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---
license: mit
tags:
- image-classification
- resnet
- roadwork-detection
- competitor-model
---
# ResNet-18 Roadwork Detector
ResNet-18 model for eroadwork
## Model Details
- **Architecture**: ResNet-18
- **Task**: Binary image classification (Roadwork detection)
- **Framework**: PyTorch/torchvision
- **Input Size**: 224x224
- **Number of Parameters**: ~11M
- **Output Type**: sigmoid
## Usage
```python
import torch
from torchvision import models, transforms
from torch import nn
from PIL import Image
# Load model
model = models.resnet18(weights=None)
model.fc = nn.Linear(512, 2)
model.load_state_dict(torch.load('pytorch_model.bin'))
model.eval()
# Prepare image
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
image = Image.open('your_image.jpg')
input_tensor = transform(image).unsqueeze(0)
# Inference
with torch.no_grad():
output = model(input_tensor)
prediction = torch.nn.functional.softmax(output, dim=1)
print(f"No Roadwork: {prediction[0][0]:.2%}")
print(f"Roadwork: {prediction[0][1]:.2%}")
```
## Classes
- 0: No Roadwork
- 1: Roadwork
## Submitted By
5Fc8jh7Yu65v7K4hi9s6d3MkkGJ8g4
## Submission Time
2025-10-24 02:12:45
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