asahiner/car-warning-light-detection

Make sure git-xet is installed (https://hf.co/docs/hub/git-xet)

brew install git-xet git xet install

git clone https://huggingface.co/asahiner/car-warning-light-detection

If you want to clone without large files - just their pointers

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/asahiner/car-warning-light-detection

Make sure the hf CLI is installed

curl -LsSf https://hf.co/cli/install.sh | bash

Download the model

hf download asahiner/car-warning-light-detection

🚗 Car Warning Light Detection

An open-source deep learning model that detects and classifies vehicle dashboard warning lights from images.

The model supports both mobile inference (ONNX) and web/server inference (PyTorch) and is designed to work in real-world conditions with multiple warning lights visible at once.

🔍 What it does

• Detects multiple warning lights in a single dashboard image
• Classifies 68 different warning light types
• Provides confidence scores
• Designed for AI-assisted vehicle diagnostics
• Works with:
  • 📱 Mobile apps (ONNX)
  • 🌐 Web / backend servers (PyTorch)

🖼️ Demo / Screenshots

🧠 Model Architecture

• Backbone: ResNet50
• Input size: 3 × 224 × 224
• Head:
  • Dropout (0.3)
  • Linear (2048 → 512)
  • ReLU
  • BatchNorm
  • Dropout (0.15)
  • Linear (512 → 68)
• Parameters: ~24.6M

Architecture definition:
📄 model_architecture.py

📦 Files

🚀 Usage

PyTorch

import torch
from model_architecture import create_model

model = create_model(num_classes=68)
model.load_state_dict(
    torch.load("car_warning_lights_resnet50.pth", map_location="cpu")
)
model.eval()