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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
import torch
import torch.nn as nn
from torchvision import transforms, models
from huggingface_hub import hf_hub_download
from PIL import Image
# Define the model architecture
class RealEstateClassifier(nn.Module):
def __init__(self):
super().__init__()
# Load ResNet50
self.model = models.resnet50(pretrained=False)
# Modify final layer for binary classification
num_ftrs = self.model.fc.in_features
self.model.fc = nn.Linear(num_ftrs, 2) # 2 classes: floorplan and no_image
def forward(self, x):
return self.model(x)
# Load the state dict
model_path = hf_hub_download("acd20000/FloorplanValidator", "best_floorplan_classifier.pt")
state_dict = torch.load(model_path, map_location=torch.device('cpu'))
# Create model and load weights
model = RealEstateClassifier()
model.load_state_dict(state_dict)
model.eval()
# Define transformation
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# Make a prediction
image = Image.open("your_image.jpg").convert('RGB')
input_tensor = transform(image).unsqueeze(0)
with torch.no_grad():
output = model(input_tensor)
probs = torch.softmax(output, dim=1)
pred_class = torch.argmax(probs, dim=1).item()
confidence = probs[0][pred_class].item()
result = {
'class': "floorplan" if pred_class == 0 else "non-floorplan",
'confidence': confidence
}
print(result)
```
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