JuicyTurtle/model_test_1

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

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