Implementation of "Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition" (CDP)

Introduction

Original paper: 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: http://mmlab.ie.cuhk.edu.hk/projects/CDP/

You can use this code for:

1. State-of-the-art face clustering in linear complexity.
2. High efficiency generic clustering.
3. Plugging the pair-to-cluster module into your clustering algorithm.

Dependency

• Please use python3, as we cannot guarantee its compatibility with python2.

• The version of PyTorch we use is 0.3.1.

• Other depencencies:

  pip install nmslib

Usage

0. Clone the repo.

   git clone [email protected]:XiaohangZhan/cdp.git
   cd cdp

Using ready-made data for face clustering

1. Download the data from Google Drive or Baidu Yun with passwd u8vz , to the repo root, and uncompress it.

   tar -xf data.tar.gz

2. Make sure the structure looks like the following:

   cdp/data/
   cdp/data/labeled/emore_l200k/
   cdp/data/unlabeled/emore_u200k/
   # ... other directories and files ...

3. Run CDP

   • Single model case:

     python -u main.py --config experiments/emore_u200k_single/config.yaml

   • Multi-model voting case (committee size: 4):

     python -u main.py --config experiments/emore_u200k_cmt4/config.yaml

   • 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

4. Collect the results

   Take Multi-model mediator case for example, the results are stored in experiments/emore_u200k_cmt4/output/k15_mediator_111_th0.9915/sz600_step0.05/meta.txt. The order is the same as that in data/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.

Using your own data

1. Create your data directory, e.g. mydata

   mkdir data/unlabeled/mydata

2. Prepare your data list as list.txt and copy it to the directory. If the data is not along with a list file, just make a dummy one, and make sure the length of the list is equal to the number of examples.

3. (optional) If you want to evaluate the performance on your data, prepare the meta file as meta.txt and copy it to the directory.

4. Prepare your feature files. Extract face features corresponding to the list.txt with your trained face recognition models, and save it as binary files via feature.tofile("xxx.bin") in numpy. The features should satisfy Cosine Similarity condition. Finally link/copy them to data/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) with mediator for better results.

5. 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.)

6. 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.

7. If you want to use mediator mode, please also prepare the training set, i.e., the features extracted using the same face recognition model as step 4, as well as the meta file containing labels. Organize them in data/labeled/mydata/ similarly to data/labeled/emore_l200k/.

8. Tips for paramters adjusting

   • Modify threshold to obtain roughly balanced precision and recall to achieve higher fscore.
   • Higher threshold results in higher precision and lower recall.
   • Larger max_sz results in lower precision and higher recall.

Using single model API for generic clustering

• The example is equivalent to using experiments/emore_u200k_single/config.yaml. However, it is easier to use if you prefer single model version of CDP. With this API, you can perform generic clustering on your own data with plenty of metrics to choose.

  # an example
  python -u test_api.py

Using isoloated pair-to-cluster module

• This function converts pairs into clusters with extremely high efficiency.

  # pairs: numpy array (N,2) containing indices of pairs, N: number of pairs
  # scores: numpy array (N,) containing edge score of each pair
  # max_sz: maximal size of a cluster
  # step: the step to adjust threshold, default: 0.05
  from source import graph
  import numpy as np
  num = len(np.unique(pairs.flatten()))
  components = graph.graph_propagation(pairs, scores, max_sz, step)
  cluster = [[n.name for n in c] for c in components]
  assert sum([len(c) for c in cluster]) == num, "Fatal error: some samples missing, please report to the author: [email protected]"

Run Baselines

• 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/`

Evaluation Results

1. 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.)

2. 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	81.64, 86.58, 84.04	2265.6s
   * Spectral (ncluster=8436)	8436	fail (out of memory)	-
   * HAC (ncluster=8436, knn=30)	8436	95.39, 86.28, 90.60	60.9h
   FastHAC (distance=0.7, method=single)	94949	98.75, 68.49, 80.88	16.3h
   DBSCAN (eps=0.75, nim_samples=10)	174886	99.02, 61.95, 76.22	79.6h
   HDBSCAN (min_samples=10)	124279	99.01, 69.31, 81.54	47.9h
   KNN DBSCAN (knn=80, min_samples=10)	133061	96.60, 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.

3. CDP (in linear time !!!)

   • 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_u1.4m

   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 using relationship and affinity; 111 means using relationship, affinity and structure.

   • The results may not be exactly reproduced, because there is randomness in knn search by NMSLIB.

   • Experiments are performed on a server with 48 CPU cores, 8 TITAN XP, 252G memory.

Face recognition framework

You may use this framework to train/evaluate face recognition models and extract features.

url: https://github.com/XiaohangZhan/face_recognition_framework

Bibtex

@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 Loy, Chen Change},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={568--583},
  year={2018}
}