🏗️ SiteSense — Model Weights
Real-Time Construction Equipment Monitoring via Aerial Computer Vision
Overview
This repository hosts the trained model weights for SiteSense — a real-time pipeline that detects, tracks, identifies, and classifies the activity of heavy construction equipment from drone/aerial video footage.
The system processes each frame through a multi-phase pipeline:
Video Frame → RF-DETR Detection → BoT-SORT Tracking → DINOv3 Re-ID → Activity Classification → Kafka Events
Model Weights
| File | Size | Architecture | Task | Training Data |
|---|---|---|---|---|
rfdetr_construction.pth |
122 MB | RF-DETR (Real-time Foundation DETR) | 8-class object detection | Custom aerial construction dataset (Roboflow) |
dinov3_reid_head.pth |
5.4 MB | Linear projection head (1536→256→128) | Equipment re-identification | Contrastive pairs from tracked equipment |
osnet_x0_25_msmt17.pt |
2.9 MB | OSNet x0.25 | Appearance-based ReID for BoT-SORT | MSMT17 (pretrained) |
Note: The DINOv3 ViT-B/16 backbone (~327 MB) is not included here. It is auto-downloaded from facebook/dinov3-vitb16-pretrain-lvd1689m on first run using your
HF_TOKEN.
Detection Classes
The RF-DETR detector is fine-tuned to recognize 8 classes of construction equipment from aerial perspectives:
| ID | Class | ID | Class |
|---|---|---|---|
| 0 | Excavator | 4 | Mobile Crane |
| 1 | Dump Truck | 5 | Tower Crane |
| 2 | Bulldozer | 6 | Roller Compactor |
| 3 | Wheel Loader | 7 | Cement Mixer |
Training Results
RF-DETR Detector
| Metric | Value |
|---|---|
| mAP@50 | 0.8340 |
| mAP@50:95 | 0.7607 |
| F1 Score | 0.8859 |
| Precision | 0.8666 |
| Recall | 0.9061 |
| Resolution | 560×560 |
| Epochs | 70 |
DINOv3 Re-ID Projection Head
| Metric | Value |
|---|---|
| Contrastive Loss | 0.0482 |
| Accuracy | 96.8% |
| Embedding Dim | 128-d L2-normalized |
| Training Pairs | ~12,000 positive pairs |
Quick Start
Option A: Download All Weights (Recommended)
pip install huggingface_hub
huggingface-cli download Zaafan/sitesense-weights --local-dir models/
Option B: Python API
from huggingface_hub import hf_hub_download
# Download individual weights
hf_hub_download(repo_id="Zaafan/sitesense-weights", filename="rfdetr_construction.pth", local_dir="models/")
hf_hub_download(repo_id="Zaafan/sitesense-weights", filename="dinov3_reid_head.pth", local_dir="models/")
hf_hub_download(repo_id="Zaafan/sitesense-weights", filename="osnet_x0_25_msmt17.pt", local_dir="models/")
Option C: Auto-Download (Zero Setup)
The SiteSense pipeline automatically downloads missing weights on first run:
# In services/cv-inference/main.py — resolve_weights() handles this transparently
weights_path = resolve_weights('rfdetr_construction.pth') # local first, HF fallback
Usage with SiteSense Pipeline
# 1. Clone the repository
git clone https://github.com/Mahmoud-Zaafan/asdfqer.git
cd asdfqer
# 2. Download weights
huggingface-cli download Zaafan/sitesense-weights --local-dir models/
# 3. Configure environment
cp .env.example .env
# 4. Launch infrastructure + run pipeline
docker compose up --build
docker compose --profile pipeline up cv-inference
Citation
If you use these weights in your research or projects, please cite:
@misc{sitesense2025,
author = {Mahmoud Zaafan},
title = {SiteSense: Real-Time Construction Equipment Monitoring via Aerial Computer Vision},
year = {2025},
url = {https://github.com/Mahmoud-Zaafan/SiteSense}
}
License
All weights are released under the MIT License.