MNIST数据集卷积神经网络实现手写数字识别（GUI）

# Learning to Cluster Faces by Infomap ## Intorduction 采用了无监督方法infomap进行人脸聚类，在公开数据集上MS-Celeb-1M、YouTube-Faces、DeepFashion获得较当前主流方法(如GCN人脸聚类等监督方法)同等或更优的效果. 通过faiss加速邻接边的构建，提高聚类速度，单批百万数据聚类仅需几分钟. 效果及性能测试详见下表. ## About GCN Method 1. L-GCN: [Linkage-based Face Clustering via Graph Convolution Network](https://arxiv.org/abs/1903.11306), CVPR 2019 2. GCN-D: [Learning to Cluster Faces on an Affinity Graph](https://arxiv.org/abs/1904.02749), CVPR 2019 (**Oral**) 3. GCN-V+GCN-E: [Learning to Cluster Faces via Confidence and Connectivity Estimation](https://arxiv.org/abs/2004.00445), CVPR 2020 4. GCN+LSTM: [Density-Aware Feature Embedding for Face Clustering](https://openaccess.thecvf.com/content_CVPR_2020/papers/Guo_Density-Aware_Feature_Embedding_for_Face_Clustering_CVPR_2020_paper.pdf), CVPR 2020 5. STAR-FC: [Structure-Aware Face Clustering on a Large-Scale Graph with 107 Nodes ](https://arxiv.org/pdf/2103.13225.pdf), CVPR 2021 [code](https://github.com/sstzal/STAR-FC) ## Requirements * Python >= 3.6 * sklearn * infomap * numpy * faiss-gpu(or faiss-cpu) ## Datasets MS-Celeb-1M : part1_test (584K)、YouTube-Faces、DeepFashion ## Run ```bash python face-cluster-by-infomap ``` ## Results on part1_test (584K) | Method | Precision | Recall | F-score | | ------ |:---------:|:------:|:-------:| | Chinese Whispers (k=80, th=0.6, iters=20) | 55.49 | 52.46 | 53.93 | | Approx Rank Order (k=80, th=0) | 99.77 | 7.2 | 13.42 | | MiniBatchKmeans (ncluster=5000, bs=100) | 45.48 | 80.98 | 58.25 | | KNN DBSCAN (k=80, th=0.7, eps=0.25, min=1) | 95.25 | 52.79 | 67.93 | | FastHAC (dist=0.72, single) | 92.07 | 57.28 | 70.63 | | GCN-D (2 prpsls) | 95.41 | 67.77 | 79.25 | | GCN-D (5 prpsls) | 94.62 | 72.59 | 82.15 | | GCN-D (8 prpsls) | 94.23 | 79.69 | 86.35 | | GCN-D (20 prplss) | 94.54 | 81.62 | 87.61 | | GCN-D + GCN-S (2 prpsls) | 99.07 | 67.22 | 80.1 | | GCN-D + GCN-S (5 prpsls) | 98.84 | 72.01 | 83.31 | | GCN-D + GCN-S (8 prpsls) | 97.93 | 78.98 | 87.44 | | GCN-D + GCN-S (20 prpsls) | 97.91 | 80.86 | 88.57 | | GCN-V | 92.45 | 82.42 | 87.14 | | GCN-V + GCN-E | 92.56 | 83.74 | 87.93 | | Infomap(ours)(k=50,min_sim=0.58) | 95.50 | 92.51 | 93.98 |  ## Results on YouTube-Faces | Method | Pairwise F-score | BCubed F-score | NMI | | ------ |:---------:|:------:|:-------:| | Chinese Whispers (k=160, th=0.75, iters=20) | 72.9 | 70.55 | 93.25 | | Approx Rank Order (k=200, th=0) | 76.45 | 75.45 | 94.34 | | Kmeans (ncluster=1436) | 67.86 | 75.77 | 93.99 | | KNN DBSCAN (k=160, th=0., eps=0.3, min=1) | 91.35 | 89.34 | 97.52 | | FastHAC (dist=0.72, single) | 93.07 | 87.98 | 97.19 | | GCN-D (4 prpsls) | 94.44 | 91.33 | 97.97 | | Infomap(ours)(k=400,min_sim=0.56) | 92.82 | 91.78 | 98.04 | ## Results on DeepFashion | Method | Pairwise F-score | BCubed F-score | NMI | | ------ |:---------:|:------:|:-------:| | Chinese Whispers (k=5, th=0.7, iters=20) | 31.22 | 53.25 | 89.8 | | Approx Rank Order (k=10, th=0) | 25.04 | 52.77 | 88.71 | | Kmeans (ncluster=3991) | 32.02 | 53.3 | 88.91 | | KNN DBSCAN (k=4, th=0., eps=0.1, min=2) | 25.07 | 53.23 | 90.75 | | FastHAC (dist=0.4, single) | 22.54 | 48.77 | 90.44 | | Meanshift (bandwidth=0.5) | 31.61 | 56.73 | 89.29 | | Spectral (ncluster=3991, affinity='rbf') | 29.6 | 47.12 | 86.95 | | DaskSpectral (ncluster=3991, affinity='rbf') | 24.25 | 44.11 | 86.21 | | CDP (single model, k=2, th=0.5, maxsz=200) | 28.28 | 57.83 | 90.93 | | L-GCN (k_at_hop=[5, 5], active_conn=5, step=0.5, maxsz=50) | 30.7 | 60.13 | 90.67 | | GCN-D (2 prpsls) | 29.14 | 59.09 | 89.48 | | GCN-D (8 prpsls) | 32.52 | 57.52 | 89.54 | | GCN-D (20 prpsls) | 33.25 | 56.83 | 89.36 | | GCN-V | 33.59 | 59.41 | 90.88 | | GCN-V + GCN-E | 38.47 | 60.06 | 90.5 | | Infomap(ours)(k=400,min_sim=0.88) | 38.67 | 60.48 | 90.97 | ## Time Consumes and Gpu Memory (k=50,min_sim=0.58) | Nodes | Edges | TimeCount | Gpu Memory | | ------ |:---------:|:---------:|:---------:| | 500000 | 16535263 | 160(s) | 2745(MiB) | | 1000000 | 30206572 | 400(s) | 3235(MiB) | ## Comments * k值越大，TimeCount会变长，Gpu Memory会增加 * k值并非infomap的参数，仅为faiss构建knn构建时使用 * 数据量增加一倍，KNN构建时间为原来的4倍(实质为n*n的向量搜索) ## References * [最小熵原理（五）：“层层递进”之社区发现与聚类](https://spaces.ac.cn/archives/7006) * [人脸聚类主流方案]