https://github.com/keerthananehru/barcode-decoder
A YOLOv8-based system for detecting, cropping, and decoding barcodes from images. Trained on a custom barcode dataset, evaluated with precision, recall, and mAP metrics, and implemented using Ultralytics YOLO and Pyzbar libraries.
https://github.com/keerthananehru/barcode-decoder
barcode-decoding barcode-detection computer-vision deep-learning object-detection opencv python pyzbar ultralytics yolov8
Last synced: about 2 months ago
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A YOLOv8-based system for detecting, cropping, and decoding barcodes from images. Trained on a custom barcode dataset, evaluated with precision, recall, and mAP metrics, and implemented using Ultralytics YOLO and Pyzbar libraries.
- Host: GitHub
- URL: https://github.com/keerthananehru/barcode-decoder
- Owner: KeerthanaNehru
- Created: 2025-04-28T17:58:50.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-04-29T19:16:21.000Z (about 2 months ago)
- Last Synced: 2025-04-30T03:53:34.104Z (about 2 months ago)
- Topics: barcode-decoding, barcode-detection, computer-vision, deep-learning, object-detection, opencv, python, pyzbar, ultralytics, yolov8
- Language: Jupyter Notebook
- Homepage:
- Size: 767 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Barcode Decoder
## Barcode Detection and Decoding System using YOLO
## 📌 Project Overview
This project builds a system to detect barcodes in images, crop the detected regions, and decode them to reveal the barcode information.
We use YOLOv8 (You Only Look Once) for barcode detection and Pyzbar library for decoding.
---
## 📚 Project Structure
- `Barcode_Decoder_Project_Model_Training.ipynb` → Trains YOLOv8 on custom barcode dataset.
- `Barcode_Decoder_Project_Model_Testing.ipynb` → Tests the trained model and decodes barcodes from test images.
- `dataset ->` "https://drive.google.com/drive/folders/1RoPo4aOoWh_ByNP-WMtlylC7FkpSdxHF?usp=sharing" -> new_dataset → Contains sample images used during training, validation, and testing.
- `README.md` → This file.
---
## 🛠️ Technologies Used
- Python
- Google Colab
- YOLOv8 (Ultralytics library)
- OpenCV
- Pyzbar
- ZBar (barcode scanning library)
---
## 🖼️ Dataset
- Custom barcode dataset created manually from large roboflow dataset
- Divided into `train`, `valid`, and `test` folders.
- Each folder contains:
- `/images` (actual images)
- `/labels` (YOLO annotation files)
---
## 🚀 How to Run the Project
1. Open the **Barcode_Decoder_Project_Model_Training.ipynb** notebook.
2. Mount your Google Drive.
3. Unzip dataset and train YOLOv8 model.
4. Save the trained model (`best.pt` or `saved_model.pt`).
Then:
5. Open the **Barcode_Decoder_Project_Model_Testing.ipynb** notebook.
6. Upload your trained model weights either `best.pt` or `saved_model.pt` but `best.pt` has best weights
7. Upload images for testing.
8. Predict barcode regions, crop, and decode them automatically.
---
## 📈 Model Performance
- Model evaluated using mAP, Precision, Confusion Matrix and Recall metrics.
- Metrics printed during validation step and their images stored in the runs
---
## ⚙️ Requirements
Install the following packages:
```bash
pip install ultralytics pyzbar opencv-python-headless
apt-get update
apt-get install -y libzbar0
```
## 🙋 Author
Keerthana Nehru
Barcode Decoder Project
April 2025