infinite000/in-20001

roadwork_v3_focal

Vision Transformer (ViT) model for binary roadwork detection, trained for Natix Subnet 72.

Model Details

• Base Model: google/vit-base-patch16-224-in21k
• Architecture: ViT-Base (86M parameters)
• Input Size: 224×224
• Output Classes: 2 (None, Roadwork)

Performance

Metric	Value
Accuracy	0.9839
MCC	0.9443
F1 Score	0.9903
AUC	0.9950
Subnet Reward	0.8962

Training Details

• Training Samples: 34,968
• Validation Samples: 4,418
• Validator Augmentations: True
• Label Smoothing: 0.05
• Confidence Margin: 0.1

Usage

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image

processor = AutoImageProcessor.from_pretrained("infinite000/in-20001")
model = AutoModelForImageClassification.from_pretrained("infinite000/in-20001")

image = Image.open("your_image.jpg")
inputs = processor(image, return_tensors="pt")
outputs = model(**inputs)
probs = outputs.logits.softmax(dim=1)
roadwork_prob = probs[0][1].item()

Model Card

See model_card.json for detailed metadata.