ID Card Object Detection Model (YOLO)
📌 Overview
This model is a YOLO-based object detection model trained to detect regions on ID card images such as:
- ID card boundaries
- Face / portrait region
- Barcodes / QR codes
- Text regions / structured fields (depending on dataset)
The model is intended for:
- Document processing pipelines
- OCR preprocessing and region extraction
- Identity verification workflows
- Computer vision research and prototyping
The model outputs bounding boxes, class labels, and confidence scores.
🚀 How to Use
Python (Ultralytics)
from ultralytics import YOLO
model = YOLO('best.pt')
results = model.predict('image.jpg', conf=0.25)
results[0].show()
Download from Hugging Face
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id='miguelescamilla/id-card-yolo', filename='best.pt')
🧠 Model Details
- Architecture: YOLO (Ultralytics)
- Framework: PyTorch
- Input size: 640×640 (default)
- Task: Object Detection
- Outputs:
- Bounding boxes (xyxy)
- Class IDs
- Confidence scores
⚠️ Limitations
- Performance depends on image quality, lighting, and camera perspective.
- Accuracy is limited by the size and diversity of the training dataset.
- Not validated for safety-critical or regulated environments.
📜 License & Credits
Model Weights
This repository contains trained model weights uploaded by the author.
YOLO Framework Credit
This model was trained using Ultralytics YOLO, licensed under the AGPL-3.0 license.
Important: If you use this model in commercial or proprietary systems, you must comply with Ultralytics licensing terms or obtain a commercial license.
Ultralytics Links:
👤 Author
Uploaded by: miguelescamilla Last updated: 2026-01-12
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