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

Hacker News Discussion ↗

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.