https://github.com/mrdbourke/airbnb-amenity-detection
Repo for 42 days project to replicate/improve Airbnb's amenity (object) detection pipeline.
https://github.com/mrdbourke/airbnb-amenity-detection
airbnb-amenity-detection computer-vision deep-learning detectron2 machine-learning machine-learning-project object-detection
Last synced: 17 days ago
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Repo for 42 days project to replicate/improve Airbnb's amenity (object) detection pipeline.
- Host: GitHub
- URL: https://github.com/mrdbourke/airbnb-amenity-detection
- Owner: mrdbourke
- Created: 2020-02-20T04:16:50.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-08-30T23:53:34.000Z (8 months ago)
- Last Synced: 2025-04-09T16:19:49.472Z (17 days ago)
- Topics: airbnb-amenity-detection, computer-vision, deep-learning, detectron2, machine-learning, machine-learning-project, object-detection
- Language: Jupyter Notebook
- Homepage: https://dbourke.link/airbnb42days
- Size: 53 MB
- Stars: 168
- Watchers: 7
- Forks: 56
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
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README
# Replicating Airbnb's Amenity Detection with Detectron2 👁 🛏
This repository contains all the code from a [42-day project](https://www.mrdbourke.com/42days) to replicate Airbnb's amenity detection using [Detectron2](https://github.com/facebookresearch/detectron2).
Sample image taken from: https://www.airbnb.com/rooms/2151100
**What's amenity detection?**
Object detection but for common and useful household items someone looking for an Airbnb rental might want to know about. For example, does this home have a fireplace?
Original inspiration was drawn from the article [Amenity Detection and Beyond — New Frontiers of Computer Vision at Airbnb](https://medium.com/airbnb-engineering/amenity-detection-and-beyond-new-frontiers-of-computer-vision-at-airbnb-144a4441b72e).
## Try it for yourself
To see the major pieces of the puzzle (data downloading, preprocessing, modelling), check out the [example Colab Notebook](https://colab.research.google.com/drive/1BRiFBC06OmWNkH4VpPl8Sf7IT21w7vXr).
You can also see a [live and deployed app which uses the model](https://airbnb-amenity-detection.appspot.com/) (note: this may have been taken down by the time you check it due to costs, an extension would be finding a way to deply the model for little to no costs).
If the deployed application doesn't work, you can watch [this video](https://youtu.be/smlQbh6jQvg) to get an idea:
[](http://www.youtube.com/watch?v=smlQbh6jQvg "I got my machine learning model deployed! | Airbnb Amenity Detection Part 8")
## What's in this repo?
* Notebooks with 00-10 are all the steps I took to train the full model, largely unchanged from when I originally wrote them.
* For a cleaned up version, see the [example Colab Notebook](https://colab.research.google.com/drive/1BRiFBC06OmWNkH4VpPl8Sf7IT21w7vXr).
* `preprocessing.py` contains the preprocessing functions for turning [Open Images images & labels](https://storage.googleapis.com/openimages/web/index.html) into [Detectron2 style](https://detectron2.readthedocs.io/tutorials/datasets.html).
* `downloadOI.py` is a slightly modified downloader script from [LearnOpenCV](https://www.learnopencv.com/fast-image-downloader-for-open-images-v4/) which downloads only certain classes of images from Open Images, example:
```
# Download only images from the Kitchen & dining room table class from Open Images validation set
!python3 downloadOI.py --dataset "validation" --classes "Kitchen & dining room table"
```
* `app` contains a Python script with a [Streamlit](https://www.streamlit.io) app built around the model, if you can see the live version, Streamlit is what I used to build it.
* `custom_images` contains a series of different images related to the project, including various rooms around my house to test the model.
## See how it was done
* [Daily progress, notes and code in a journal format](https://dbourke.link/airbnb42days) (since this was a 42-day project, I took notes every day on what I was working on).
* [YouTube video series](https://www.youtube.com/playlist?list=PL6vjgQ2-qJFeMrZ0sBjmnUBZNX9xaqKuM) starting from the project overview to building a model to deploying a model to wrapping up the project.
* All [modelling experiments](https://app.wandb.ai/mrdbourke/airbnb-amenity-detection) I tracked using Weights & Biases.
Questions/feedback/advice is welcome: daniel at mrdbourke dot com.