Thalos Housekeeping & Obstruction Safety – v1.0
(Roboflow → HuggingFace export)
This repository contains the Housekeeping & Obstruction Safety Detection model used in the Thalos Safety Intelligence pipeline.
It identifies housekeeping and obstruction-related hazards such as:
• tripping hazards
• misplaced pallets or boxes
• walkway obstructions
• storage blockages
• cluttered work areas
• general housekeeping violations in industrial, warehouse, and facility settings
This model was originally trained in Roboflow (Object Detection Large / YOLOv8-derived) and exported as a PyTorch .pt file.
It is now hosted on HuggingFace for independent, cost-controlled inference inside the Thalos YOLO service.
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🧹 Usage (Python)
from ultralytics import YOLO
model = YOLO("thalostech2025/thalos-housekeeping-safety-v1/housekeeping_obstructions_weights.pt")
results = model("example.jpg")
results.show()
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🌐 Load From URL
(used by Thalos YOLO Service)
import torch
import requests
from io import BytesIO
response = requests.get(HF_URL)
model = torch.load(BytesIO(response.content), map_location="cpu")
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🏷️ Labels / Classes
The following hazard classes are included:
pallet
box
storage_block
walkway_obstruction
misc_item
(May include additional classes depending on v1.0 dataset structure.)
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📄 License
This model is distributed under the AGPL-3.0 license, consistent with Roboflow export requirements.
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⚠️ Important Notes
• This repository only contains weights, not training data.
• Thalos uses this model via server-side inference in the YOLO microservice.
• Future versions (v2+) may be distilled or retrained for performance and speed improvements.
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👤 Maintainer
Thalos Tech (2025)
Safety Intelligence & Hazard Detection Platform
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