Implementation of "Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition" (CDP)
Code is in continuous updating, please pull before execution.
Xiaohang Zhan, Ziwei Liu, Junjie Yan, Dahua Lin, Chen Change Loy, "Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition", ECCV 2018
Project Page: link
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Please use python3, as we cannot guarantee its compatibility with python2.
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The version of PyTorch we use is 0.3.1.
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Other depencencies:
pip install nmslib
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Clone the repo.
git clone [email protected]:XiaohangZhan/cdp.git cd cdp
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Download the data here to the repo root, and uncompress it.
tar -xf data.tar.gz
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Make sure the structure looks like the following:
cdp/data/ cdp/data/labeled/emore_l200k/ cdp/data/unlabeled/emore_u200k/ # ... other directories and files ...
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Run CDP
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Single model case:
python -u main.py --config experiments/emore_u200k_single/config.yaml
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Multi-model voting case (committee size: 4):
python -u main.py --config experiments/emore_u200k_cmt4/config.yaml
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Multi-model mediator case (committee size: 4):
# edit `experiments/emore_u200k_cmt4/config.yaml` as following: # strategy: mediator python -u main.py --config experiments/emore_u200k_cmt4/config.yaml
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Collect the results
Take
Multi-model mediator case
for example, the results are stored inexperiments/emore_u200k_cmt4/output/k15_mediator_111_th0.9915/sz600_step0.05/meta.txt
. The order is the same as that indata/unlabeled/emore_u200k/list.txt
. The samples labeled as-1
are discarded by CDP. You may assign them with new unique labels if you must use them.
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Create your data directory, e.g.
mydata
mkdir data/unlabeled/mydata
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Prepare your data list as
list.txt
and copy it to the directory. -
(optional) If you want to evaluate the performance on your data, prepare the meta file as
meta.txt
and copy it to the directory. -
Prepare your feature files. Extract face features corresponding to the
list.txt
with your trained face models, and save it as binary files viafeature.tofile("xxx.bin")
in numpy. The features should satisfyCosine Similarity
condition. Finally link/copy them todata/unlabeled/mydata/features/
. We recommand renaming the feature files using model names, e.g.,resnet18.bin
. CDP works for single model case, but we recommend you to use multiple models (i.e., preparing multiple feature files extracted from different models) withmediator
for better results. -
The structure should look like:
cdp/data/unlabeled/mydata/ cdp/data/unlabeled/mydata/list.txt cdp/data/unlabeled/mydata/meta.txt (optional) cdp/data/unlabeled/mydata/features/ cdp/data/unlabeled/mydata/features/*.bin
(You do not need to prepare knn files.)
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Prepare the config file. Please refer to the examples in
experiments/
mkdir experiments/myexp cp experiments/emore_u200k_cmt4/config.yaml experiments/myexp/ # edit experiments/myexp/config.yaml to fit your case. # you may need to change `base`, `committee`, `data_name`, etc.
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Tips for paramters adjusting
- Modify
threshold
to obtain roughly closedprecision
andrecall
to achieve higherfscore
. - Higher threshold results in higher precision and lower recall.
- Larger
max_sz
results in lower precision and higher recall.
- Modify
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We also implement several baseline clustering methods including: KMeans, MiniBatch-KMeans, Spectral, Hierarchical Agglomerative Clustering (HAC), FastHAC, DBSCAN, HDBSCAN, KNN DBSCAN, Approximate Rank-Order.
sh run_baselines.sh # results stored in `baseline_output/`
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Data
- emore_u200k (images: 200K, identities: 2,577)
- emore_u600k (images: 600K, identities: 8,436)
- emore_u1.4m (images: 1.4M, identities: 21,433) (These datasets are not the one in the paper which cannot be released, but the relative results are similar.)
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Baselines
- emore_u200k
method #clusters prec, recall, fscore total time * kmeans (ncluster=2577) 2577 94.24, 74.89, 83.45 618.1s * MiniBatchKMeans (ncluster=2577) 2577 89.98, 87.86, 88.91 122.8s * Spectral (ncluster=2577) 2577 97.42, 97.05, 97.24 12.1h * HAC (ncluster=2577, knn=30) 2577 97.74, 88.02, 92.62 5.65h FastHAC (distance=0.7, method=single) 46767 99.79, 53.18, 69.38 1.66h DBSCAN (eps=0.75, nim_samples=10) 52813 99.52, 65.52, 79.02 6.87h HDBSCAN (min_samples=10) 31354 99.35, 75.99, 86.11 4.87h KNN DBSCAN (knn=80, min_samples=10) 39266 97.54, 74.42, 84.43 60.5s ApproxRankOrder (knn=20, th=10) 85150 52.96, 16.93, 25.66 86.4s - emore_u600k
method #clusters prec, recall, fscore total time * kmeans (ncluster=8436) 8436 fail (out of memory) - * MiniBatchKMeans (ncluster=8436) 8436 * Spectral (ncluster=8436) 8436 * HAC (ncluster=8436, knn=30) 8436 95.39, 86.28, 90.6 60.9h FastHAC (distance=0.7, method=single) 94949 98.75, 68.49, 80.88 16.3h DBSCAN (eps=0.75, nim_samples=10) HDBSCAN (min_samples=10) 124279 99.01, 69.31, 81.54 47.9h KNN DBSCAN (knn=80, min_samples=10) 133061 96.6, 70.97, 81.82 644.5s ApproxRankOrder (knn=30, th=10) 304022 65.56, 8.139, 14.48 626.9s Note: Methods marked * are reported with their theoretical upper bound results, since they need number of clusters as input. We use the values from the ground truth to obtain the results. For each method, we adjust the parameters to achieve the best performance.
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CDP
- emore_u200k
strategy #model setting prec, recall, fscore knn time cluster time total time vote 1 k15_accept0_th0.66 89.35, 88.98, 89.16 14.8s 7.7s 22.5s vote 5 k15_accept4_th0.605 93.36, 92.91, 93.13 78.7s 6.0s 84.7s mediator 5 k15_110_th0.9938 94.06, 92.45, 93.25 78.7s 77.7s 156.4s mediator 5 k15_111_th0.9925 96.66, 94.93, 95.79 78.7s 100.2s 178.9s - emore_u600k
strategy #model setting prec, recall, fscore knn time cluster time total time vote 1 k15_accept0_th0.665 88.19, 85.33, 86.74 60.8s 24s 84.8s vote 5 k15_accept4_th0.605 90.21, 89.9, 90.05 309.4s 18.3s 327.7s mediator 5 k15_110_th0.985 90.43, 89.13, 89.78 309.4s 184.2s 493.6s mediator 5 k15_111_th0.982 96.55, 91.98, 94.21 309.4s 246.3s 555.7s - emore_u1.6m
strategy #model setting prec, recall, fscore knn time cluster time total time vote 1 k15_accept0_th0.68 89.49, 81.25, 85.17 187.5s 47.7s 235.2s vote 5 k15_accept4_th0.62 90.63, 87.32, 88.95 967.0s 44.3s 1011.3s mediator 5 k15_110_th0.99 93.67, 84.43, 88.81 967.0s 406.9s 1373.9s mediator 5 k15_111_th0.982 95.29, 90.97, 93.08 967.0s 584.7s 1551.7s Note:
- For mediator,
110
means usingrelationship
andaffinity
;111
means usingrelationship
,affinity
andstructure
.
@inproceedings{zhan2018consensus,
title={Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition},
author={Zhan, Xiaohang and Liu, Ziwei and Yan, Junjie and Lin, Dahua and Change Loy, Chen},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
pages={568--583},
year={2018}
}