Show HN: Using YOLO to Detect Office Chairs in 40M Hotel Photos

Hacker News Discussion ↗

An experiment using the YOLO object-detection model to scan 40 million hotel photos for office chairs aims to help remote workers and business travelers find rooms with usable workspaces. Commenters explore how the system was built—combining custom training, manual verification, and affiliate hotel-photo APIs—while noting that a wheeled chair alone is a poor proxy for true ergonomics or productivity. The project prompts broader debate on whether hotels should cater more to “work-from-anywhere” guests, with many pointing instead to co-working spaces, business centers, and short-term rentals as better suited to serious remote work.

Project goals & outcomes

  • Tool uses YOLO-based object detection on ~40M hotel photos to highlight rooms with office/ergonomic-style chairs and desks.
  • Many commenters find the idea clever and practically useful for people who need to work while traveling.
  • Some report that detected “office chairs” are often in lobbies, conference rooms, or tiny “business centers,” not guest rooms.
  • Several note that wheeled/mesh chairs are a weak proxy for genuinely ergonomic or usable workspaces.

Data, labeling, and processing

  • Photo corpus includes all hotel image types: rooms, lobbies, spas, pools, exteriors, etc., coming from hotel content partners rather than scraping.
  • About 1,000 chairs were manually labeled to train the model.
  • Around 50k photos flagged by the model were manually reviewed via a custom “Tinder-like” verification app; claimed throughput ~60 photos/minute, completed in about a week using outsourced reviewers.
  • Deduplication: perceptual hashing (including dhash variants) is recommended, with some debate over robustness to crops and edits.

Tech stack and model choices

  • YOLO (Ultralytics implementation) was run locally; object detection over tens of millions of images reportedly took only a few days, with downloading being the bottleneck.
  • Cloud VLMs (e.g., Vertex AI) were considered too expensive and would require uploading all images.
  • Site stack: Python backend, NextJS frontend, MySQL, and Mapbox (with clustering) for the interactive map.

Travel, workspaces, and market gap

  • Many see a gap for hotels designed for remote workers: good chairs, desks at sensible heights, outlets, and external displays.
  • Others argue hotels are primarily for sleep and short business travel; deeper work should happen in offices, coworking spaces, or “business centers.”
  • Experiences differ by region and brand: some say even budget business hotels have adequate desks; others, especially in Europe, report “business” rooms with no real desk/chair.
  • Comparisons with Airbnbs and serviced apartments: often better for working but raise concerns about housing pressure.

Extensions and related ideas

  • Ideas include detecting all object types, clustering via CLIP/UMAP/HDBSCAN, identifying specific chair models, tagging dashcam footage (e.g., EV counts), and mapping pubs with pool tables.