---
license: mit
tags:
  - pytorch
  - object-detection
  - yolo
  - computer-vision
  - aircraft-detection
---

# WingID

## Model Description
A YOLO11l model fine-tuned for aircraft and bird detection. WingID is a real-time visual identification system capable of detecting and classifying flying objects — including various aircraft types and bird species — from camera feeds or static images.

## Model Architecture
- **Base Model**: YOLO11l (Large variant)
- **Framework**: PyTorch / Ultralytics
- **Task**: Object Detection
- **Input**: RGB images / video frames

## Training Details
- **Approach**: Fine-tuned YOLO11l on a curated dataset of aircraft and bird images
- **Augmentations**: Mosaic, random flip, scale jitter, HSV augmentation
- **Optimizer**: SGD / AdamW with cosine LR scheduling

## Performance
Achieves high mAP on the validation set for aircraft and bird detection across multiple classes.

## Files
| File | Description |
|------|-------------|
| `yolo11l.pt` | Fine-tuned YOLO11l model weights |

## Usage

```python
from ultralytics import YOLO
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(repo_id='devanshty/WingID', filename='yolo11l.pt')

# Load model
model = YOLO(model_path)

# Run inference
results = model('aircraft_image.jpg')
results[0].show()
```

## Download & Use

```python
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id='devanshty/WingID', filename='yolo11l.pt')
```