MFR Ongoing

💡 Due to GPU resource constraints, we are temporarily taking this service offline. If any companies or individuals are interested in hosting this service, please contact me via email: guojia AT insightface.ai

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.

<details> <summary>Click to check the sample images(here we manually blur it to protect privacy) </summary> </details>

`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.

<details> <summary>Click to check the sample images(here we manually blur it to protect privacy) </summary> </details>

`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

<details> <summary>Click to check the sample images(here we manually blur it to protect privacy) </summary> </details>

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	<div style="width: 50pt">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

We have two tracks, academic and unconstrained.
Please DO NOT register the account with messy or random characters(for both username and organization).
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.
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.
Participants submit onnx model, then get scores by our online evaluation.
Matching score is measured by cosine similarity.
Online evaluation server uses onnxruntime-gpu==1.8, cuda==11.1, cudnn==8.0.5, GPU is RTX3090.
Any float-16 model weights is prohibited, as it will lead to incorrect model size estimiation.
Please use onnx_helper.py to check whether the model is valid.
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

Participants must package the onnx model for submission using zip xxx.zip model.onnx.
Each participant can submit three times a day at most.
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).
You can decide which submission to be displayed on the leaderboard by clicking 'Set Public' button.
Please click 'sign-in' on submission server if find you're not logged in.