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+ x755:
1471
+ - 0.0
1472
+ x756:
1473
+ - 0.0
1474
+ x757:
1475
+ - 0.0
1476
+ x758:
1477
+ - 0.0
1478
+ x759:
1479
+ - 0.0
1480
+ x76:
1481
+ - 0.0
1482
+ x760:
1483
+ - 0.0
1484
+ x761:
1485
+ - 0.0
1486
+ x762:
1487
+ - 0.0
1488
+ x763:
1489
+ - 0.0
1490
+ x764:
1491
+ - 0.0
1492
+ x765:
1493
+ - 0.0
1494
+ x766:
1495
+ - 0.0
1496
+ x767:
1497
+ - 0.0
1498
+ x77:
1499
+ - 0.0
1500
+ x78:
1501
+ - 0.0
1502
+ x79:
1503
+ - 0.0
1504
+ x8:
1505
+ - 0.0
1506
+ x80:
1507
+ - 0.0
1508
+ x81:
1509
+ - 0.0
1510
+ x82:
1511
+ - 0.0
1512
+ x83:
1513
+ - 0.0
1514
+ x84:
1515
+ - 0.0
1516
+ x85:
1517
+ - 0.0
1518
+ x86:
1519
+ - 0.0
1520
+ x87:
1521
+ - 0.0
1522
+ x88:
1523
+ - 0.0
1524
+ x89:
1525
+ - 0.0
1526
+ x9:
1527
+ - 0.0
1528
+ x90:
1529
+ - 0.0
1530
+ x91:
1531
+ - 0.0
1532
+ x92:
1533
+ - 0.0
1534
+ x93:
1535
+ - 0.0
1536
+ x94:
1537
+ - 0.0
1538
+ x95:
1539
+ - 0.0
1540
+ x96:
1541
+ - 0.0
1542
+ x97:
1543
+ - 0.0
1544
+ x98:
1545
+ - 0.0
1546
+ x99:
1547
+ - 0.0
1548
  ---
1549
+
1550
+ # Model description
1551
+
1552
+ 一个简易说的人脸识别baseline，使用openai/clip-vit-base-patch16 + LDA的策略
1553
+
1554
+ ## Intended uses & limitations
1555
+
1556
+ 整体需要配合github对应的代码使用
1557
+
1558
+ ## Training Procedure
1559
+
1560
+ [More Information Needed]
1561
+
1562
+ ### Hyperparameters
1563
+
1564
+ <details>
1565
+ <summary> Click to expand </summary>
1566
+
1567
+ | Hyperparameter | Value |
1568
+ |----------------------|---------|
1569
+ | covariance_estimator | |
1570
+ | n_components | 512 |
1571
+ | priors | |
1572
+ | shrinkage | |
1573
+ | solver | svd |
1574
+ | store_covariance | False |
1575
+ | tol | 0.0001 |
1576
+
1577
+ </details>
1578
+
1579
+ ### Model Plot
1580
+
1581
+ <style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LinearDiscriminantAnalysis(n_components=512)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">LinearDiscriminantAnalysis</label><div class="sk-toggleable__content"><pre>LinearDiscriminantAnalysis(n_components=512)</pre></div></div></div></div></div>
1582
+
1583
+ ## Evaluation Results
1584
+
1585
+ [More Information Needed]
1586
+
1587
+ # How to Get Started with the Model
1588
+
1589
+ [More Information Needed]
1590
+
1591
+ # Model Card Authors
1592
+
1593
+ Cheng Li(https://github.com/LC1332)
1594
+
1595
+ # Model Card Contact
1596
+
1597
+ You can contact the model card authors through following channels:
1598
+ [More Information Needed]
1599
+
1600
+ # Citation
1601
+
1602
+ @inproceedings{wang2018devil,
1603
+ title={The devil of face recognition is in the noise},
1604
+ author={Wang, Fei and Chen, Liren and Li, Cheng and Huang, Shiyao and Chen, Yanjie and Qian, Chen and Loy, Chen Change},
1605
+ booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
1606
+ pages={765--780},
1607
+ year={2018}
1608
+ }
config.json ADDED
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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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