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similarity_model_0.6.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c6da469b468a640af58f3f5c4c3e2c7a84063ade75aeb36a8186e1e59ee91d00
+size 82433310

similarity_model_0.6.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:49a0cfa74e3706800013658d1ec64f56427625000d0a5b7354bd3fd890c33453
+size 82951851

similarity_model_0.6_inference_example.py

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+#!/usr/bin/env python3
+"""
+ONNX 모델을 사용한 이미지 유사도 추론 예제
+"""
+
+import onnxruntime as ort
+import numpy as np
+from PIL import Image
+import torchvision.transforms as transforms
+
+# 전처리 파이프라인
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                        std=[0.229, 0.224, 0.225])
+])
+
+def preprocess_image(image_path):
+    """이미지 전처리"""
+    image = Image.open(image_path).convert('RGB')
+    tensor = transform(image)
+    return tensor.unsqueeze(0).numpy()  # 배치 차원 추가
+
+def predict_similarity(onnx_model_path, image1_path, image2_path):
+    """이미지 쌍 유사도 예측"""
+
+    # ONNX 세션 생성
+    session = ort.InferenceSession(onnx_model_path)
+
+    # 이미지 전처리
+    img1 = preprocess_image(image1_path)
+    img2 = preprocess_image(image2_path)
+
+    # 추론 실행
+    inputs = {'image1': img1, 'image2': img2}
+    logits = session.run(None, inputs)[0]
+
+    # 시그모이드로 확률 변환
+    similarity = 1 / (1 + np.exp(-logits[0][0]))
+
+    return similarity
+
+# 사용 예시
+if __name__ == "__main__":
+    onnx_path = "room_image_comparator.onnx"
+    img1_path = "room1.jpg"
+    img2_path = "room2.jpg"
+
+    similarity = predict_similarity(onnx_path, img1_path, img2_path)
+    print(f"유사도: {similarity:.4f}")

similarity_model_0.6_model_info.json

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+{
+  "image_size": [
+    224,
+    224
+  ],
+  "channels": 3,
+  "pixel_range": [
+    0.0,
+    1.0
+  ],
+  "normalization": {
+    "mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "description": "ImageNet normalization for EfficientNet"
+  },
+  "input_format": "RGB",
+  "resize_method": "bilinear",
+  "center_crop": true,
+  "batch_dimension": 0,
+  "tensor_layout": "NCHW"
+}