🎯 Hit Detector Model

This PyTorch-based CNN detects holes on boards or paper using a sliding window approach. It was trained on image patches of size 24×24. The model scans larger images with this patch size to detect regions of interest.

Holes or defects must approximately fit within a 20×20 region to be accurately detected.

🤗 Model Card on Hugging Face.

🔗 Live Demo

Try the model here: Hit Detector Gradio Demo

📥 Model Inputs & Outputs

Input: RGB or grayscale image (PIL.Image)
Output: Annotated PIL.Image with red (or specified color) squares highlighting detected holes

🚀 Quick Start

🧠 Inference in Python

from PIL import Image
from  pipeline  import  HitDetectorPipeline

pipe  =  HitDetectorPipeline("model.pt")

img  = Image.open("input.png")
result  =  pipe(img)
result.save("output.png")
print("✅ Output saved to output.png")

📦 Installation

pip install -r requirements.txt

🧪 Testing in Docker

To test the model or pipeline scripts inside a clean container:

cd <project folder>
docker run -it --rm -p 7860:7860 -v $PWD:/appx:rw romanenco/python-tool-chain /bin/bash
cd /appx
pip install -r requirements.txt
python test_pipeline.py

You should see output.png generated as a result.

🌐 Run Gradio UI

pip install gradio
python app.py

Open http://127.0.0.1:7860 to test the interactive web UI.

🛠 Retrain or Fine-Tune

To retrain the model on your own dataset, use the full pipeline and tools from the main training repo, which includes:

📁 Tools to extract training patches from full images
🧠 Training script
📈 Inference script

📄 License

MIT

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