MilitaryConvoy-YOLO11L — mAP@0.5: 50.0%
Browse files- README.md +66 -0
- best.pt +3 -0
- data.yaml +20 -0
- eval_metrics.json +6 -0
- last.pt +3 -0
README.md
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---
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license: mit
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tags:
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- ultralytics
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- yolo
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- yolo11
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- object-detection
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- military-vehicle-detection
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- convoy-detection
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- visdrone
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- aerial-detection
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- OmniSense
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- MediaSense
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- DeSense
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- ConnectiviaLabs
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datasets:
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- visdrone
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- military-vehicle-recognition
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pipeline_tag: object-detection
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library_name: ultralytics
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---
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# MilitaryConvoy-YOLO11L — Military Vehicle & Convoy Detection
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Part of **OmniSense / MediaSense / DeSense** — Connectivia Labs' defense AI platform.
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## Performance
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| Metric | Value |
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|--------|-------|
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| **mAP@0.5** | **50.0%** |
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| **mAP@0.5:0.95** | **30.4%** |
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| Precision | 57.0% |
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| Recall | 47.7% |
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| Architecture | YOLO11L |
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| Input size | 640×640 |
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| Training date | 2026-04-05 |
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## Classes (15 total)
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**Aerial/civilian (VisDrone):** `pedestrian` · `person` · `bicycle` · `car` · `van` · `truck` · `tricycle` · `awning-tricycle` · `bus` · `motor`
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**Military (DeSense):** `military-vehicle` · `tank` · `apc` · `afv` · `artillery`
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## Training
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- Base: YOLO11L (COCO pretrained)
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- Dataset: VisDrone2019-DET + MilitaryVehicleRecognition v7 (~9,300 images merged)
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- Epochs: 75 | Batch: 32 | Imgsz: 640 | Optimizer: AdamW + cosine LR
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- Augmentation: mosaic, mixup, copy-paste, flips, scale
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## Usage
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```python
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from ultralytics import YOLO
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model = YOLO('MuayThaiLegz/MilitaryConvoy-YOLO11L')
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results = model('aerial_frame.jpg')
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# Filter military classes only (indices 10-14)
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mil = [b for b in results[0].boxes if int(b.cls) >= 10]
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print(f'Military vehicles: {len(mil)}')
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```
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## DeSense Pipeline
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```
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Camera/Satellite → MilitaryConvoy-YOLO11L → ByteTrack
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→ DBSCAN convoy clustering → Stone Soup fusion → Geofence alert
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```
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> **OmniSense** · Pillars: MediaSense · DeSense · Connectivia Labs 🔒
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best.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:71af003311281eff8d54ef42149ebd2d7d1f3d3e44eca7107dbdf18eab4b5aec
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size 51219993
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data.yaml
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names:
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- pedestrian
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- person
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- bicycle
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- car
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- van
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- truck
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- tricycle
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- awning-tricycle
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- bus
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- motor
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- military-vehicle
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- tank
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- apc
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- afv
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- artillery
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nc: 15
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path: /content/drive/MyDrive/OmniSense/MilitaryConvoy/data/merged
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train: train/images
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val: val/images
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eval_metrics.json
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{
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"mAP50": 0.4995,
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"mAP50_95": 0.3045,
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"precision": 0.5699,
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"recall": 0.4771
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}
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last.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:644408cb3cb2221a01992b8096ef04c1d1a85ee11d61cbb26363ede81f705b73
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size 51219993
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