learn-to-cluster
Learning to Cluster Faces (CVPR 2019, CVPR 2020)
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Download Latest Version (.zip)- __init__.py
- aro.py
- chinese_whispers.py
- sklearn_cluster.py
- cfg_test_0.7_0.75.yaml
- cfg_test_hnsw_2_i0_18_i1.yaml
- cfg_test_hnsw_2_i0_6_i1.yaml
- cfg_train_0.7_0.75.yaml
- cfg_train_8_prpsl.yaml
- cfg_test_det_fashion_20_prpsls.py
- cfg_test_det_fashion_2_prpsls.py
- cfg_test_det_fashion_8_prpsls.py
- cfg_test_det_ms1m_20_prpsls.py
- cfg_test_det_ms1m_2_prpsls.py
- cfg_test_det_ms1m_5_prpsls.py
- cfg_test_det_ms1m_8_prpsls.py
- cfg_test_det_ytb_4_prpsls.py
- cfg_test_seg_ms1m_20_prpsls.py
- cfg_test_seg_ms1m_2_prpsls.py
- cfg_test_seg_ms1m_5_prpsls.py
- cfg_test_seg_ms1m_8_prpsls.py
- cfg_train_det_fashion_4_prpsls.py
- cfg_train_det_fashion_84_prpsls.py
- cfg_train_det_fashion_8_prpsls.py
- cfg_train_det_ms1m_4_prpsls.py
- cfg_train_det_ms1m_84_prpsls.py
- cfg_train_det_ms1m_8_prpsls.py
- cfg_train_seg_ms1m_4_prpsls.py
- cfg_train_seg_ms1m_84_prpsls.py
- cfg_train_seg_ms1m_8_prpsls.py
- __init__.py
- build_dataloader.py
- cluster_dataset.py
- cluster_det_processor.py
- cluster_processor.py
- cluster_seg_processor.py
- sampler.py
- __init__.py
- dsgcn.py
- __init__.py
- runner.py
- __init__.py
- main.py
- README.md
- test_cluster_det.py
- test_cluster_seg.py
- train.py
- train_cluster_det.py
- train_cluster_seg.py
- __init__.py
- evaluate.py
- metrics.py
- cfg_test_lgcn_fashion.py
- cfg_test_lgcn_ms1m.py
- cfg_train_lgcn_fashion.py
- cfg_train_lgcn_ms1m.py
- __init__.py
- build_dataloader.py
- cluster_dataset.py
- __init__.py
- lgcn.py
- __init__.py
- main.py
- online_evaluation.py
- README.md
- test_lgcn.py
- train_lgcn.py
- __init__.py
- deoverlap.py
- nms.py
- __init__.py
- generate_basic_proposals.py
- generate_iter_proposals.py
- generate_proposals.py
- graph.py
- metrics.py
- stat_cluster.py
- super_vertex.py
- aro.sh
- aro_fashion.sh
- aro_ytb.sh
- cw_fashion.sh
- cw_ms1m.sh
- cw_ytb.sh
- dbscan.sh
- dbscan_fashion.sh
- dbscan_ytb.sh
- fast_hac.sh
- fast_hac_fashion.sh
- fast_hac_ytb.sh
- kmeans_fashion.sh
- kmeans_ytb.sh
- meanshift_fashion.sh
- mini_kmeans.sh
- spectral.sh
- spectral_fashion.sh
- deoverlap.sh
- evaluate.sh
- gcn_d_upper_bound.sh
- generate_basic_proposals.sh
- generate_iter_proposals.sh
- pipeline.sh
- test_cluster_det_fashion.sh
- test_cluster_det_iop_ms1m.sh
- test_cluster_det_ms1m.sh
- test_cluster_det_ytb.sh
- test_cluster_seg_ms1m.sh
- train_cluster_det_fashion.sh
- train_cluster_det_iop_ms1m.sh
- train_cluster_det_ms1m.sh
- train_cluster_seg_ms1m.sh
- test_lgcn_fashion.sh
- test_lgcn_ms1m.sh
- train_lgcn_fashion.sh
- train_lgcn_ms1m.sh
- test_knn.sh
- test_gcn_e_fashion.sh
- test_gcn_e_ms1m.sh
- test_gcn_v_fashion.sh
- test_gcn_v_ms1m.sh
- train_gcn_e_fashion.sh
- train_gcn_e_ms1m.sh
- train_gcn_v_fashion.sh
- train_gcn_v_ms1m.sh
- analyze_proposals.py
- baseline_cluster.py
- download_data.py
- dsgcn_upper_bound.py
- test_knn.py
- __init__.py
- adjacency.py
- dataset.py
- dist.py
- draw.py
- faiss_gpu.py
- faiss_search.py
- knn.py
- logger.py
- misc.py
- misc_cluster.py
- cfg_test_gcne_fashion.py
- cfg_test_gcne_ms1m.py
- cfg_test_gcnv_fashion.py
- cfg_test_gcnv_ms1m.py
- cfg_train_gcne_fashion.py
- cfg_train_gcne_ms1m.py
- cfg_train_gcnv_fashion.py
- cfg_train_gcnv_ms1m.py
- __init__.py
- gcn_e_dataset.py
- gcn_v_dataset.py
- __init__.py
- gcn_e.py
- gcn_v.py
- utils.py
- __init__.py
- runner.py
- __init__.py
- confidence.py
- deduce.py
- extract.py
- main.py
- README.md
- test_gcn_e.py
- test_gcn_v.py
- train_gcn_e.py
- train_gcn_v.py
- .gitignore
- DATASET.md
- LICENSE
- MODEL_ZOO.md
- README.md
- requirements.txt
# Installation Guide
1. Get the code
git clone https://github.com/yl-1993/learn-to-cluster
Downloads the entire project code from GitHub to your computer.
cd learn-to-cluster
Moves into the project folder you just downloaded.
2. Python
Easy RecommendedPrerequisites
pip install -r requirements.txt
Installs the Python libraries listed in requirements.txt (or similar).
If it runs without errors and prints output in the terminal, it worked.
Pulled directly from this repo's README.
// repository documentation
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