Upload FloorplanValidator model

README.md

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+---
+license: mit
+tags:
+- floorplan
+- real-estate
+- image-classification
+datasets:
+- custom
+---
+
+# FloorplanValidator
+
+This model distinguishes between floorplan images and non-floorplan images in real estate listings.
+
+## Model Details
+
+- Model type: ResNet50 fine-tuned for binary classification
+- Task: Binary image classification
+- Training data: Custom dataset of floorplan and non-floorplan images
+- Class labels: 0 (floorplan), 1 (no_image)
+
+## Intended Use
+
+- Identify valid floorplan images in real estate listings
+- Filter out non-floorplan images
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+from PIL import Image
+from torchvision import transforms
+
+# Load model
+model_path = hf_hub_download("acd20000/FloorplanValidator", "model.pt")
+model = torch.load(model_path, map_location=torch.device('cpu'))
+model.eval()
+
+# Define transformation for input images
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+# Load and transform an image
+image = Image.open("your_image.jpg").convert('RGB')
+input_tensor = transform(image).unsqueeze(0)
+
+# Make prediction
+with torch.no_grad():
+    output = model(input_tensor)
+    probabilities = torch.softmax(output, dim=1)
+    predicted_class = torch.argmax(probabilities, dim=1).item()
+    confidence = probabilities[0][predicted_class].item()
+    
+    result = {
+        'class': "floorplan" if predicted_class == 0 else "non-floorplan",
+        'confidence': confidence
+    }
+    print(result)
+```

config.json

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+{
+  "model_type": "image-classification",
+  "num_classes": 2,
+  "class_names": [
+    "floorplan",
+    "no_image"
+  ],
+  "architecture": "resnet50",
+  "image_size": 224,
+  "transforms": {
+    "normalize_mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "normalize_std": [
+      0.229,
+      0.224,
+      0.225
+    ]
+  }
+}

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:81288a7a48ee5212bc5a0f218789ba6b8b208a9efa30767ddb100a33c9d21216
+size 94371306

transform.py

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+
+import torch
+from torchvision import transforms
+
+def get_transform():
+    return transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])