|
|
--- |
|
|
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 |
|
|
|
