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imav-2025-gate
blackbeedrones/imav-2025-gate
imav-2025-gate is a object detection model from blackbeedrones. Use it when you need objects located in an image. It is set up for ultralytics. The card lists the license as apache-2.0.
Gate detection model for IMAV 2025 Indoor Competition - Mission 1.
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At a glance
- Task
- Object Detection
- Library
- ultralytics
- License
- apache-2.0
- Access
- Public
- Created
- Feb 2, 2026
- Updated
- Apr 12, 2026
- SHA
- 07b604bd
Model card
From the Hugging Face model README
IMAV 2025 Gate Detection - YOLOv11n
Gate detection model for IMAV 2025 Indoor Competition - Mission 1.
Competition Context
The 16th International Micro Air Vehicle Conference and Competition (IMAV 2025) took place in San Andrés Cholula, Puebla, Mexico. The competition theme was "Search and Rescue", inspired by Mexico's seismic activity and the need for micro air vehicles in disaster response scenarios.
Indoor Arena
3D view of the 10m × 10m indoor arena. Tunnel-like spaces are formed by gates in blue (1.5m), green (1m), and red (0.5m).
Target Object
Gate Specifications:
- Tube diameter: Ø 38.1 mm (1.5")
- Total height: 2m (base + window)
- Three tunnel sizes:
| Tunnel | Window Size | Base Height | Points |
|---|---|---|---|
| Wide (blue) | 1.5m × 1.5m | 0.5m | 1 |
| Medium (green) | 1.0m × 1.0m | 1.0m | 2 |
| Small (red) | 0.5m × 0.5m | 1.5m | 3 |
Mission 1: Enter the Room
The MAV must navigate through one of four entry options:
- Free passage (0 pts): 1m wide open path
- Wide tunnel (1 pt): 5 aligned gates, 1.5m × 1.5m window
- Medium tunnel (2 pts): 5 aligned gates, 1.0m × 1.0m window
- Small tunnel (3 pts): 5 aligned gates, 0.5m × 0.5m window
Each tunnel is 2m in length (5 gates aligned horizontally).
Performance
| Metric | Value |
|---|---|
| mAP@50 | 0.995 |
| mAP@50-95 | 0.991 |
| Precision | 1.0 |
| Recall | 0.999 |
Training Curves
Confusion Matrix
Validation Predictions
Model Formats
| Format | File | Use Case |
|---|---|---|
| PyTorch | gate_yolov11n.pt | Training, fine-tuning |
| ONNX | gate_yolov11n.onnx | Cross-platform inference |
| TensorRT | gate_yolov11n.engine | Jetson Orin Nano Super |
Training Configuration
| Parameter | Value |
|---|---|
| Base model | yolo11n.pt |
| Epochs | 27 (early stopping) |
| Image size | 640×640 |
| Batch | Auto |
| Optimizer | Auto |
| LR | 0.01 (cosine) |
| Augmentation | Mosaic, RandAugment |
Full config: train/args.yaml
Usage
Nectar SDK
from nectar.ai.detection import Detector
detector = Detector("blackbeedrones/imav-2025-gate:best.pt")
detector.load()
result = detector.detect(image, conf=0.5)
for det in result:
print(f"Gate: {det.confidence:.2f} at {det.center}")
Ultralytics
from ultralytics import YOLO
model = YOLO("best.pt")
results = model.predict(image, conf=0.5)
References
- Task
- Object Detection
- Library
- ultralytics
- License
- apache-2.0
- Created
- Feb 2, 2026
- Updated
- Apr 12, 2026