Upload model and supporting files

.gitattributes

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+*.pkl filter=lfs diff=lfs merge=lfs -text

Model-0.4/config.json

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+{
+  "model_name": "tripleS-Member-Recognition 0.4",
+  "description": "Fine-tuned classifier model with confidence threshold of 0.4. Higher recall, lower precision.",
+  "threshold": 0.4,
+  "fine_tuned": true,
+  "model_file": "face_model.pkl",
+  "embedding_model": "buffalo_l",
+  "format": "pkl",
+  "total_persons": 24,
+  "total_samples": 13951,
+  "num_classes": 24,
+  "person_names": [
+    "ChaeWon",
+    "ChaeYeon",
+    "DaHyun",
+    "HaYeon",
+    "HyeRin",
+    "JiWoo",
+    "JiYeon",
+    "JooBin",
+    "Kaede",
+    "Kotone",
+    "Lynn",
+    "Mayu",
+    "NaKyoung",
+    "Nien",
+    "SeoAh",
+    "SeoYeon",
+    "ShiOn",
+    "SoHyun",
+    "SooMin",
+    "Sullin",
+    "Xinyu",
+    "YeonJi",
+    "YooYeon",
+    "YuBin"
+  ]
+}

Model-0.4/face_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dc2ad2a7d4c07ba199ada263a6948f01599a35509b55bb55999e13d70f8ae9a3
+size 16295426

Model-0.4/member.json

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+[
+  "ChaeWon",
+  "ChaeYeon",
+  "DaHyun",
+  "HaYeon",
+  "HyeRin",
+  "JiWoo",
+  "JiYeon",
+  "JooBin",
+  "Kaede",
+  "Kotone",
+  "Lynn",
+  "Mayu",
+  "NaKyoung",
+  "Nien",
+  "SeoAh",
+  "SeoYeon",
+  "ShiOn",
+  "SoHyun",
+  "SooMin",
+  "Sullin",
+  "Xinyu",
+  "YeonJi",
+  "YooYeon",
+  "YuBin"
+]

Model-0.5/config.json

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+{
+  "model_name": "tripleS-Member-Recognition 0.5",
+  "description": "Fine-tuned classifier model with confidence threshold of 0.5. Balanced recall and precision.",
+  "threshold": 0.5,
+  "fine_tuned": true,
+  "model_file": "face_model.pkl",
+  "embedding_model": "buffalo_l",
+  "format": "pkl",
+  "total_persons": 24,
+  "total_samples": 13707,
+  "num_classes": 24,
+  "person_names": [
+    "ChaeWon",
+    "ChaeYeon",
+    "DaHyun",
+    "HaYeon",
+    "HyeRin",
+    "JiWoo",
+    "JiYeon",
+    "JooBin",
+    "Kaede",
+    "Kotone",
+    "Lynn",
+    "Mayu",
+    "NaKyoung",
+    "Nien",
+    "SeoAh",
+    "SeoYeon",
+    "ShiOn",
+    "SoHyun",
+    "SooMin",
+    "Sullin",
+    "Xinyu",
+    "YeonJi",
+    "YooYeon",
+    "YuBin"
+  ]
+}

Model-0.5/face_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:18d7861c9b0335568e089eb72f5092d1002b596f4848a08b9e9d52ad9793493a
+size 16889682

Model-0.5/member.json

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+[
+  "ChaeWon",
+  "ChaeYeon",
+  "DaHyun",
+  "HaYeon",
+  "HyeRin",
+  "JiWoo",
+  "JiYeon",
+  "JooBin",
+  "Kaede",
+  "Kotone",
+  "Lynn",
+  "Mayu",
+  "NaKyoung",
+  "Nien",
+  "SeoAh",
+  "SeoYeon",
+  "ShiOn",
+  "SoHyun",
+  "SooMin",
+  "Sullin",
+  "Xinyu",
+  "YeonJi",
+  "YooYeon",
+  "YuBin"
+]

Model-0.6/config.json

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+{
+  "model_name": "tripleS-Member-Recognition 0.6",
+  "description": "Fine-tuned classifier model with confidence threshold of 0.6. Higher precision, lower recall.",
+  "threshold": 0.6,
+  "fine_tuned": true,
+  "model_file": "face_model.pkl",
+  "embedding_model": "buffalo_l",
+  "format": "pkl",
+  "total_persons": 24,
+  "total_samples": 13358,
+  "num_classes": 24,
+  "person_names": [
+    "ChaeWon",
+    "ChaeYeon",
+    "DaHyun",
+    "HaYeon",
+    "HyeRin",
+    "JiWoo",
+    "JiYeon",
+    "JooBin",
+    "Kaede",
+    "Kotone",
+    "Lynn",
+    "Mayu",
+    "NaKyoung",
+    "Nien",
+    "SeoAh",
+    "SeoYeon",
+    "ShiOn",
+    "SoHyun",
+    "SooMin",
+    "Sullin",
+    "Xinyu",
+    "YeonJi",
+    "YooYeon",
+    "YuBin"
+  ]
+}

Model-0.6/face_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:063f8579d8f7d58723f27474f5fe7e691b7a2a98d062751ffffa66c309350da3
+size 17332002

Model-0.6/member.json

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+[
+  "ChaeWon",
+  "ChaeYeon",
+  "DaHyun",
+  "HaYeon",
+  "HyeRin",
+  "JiWoo",
+  "JiYeon",
+  "JooBin",
+  "Kaede",
+  "Kotone",
+  "Lynn",
+  "Mayu",
+  "NaKyoung",
+  "Nien",
+  "SeoAh",
+  "SeoYeon",
+  "ShiOn",
+  "SoHyun",
+  "SooMin",
+  "Sullin",
+  "Xinyu",
+  "YeonJi",
+  "YooYeon",
+  "YuBin"
+]

Model-base/config.json

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+{
+  "model_name": "tripleS-Member-Recognition base",
+  "description": "Base classifier model trained only on the tripleS Member Objekt dataset without any fine-tuning or external data.",
+  "fine_tuned": false,
+  "model_file": "face_model.pkl",
+  "embedding_model": "buffalo_l",
+  "format": "pkl",
+  "total_persons": 24,
+  "total_samples": 3480,
+  "num_classes": 24,
+  "person_names": [
+    "ChaeWon",
+    "ChaeYeon",
+    "DaHyun",
+    "HaYeon",
+    "HyeRin",
+    "JiWoo",
+    "JiYeon",
+    "JooBin",
+    "Kaede",
+    "Kotone",
+    "Lynn",
+    "Mayu",
+    "NaKyoung",
+    "Nien",
+    "SeoAh",
+    "SeoYeon",
+    "ShiOn",
+    "SoHyun",
+    "SooMin",
+    "Sullin",
+    "Xinyu",
+    "YeonJi",
+    "YooYeon",
+    "YuBin"
+  ]
+}

Model-base/member.json

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+[
+  "ChaeWon",
+  "ChaeYeon",
+  "DaHyun",
+  "HaYeon",
+  "HyeRin",
+  "JiWoo",
+  "JiYeon",
+  "JooBin",
+  "Kaede",
+  "Kotone",
+  "Lynn",
+  "Mayu",
+  "NaKyoung",
+  "Nien",
+  "SeoAh",
+  "SeoYeon",
+  "ShiOn",
+  "SoHyun",
+  "SooMin",
+  "Sullin",
+  "Xinyu",
+  "YeonJi",
+  "YooYeon",
+  "YuBin"
+]

Model-base/svm_face_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cf1c26d2704b8a3ec0b5b4df372d095bbfffc66fdf1add284de5349573e711a1
+size 7690050

README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: image-classification
+tags:
+- face_recognition
+- tripleS
+- k-pop
+- computer-vision
+- face-classification
+---
+
+# tripleS Member Recognition Model
+
+
+This repository contains face recognition models specifically trained to identify members of the K-pop girl group **tripleS**. The models are built using deep learning techniques with InsightFace embeddings and can classify faces of all 24 tripleS members with varying levels of precision and recall depending on the model version.
+
+## Model Versions
+
+This repository includes multiple model versions, each trained with different confidence thresholds to provide various trade-offs between precision and recall:
+
+### Base Model (`Model-base/`)
+- **Description**: Base classifier model trained only on the tripleS Member Objekt dataset without any fine-tuning or external data
+- **Training samples**: 3,480 samples
+- **Fine-tuned**: No
+- **Use case**: Baseline performance reference
+
+### Version 0.4 (`Model-0.4/`)
+- **Confidence threshold**: ≥ 0.4
+- **Description**: Trained using more inclusive data, resulting in a model that may generalize better but risks misclassification
+- **Training samples**: 13,951 samples
+- **Characteristics**: Higher recall, lower precision
+- **Use case**: When you want to catch as many faces as possible, even with some false positives
+
+### Version 0.5 (`Model-0.5/`)
+- **Confidence threshold**: ≥ 0.5
+- **Description**: Balanced training set offering a trade-off between recall and precision
+- **Training samples**: 13,707 samples
+- **Characteristics**: Balanced recall and precision
+- **Use case**: General-purpose recognition with balanced performance
+
+### Version 0.6 (`Model-0.6/`)
+- **Confidence threshold**: ≥ 0.6
+- **Description**: Trained only on high-confidence samples, producing a stricter classifier with higher precision
+- **Training samples**: 13,358 samples
+- **Characteristics**: Higher precision, lower recall
+- **Use case**: When accuracy is critical and you want to minimize false positives
+
+## Recognized Members
+
+The model can identify all 24 tripleS members:
+
+**Korean Members**: ChaeWon, ChaeYeon, DaHyun, HaYeon, HyeRin, JiWoo, JiYeon, JooBin, NaKyoung, SeoAh, SeoYeon, ShiOn, SoHyun, SooMin, YeonJi, YooYeon, YuBin
+
+**Japanese Members**: Kaede, Kotone, Lynn, Mayu
+
+**Chinese Members**: Nien, Sullin, Xinyu
+
+## Model Files Structure
+
+Each model version contains:
+- `config.json`: Model configuration and metadata
+- `face_model.pkl`: Trained model weights
+- `face_model_names.json`: Label mapping (versions 0.4-0.6 only)
+
+## Model Selection Guide
+
+- **Choose Base Model** if you want the original performance without fine-tuning
+- **Choose Version 0.4** if you prioritize detecting all possible members (high recall)
+- **Choose Version 0.5** if you want balanced performance for general use cases
+- **Choose Version 0.6** if you prioritize accuracy and want to minimize false identifications