From 562dcefcbb2b9c00e652804fe0711537ce727afa Mon Sep 17 00:00:00 2001 From: JiankangDeng Date: Tue, 21 May 2019 21:12:48 +0100 Subject: [PATCH] Update README.md --- iccv19-challenge/README.md | 34 ++++++++++++++-------------------- 1 file changed, 14 insertions(+), 20 deletions(-) diff --git a/iccv19-challenge/README.md b/iccv19-challenge/README.md index 031e0e9..43159b7 100644 --- a/iccv19-challenge/README.md +++ b/iccv19-challenge/README.md @@ -1,18 +1,14 @@ [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$). From each track, the top 3 players will share the funding pool for 50%, 30% and 20% respectively. ================== @@ -22,21 +18,21 @@ Please read carefully and strictly follow the rules. For example, you should use 1. Download ms1m-retinaface from [baiducloud](https://pan.baidu.com/s/1rQxJ3drqm_071vpxBtp98A) or [dropbox](https://www.dropbox.com/s/ev5ezzcz79p2hge/ms1m-retinaface-t1.zip?dl=0) and unzip it to `$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 . 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 . 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) ------------------ @@ -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