO7VYUbNAezdvLm2m9w)(nzof), [GDrive](https://drive.google.com/drive/folders/1OTXuAUdkLVaf78iz63D1uqGLZi4LbPeL?usp=sharing) |
| BlazeFace-FPN-SSH (paddle) | 91.9 | 89.8 | 81.7% | [pretrained model](https://paddledet.bj.bcebos.com/models/blazeface_fpn_ssh_1000e.pdparams), [inference model](https://paddle-model-ecology.bj.bcebos.com/model/insight-face/blazeface_fpn_ssh_1000e_v1.0_infer.tar) |
### 2.2 SCRFD
In SCRFD, mAP was evaluated with single scale testing, VGA resolution.
``2.5G``: means the model cost ``2.5G`` FLOPs while the input image is in VGA(640x480) resolution.
``_KPS``: means this model can detect five facial keypoints.
| Name | Easy | Medium | Hard | FLOPs | Params(M) | Infer(ms) | Link(pth) |
| :------------: | ----- | ------ | ----- | ----- | --------- | --------- | ------------------------------------------------------------ |
| SCRFD_500M | 90.57 | 88.12 | 68.51 | 500M | 0.57 | 3.6 | [GDrive](https://drive.google.com/file/d/1OX0i_vWDp1Fp-ZynOUMZo-q1vB5g1pTN/view?usp=sharing) |
| SCRFD_1G | 92.38 | 90.57 | 74.80 | 1G | 0.64 | 4.1 | [GDrive](https://drive.google.com/file/d/1acd5wKjWnl1zMgS5YJBtCh13aWtw9dej/view?usp=sharing) |
| SCRFD_2.5G | 93.78 | 92.16 | 77.87 | 2.5G | 0.67 | 4.2 | [GDrive](https://drive.google.com/file/d/1wgg8GY2vyP3uUTaAKT0_MSpAPIhmDsCQ/view?usp=sharing) |
| SCRFD_10G | 95.16 | 93.87 | 83.05 | 10G | 3.86 | 4.9 | [GDrive](https://drive.google.com/file/d/1kUYa0s1XxLW37ZFRGeIfKNr9L_4ScpOg/view?usp=sharing) |
| SCRFD_34G | 96.06 | 94.92 | 85.29 | 34G | 9.80 | 11.7 | [GDrive](https://drive.google.com/file/d/1w9QOPilC9EhU0JgiVJoX0PLvfNSlm1XE/view?usp=sharing) |
| SCRFD_500M_KPS | 90.97 | 88.44 | 69.49 | 500M | 0.57 | 3.6 | [GDrive](https://drive.google.com/file/d/1TXvKmfLTTxtk7tMd2fEf-iWtAljlWDud/view?usp=sharing) |
| SCRFD_2.5G_KPS | 93.80 | 92.02 | 77.13 | 2.5G | 0.82 | 4.3 | [GDrive](https://drive.google.com/file/d/1KtOB9TocdPG9sk_S_-1QVG21y7OoLIIf/view?usp=sharing) |
| SCRFD_10G_KPS | 95.40 | 94.01 | 82.80 | 10G | 4.23 | 5.0 | [GDrive](https://drive.google.com/file/d/1-2uy0tgkenw6ZLxfKV1qVhmkb5Ep_5yx/view?usp=sharing) |
## 3. Face Alignment models.
### 2.1 2D Face Alignment
| Impelmentation | Points | Backbone | Params(M) | Link(onnx) |
| --------------------- | ------ | ------------- | --------- | ------------------------------------------------------------ |
| Coordinate-regression | 106 | MobileNet-0.5 | 1.2 | [GDrive](https://drive.google.com/file/d/1M5685m-bKnMCt0u2myJoEK5gUY3TDt_1/view?usp=sharing) |
### 2.2 3D Face Alignment
| Impelmentation | Points | Backbone | Params(M) | Link(onnx) |
| -------------- | ------ | --------- | --------- | ------------------------------------------------------------ |
| - | 68 | ResNet-50 | 34.2 | [GDrive](https://drive.google.com/file/d/1aJe5Rzoqrtf_a9U84E-V1b0rUi8-QbCI/view?usp=sharing) |
### 2.3 Dense Face Alignment
## 4. Face Attribute models.
### 4.1 Gender&Age
| Training-Set | Backbone | Params(M) | Link(onnx) |
| ------------ | -------------- | --------- | ------------------------------------------------------------ |
| CelebA | MobileNet-0.25 | 0.3 | [GDrive](https://drive.google.com/file/d/1Mm3TeUuaZOwmEMp0nGOddvgXCjpRodPU/view?usp=sharing) |
### 4.2 Expression
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