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iccv19-challenge/README.md
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[The Lightweight Face Recognition Challenge & Workshop](https://ibug.doc.ic.ac.uk/resources/lightweight-face-recognition-challenge-workshop/) will be held in conjunction with the International Conference on Computer Vision (ICCV) 2019, Seoul Korea.
Please read carefully and strictly follow the rules. For example, you should use the same [method](https://github.com/deepinsight/insightface/blob/master/common/flops_counter.py) with us for calculating FLOPs.
Please strictly follow the rules. For example, please use the same [method](https://github.com/deepinsight/insightface/blob/master/common/flops_counter.py) for the FLOPs calculation.
[Test Submission Server](http://www.insightface-challenge.com/overview)
[Test Server](http://www.insightface-challenge.com/overview)
**NEWS**
``2019.05.21`` Baseline models and training logs available.
``2019.05.16`` The four sections(glint-large, glint-light, iqiyi-large, iqiyi-light) will share the price pool for 1/4 each respectively. From each section, the top 3 players share the section price pool for 50%, 30% and 20% respectively.
``2019.05.11`` We updated the groundtruth of iQIYI video testset to v0.2. Please re-summit the feature set for iQIYI sections.
``2019.05.16`` The four tracks (deepglint-light, deepglint-large, iQIYI-light, iQIYI-large) will equally share the dynamic funding pool (14000$). From each track, the top 3 players will share the funding pool for 50%, 30% and 20% respectively.
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1. Download ms1m-retinaface from [baiducloud](https://pan.baidu.com/s/1rQxJ3drqm_071vpxBtp98A) or [dropbox](https://www.dropbox.com/s/ev5ezzcz79p2hge/ms1m-retinaface-t1.zip?dl=0) and unzip it to `$INSIGHTFACE_ROOT/datasets/`
2. Go into `$INSIGHTFACE_ROOT/recognition/`
3. Refer to the `retina` dataset config section in `sample_config.py` and copy it to your own`config.py`.
3. Refer to the `retina` dataset configuration section in `sample_config.py` and copy it as your own configuration file `config.py`.
4. Start training with `CUDA_VISIBLE_DEVICES='0,1,2,3' python -u train.py --dataset retina --network [your-network] --loss arcface`. It will output the accuracy of lfw, cfp_fp and agedb_30 every 2000 batches by default.
5. It is better to put the training dataset on SSD hard disk, to obtain good training performance.
5. Putting the training dataset on SSD hard disk will achieve better training efficiency.
------------------
**Testing:**
1. testdata-image from [baiducloud](https://pan.baidu.com/s/1UKUYsRfVTSzj1tfU3BVFrw) or [dropbox](https://www.dropbox.com/s/r5y6xt754m36rh8/iccv19-challenge-data-v1.zip?dl=0). These face images are all pre-processed and aligned so no need to do further modification.
2. To download testdata-video from iQIYI, please visit <http://challenge.ai.iqiyi.com/data-cluster>. You must download iQIYI-VID-FACE.z01, iQIYI-VID-FACE.z02 and iQIYI-VID-FACE.zip after signin. These face images are all pre-processed and aligned so no need to do further modification.
1. To unzip: ``zip iQIYI_VID_FACE.zip -s=0 --out iQIYI_VID_FACE_ALL.zip; unzip iQIYI_VID_FACE_ALL.zip``
2. We can get a directory named ``iQIYI_VID_FACE`` after decompression. Then we have to move ``video_filelist.txt`` in testdata-image package to ``iQIYI_VID_FACE/filelist.txt``, to indicate the order of videos in our submission feature file.
1. Download testdata-image from [baiducloud](https://pan.baidu.com/s/1UKUYsRfVTSzj1tfU3BVFrw) or [dropbox](https://www.dropbox.com/s/r5y6xt754m36rh8/iccv19-challenge-data-v1.zip?dl=0). These face images are all pre-processed and aligned.
2. To download testdata-video from iQIYI, please visit <http://challenge.ai.iqiyi.com/data-cluster>. You need to download iQIYI-VID-FACE.z01, iQIYI-VID-FACE.z02 and iQIYI-VID-FACE.zip after registration. These face frames are also pre-processed and aligned.
1. Unzip: ``zip iQIYI_VID_FACE.zip -s=0 --out iQIYI_VID_FACE_ALL.zip; unzip iQIYI_VID_FACE_ALL.zip``
2. We can get a directory named ``iQIYI_VID_FACE`` after decompression. Then, we have to move ``video_filelist.txt`` in testdata-image package to ``iQIYI_VID_FACE/filelist.txt``, to indicate the order of videos in our submission feature file.
3. To generate image feature submission file: check ``gen_image_feature.py``
4. To generate video feature submission file: check ``gen_video_feature.py``
5. Submit binary feature to the right section on test server.
5. Submit binary feature to the right track of the test server.
You can also check the verification performance during training time on LFW,CFP_FP,AgeDB_30 datasets.
@@ -55,9 +51,9 @@ For image testset, we evaluate the TAR under FAR@e-8 while we choose the TAR und
1. Network y2(a deeper mobilefacenet): 933M FLOPs. TAR_image: 0.64691, TAR_video: 0.47191
2. Network r100fc(ResNet100FC-IR): 24G FLOPs. TAR_image: 0.80312, TAR_video: 0.64894
Baseline models download link: [baidu cloud](https://pan.baidu.com/s/1Em0ZFnefSoTsZoTd-9m8Nw)
Baseline models download link: [baidu cloud](https://pan.baidu.com/s/1Em0ZFnefSoTsZoTd-9m8Nw) [dropbox](https://www.dropbox.com/s/yqaziktiv38ehrv/iccv19-baseline-models.zip?dl=0)
Training logs: [baidu cloud](https://pan.baidu.com/s/12rsp-oMzsjTeU6nugEvA9g)
Training logs: [baidu cloud](https://pan.baidu.com/s/12rsp-oMzsjTeU6nugEvA9g) [dropbox](https://www.dropbox.com/s/4ufb9g7n76rfav5/iccv-baseline-log.zip?dl=0)
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@@ -65,14 +61,12 @@ Training logs: [baidu cloud](https://pan.baidu.com/s/12rsp-oMzsjTeU6nugEvA9g)
[https://github.com/deepinsight/insightface/issues/632](https://github.com/deepinsight/insightface/issues/632)
------------------
**Candidate solutions:**
1. Use slightly deeper or wider mobile-level networks.
2. Try different training methods/losses than straightforward arcface.
1. Manually design or automatically search different networks/losses.
2. Use slightly deeper or wider mobile-level networks.
3. [OctConv](https://arxiv.org/abs/1904.05049), to reduce FLOPs.
4. [HRNet](https://arxiv.org/abs/1904.04514), for large FLOPs track.
and so on