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