blackbeedrones/imav-2025-gate
By blackbeedrones · ↓ 3 · ♥ 0 · Updated 2026-02-02
What's in this repo
This page lists the files in blackbeedrones/imav-2025-gate, including best.onnx.
- 1 ONNX models (.onnx files)
- 51 files, including 21 .png, 20 .jpg, 2 .json, 1 .md, 1 .engine, 1 .onnx
- Last updated 2026-02-02
ONNX files
| File | Size | Download |
|---|---|---|
| best.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
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
mirela-sdk
from mirela_sdk.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
Model info
Created: 2026-02-02
Updated: 2026-02-02
View on Hugging Face