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MFR Ongoing

This is the ongoing version of ICCV-2021 Masked Face Recognition Challenge & Workshop(MFR). We also extend it to involve some public available and popular benchmarks such as IJBC, LFW, CFPFP and AgeDB.

(:bulb: :bulb: Once you find the name IFRT which is InsightFace Recognition Test in short anywhere, it is the same as MFR-Ongoing.)

For detail, please check our ICCV 2021 workshop paper.

More information about the workshop challenge can be found here, for reference.

MFR testset consists of non-celebrities so we can ensure that it has very few overlap with public available face recognition training set, such as MS1M and CASIA as they mostly collected from online celebrities. As the result, we can evaluate the FAIR performance for different algorithms.

In recent changes, we also add public available popular benchmarks such as IJBC, LFW, CFPFP, AgeDB into MFR-Ongoing.

Current submission server link: http://iccv21-mfr.com/

For any question, please send email to insightface.challenge AT gmail.com

Testsets

In MFR-Ongoing, we will evaluate the accuracy of following testsets:

  • Accuracy between masked and non-masked faces.
  • Accuracy among children(2~16 years old).
  • Accuracy of globalised multi-racial benchmarks.

We ensure that there's no overlap between the above testsets and public available training datasets, as they are not collected from online celebrities.

We also evaluate below public available popular benchmarks:

  • IJBC under FAR<=e-5 and FAR<=e-4.
  • Some 1:1 verification testsets, such as LFW, CFPFP, AgeDB-30.

Mask test-set:

Mask testset contains 6,964 identities, 6,964 masked images and 13,928 non-masked images. There are totally 13,928 positive pairs and 96,983,824 negative pairs.

Click to check the sample images(here we manually blur it to protect privacy) ifrtsample

Children test-set:

Children testset contains 14,344 identities and 157,280 images. There are totally 1,773,428 positive pairs and 24,735,067,692 negative pairs.

Click to check the sample images(here we manually blur it to protect privacy) ifrtsample

Multi-racial test-set (MR in short):

The globalised multi-racial testset contains 242,143 identities and 1,624,305 images.

Race-Set Identities Images Positive Pairs Negative Pairs
African 43,874 298,010 870,091 88,808,791,999
Caucasian 103,293 697,245 2,024,609 486,147,868,171
Indian 35,086 237,080 688,259 56,206,001,061
Asian 59,890 391,970 1,106,078 153,638,982,852
ALL 242,143 1,624,305 4,689,037 2,638,360,419,683
Click to check the sample images(here we manually blur it to protect privacy) ifrtsample

Evaluation Metric

For Mask set, TAR is measured on mask-to-nonmask 1:1 protocal, with FAR less than 0.0001(e-4).

For Children set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.0001(e-4).

For multi-racial sets, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6).

For IJBC and verification test-set, we use the most common test protocal.

Participants are ordered in terms of highest scores across two datasets: TAR@Mask and TAR@MR-All, by the formula of 0.25 * TAR@Mask + 0.75 * TAR@MR-All.

Baselines

2021.04.25 We made a clean on East Asian subset, by removing children images.

2021.04.27 Add onnx download links.

Backbone Dataset Method Mask Children African Caucasian South Asian East Asian All size(mb) infer(ms) link
R100 Casia ArcFace 26.623 30.359 39.666 53.933 47.807 21.572 42.735 248.904 7.073 download
R100 MS1MV2 ArcFace 65.767 60.496 79.117 87.176 85.501 55.807 80.725 248.904 7.028 download
R18 MS1MV3 ArcFace 47.853 41.047 62.613 75.125 70.213 43.859 68.326 91.658 1.856 download
R34 MS1MV3 ArcFace 58.723 55.834 71.644 83.291 80.084 53.712 77.365 130.245 3.054 download
R50 MS1MV3 ArcFace 63.850 60.457 75.488 86.115 84.305 57.352 80.533 166.305 4.262 download
R100 MS1MV3 ArcFace 69.091 66.864 81.083 89.040 88.082 62.193 84.312 248.590 7.031 download
R18 Glint360K ArcFace 53.317 48.113 68.230 80.575 75.852 47.831 72.074 91.658 2.013 download
R34 Glint360K ArcFace 65.106 65.454 79.907 88.620 86.815 60.604 83.015 130.245 3.044 download
R50 Glint360K ArcFace 70.233 69.952 85.272 91.617 90.541 66.813 87.077 166.305 4.340 download
R100 Glint360K ArcFace 75.567 75.202 89.488 94.285 93.434 72.528 90.659 248.590 7.038 download
- Private
insightface-000 of frvt
97.760 93.358 98.850 99.372 99.058 87.694 97.481 - - -

(MS1M-V2 means MS1M-ArcFace, MS1M-V3 means MS1M-RetinaFace).

Inference time in above table was evaluated on Tesla V100 GPU, using onnxruntime-gpu==1.6.

Rules

  1. We have two tracks, academic and unconstrained.
  2. Please DO NOT register the account with messy or random characters(for both username and organization).
  3. For academic submissions, we recommend to set the username as the name of your proposed paper or method. Orgnization hiding is not allowed(or the score will be banned) for this track but you can set the submission as private. You can also create multiple accounts, one account for one method.
  4. Right now we only support 112x112 input, so make sure that the submission model accepts the correct input shape(['*',3,112,112]), in RGB order. Add an interpolate operator into the first layer of the submission model if you need a different input resolution.
  5. Participants submit onnx model, then get scores by our online evaluation.
  6. Matching score is measured by cosine similarity.
  7. Online evaluation server uses onnxruntime-gpu==1.8, cuda==11.1, cudnn==8.0.5, GPU is RTX3090.
  8. Any float-16 model weights is prohibited, as it will lead to incorrect model size estimiation.
  9. Please use onnx_helper.py to check whether the model is valid.
  10. Leaderboard is now ordered in terms of highest scores across two datasets: TAR@Mask and TAR@MR-All, by the formula of 0.25 * TAR@Mask + 0.75 * TAR@MR-All.

Submission Guide

  1. Participants must package the onnx model for submission using zip xxx.zip model.onnx.
  2. Each participant can submit three times a day at most.
  3. Please sign-up with the real organization name. You can hide the organization name in our system if you like(not allowed for academic track).
  4. You can decide which submission to be displayed on the leaderboard by clicking 'Set Public' button.
  5. Please click 'sign-in' on submission server if find you're not logged in.