I am a professional photographer and I take photos of the interior of houses being listed for sale. The number of photos can range from 40 to 100 depending on the size of the house. After taking the photos I need to label them before delivery to my customers.
A sample is below
Photos: https://silverscreen.smugmug.com/Real-Estate/17050-Saint-Anne-Ln/
Virtual tour which needs labelling https://silver-screen-photography.seehouseat.com/1215335?a=1
Labeling photos is a tedious task. So I decided to build a classifier to do it.
Even before I finished Lessons 1 and 2, I could build and deploy such a classifier. I used downloaded images from Google and the classifier can classify images into 7 classes - bedrooms, bathrooms, front views, backyards, living rooms, kitchen and dining rooms.
The notebook is at https://github.com/suresh-subramaniam/fastai
I have not implemented the code to label the photo with the predicted category or created an application out of it. I plan to do it in the future.
I learnt the following
- It is easier to use a Chrome plugin to download photos from Google than the script suggested by Jeremy.
- The best accuracy I could get is around 83%. I had not cleaned the photos before training. I am sure that cleaning up the junk photos will improve the accuracy.
- Sometimes photos of rooms do not have furniture in them. It is harder or impossible to distinguish between a living room and a bedroom, if they both don’t have furniture. This uncertainty is reflected in the low accuracy.
I hope this was useful.