Migrasi batch otomatis

.gitattributes

@@ -33,3 +33,13 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+buffalo_l/CALFW_plot.png filter=lfs diff=lfs merge=lfs -text
+buffalo_l/CFPW_plot.png filter=lfs diff=lfs merge=lfs -text
+buffalo_l/CPLFW_plot.png filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -0,0 +1,62 @@
+---
+license: other
+license_name: model-distribution-disclaimer-license
+license_link: https://huggingface.co/spaces/deepghs/RDLicence
+pipeline_tag: feature-extraction
+tags:
+- onnx
+- face
+---
+
+ONNX models from [insightface project](https://github.com/deepinsight/insightface).
+
+# How To Use
+
+```shell
+pip install dghs-realutils>=0.1.0
+```
+
+```python
+from realutils.face.insightface import isf_face_batch_similarity, isf_analysis_faces, isf_faces_visualize
+
+image_path = "/your/image/file"
+# get the analysis all the faces
+faces = isf_analysis_faces(image_path)
+print(faces)
+
+# compare them
+print(isf_face_batch_similarity([face.embedding for face in faces]))
+
+# visualize it
+isf_faces_visualize(image_path, faces).show()
+
+```
+
+# Available Models
+
+We evaluated all these models with some evaluation datasets on face recognition.
+
+* CFPW (500 ids/7K images/7K pairs)[1]
+* LFW (5749 ids/13233 images/6K pairs)[2]
+* CALFW (5749 ids/13233 images/6K pairs)[3]
+* CPLFW (5749 ids/13233 images/6K pairs)[4]
+
+Below are the complete results and recommended thresholds.
+
+* Det: Success rate of face detection and landmark localization.
+* Rec-F1: Maximum F1 score achieved in face recognition.
+* Rec-Thresh: Optimal threshold determined by the maximum F1 score.
+
+| Model     | Eval ALL (Det/Rec-F1/Rec-Thresh)   | Eval CALFW (Det/Rec-F1/Rec-Thresh)   | Eval CFPW (Det/Rec-F1/Rec-Thresh)   | Eval CPLFW (Det/Rec-F1/Rec-Thresh)   | Eval LFW (Det/Rec-F1/Rec-Thresh)   |
+|:----------|:-----------------------------------|:-------------------------------------|:------------------------------------|:-------------------------------------|:-----------------------------------|
+| buffalo_l | 99.88% / 98.34% / 0.2203           | 100.00% / 95.75% / 0.2273            | 99.99% / 99.66% / 0.1866            | 99.48% / 96.41% / 0.2207             | 100.00% / 99.85% / 0.2469          |
+| buffalo_s | 99.49% / 96.87% / 0.1994           | 99.99% / 94.45% / 0.2124             | 99.65% / 98.64% / 0.1845            | 98.04% / 92.61% / 0.2019             | 100.00% / 99.68% / 0.2314          |
+
+[1] Sengupta Soumyadip, Chen Jun-Cheng, Castillo Carlos, Patel Vishal M, Chellappa Rama, Jacobs David W, Frontal to profile face verification in the wild, WACV, 2016.
+
+[2] Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, 2007.
+
+[3] Zheng Tianyue, Deng Weihong, Hu Jiani, Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments, arXiv:1708.08197, 2017.
+
+[4] Zheng, Tianyue, and Weihong Deng. Cross-Pose LFW: A Database for Studying Cross-Pose Face Recognition in Unconstrained Environments, 2018.
+

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+    "max_f1": 0.9575276193158405,
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+}

buffalo_l/CFPW_metrics.json

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+    "pr_auc": 0.9994035231656557,
+    "roc_auc": 0.9991370988207875
+}

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+}

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