README.md

metadata

license: mit
tags:
  - yolov8
  - object-detection
  - railway
  - wagon-number-recognition
  - ocr
  - computer-vision
  - ultralytics
datasets:
  - custom
language:
  - en
pipeline_tag: object-detection

YOLOv8 — Railway Wagon Big Number Detection

Model Description

A fine-tuned YOLOv8 model for detecting and localizing large identification numbers on railway wagons. This model was developed as part of a government railway monitoring system to automate wagon identification in real-time video streams.

The model accurately detects oversized wagon numbers painted on the side of freight and passenger cars, even under challenging conditions such as varying lighting, weather, partial occlusion, and motion blur.

Intended Use

Use Case	Description
Railway monitoring	Automated wagon identification in surveillance camera feeds
Logistics tracking	Real-time wagon number extraction for freight management
Infrastructure inspection	Integration into railway digitization pipelines

⚠️ Not intended for: General-purpose OCR, license plate recognition, or non-railway number detection.

Performance

Metric	Score
F1 Score	98.1%

Training Details

Dataset

• Source: Custom-annotated dataset of railway wagon images
• Size: 1240X800 images
• Annotation format: YOLO (bounding boxes)
• Classes: 1 (big_number)
• Train/Val split: 80/20

Hyperparameters

Parameter	Value
Base model	YOLOv8n / YOLOv8s
Image size	640
Batch size	16
Epochs	4
Optimizer	AdamW
Learning rate	0.01
Device	NVIDIA GPU

Training Framework

• Ultralytics YOLOv8
• Python 3.10+
• PyTorch 2.x

How to Use

from ultralytics import YOLO

# Load the model
model = YOLO("best.pt")

# Run inference on an image
results = model("wagon_image.jpg")

# Display results
for result in results:
    boxes = result.boxes
    for box in boxes:
        print(f"Big Number detected | Confidence: {box.conf[0]:.2f} | BBox: {box.xyxy[0]}")

Model Files

File	Description
best.pt	Best checkpoint (highest validation metric)
last.pt	Last training epoch checkpoint

Limitations

• Trained specifically on railway wagon numbers; may not generalize to other number detection tasks
• Performance may degrade on heavily occluded or damaged wagon surfaces
• Optimized for daytime footage; low-light performance may vary

Citation

@misc{yolo8_bignumbers_2024,
  author = {Zarina},
  title  = {YOLOv8 Railway Wagon Big Number Detection},
  year   = {2024},
  url    = {https://huggingface.co/Zarinaaa/yolo8_BigNumbers_model}
}

Author

Zarina — Machine Learning Engineer specializing in Computer Vision, NLP, and Speech Technologies.

• 🤗 HuggingFace
• 💼 LinkedIn