Create README

README.md

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+---
+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
+
+<p align="center">
+  <img src="001_20241208064334_[M][0@0][0].jpg" alt="Example prediction" width="600"/>
+</p>
+
+## 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 |
+|--------|-------|
+| Precision | `XX.X%` |
+| Recall | `XX.X%` |
+| F1 Score | `XX.X%` |
+| Accuracy | `XX.X%` |
+
+<!-- 🔧 TODO: Replace XX.X% with your actual metric values -->
+
+## Training Details
+
+### Dataset
+
+- **Source:** Custom-annotated dataset of railway wagon images
+- **Size:** `X,XXX` images <!-- TODO: fill in -->
+- **Annotation format:** YOLO (bounding boxes)
+- **Classes:** 1 (big_number)
+- **Train/Val split:** 80/20
+
+### Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| Base model | YOLOv8n / YOLOv8s <!-- specify which --> |
+| Image size | 640 |
+| Batch size | 16 |
+| Epochs | `XXX` |
+| Optimizer | AdamW |
+| Learning rate | 0.01 |
+| Device | NVIDIA GPU |
+
+<!-- 🔧 TODO: Update hyperparameters with actual values -->
+
+### Training Framework
+
+- [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics)
+- Python 3.10+
+- PyTorch 2.x
+
+## How to Use
+
+```python
+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
+
+```bibtex
+@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](https://huggingface.co/Zarinaaa)
+- 💼 [LinkedIn](https://linkedin.com/in/YOUR_LINKEDIN) <!-- TODO: add your link -->