Classes are (almost) over and I'm back!
I ran a Semantic Segmentation algorithm on Hotels 50K, and visualised the results here! Some quick notes:
- For the purpose of this test I only used unoccluded images
- I ran it locally on my computer for about 12 hours, and it only processed 1068 images, but it should be enough for now to see how it works.
- I just got access to the servers, and my next step is to figure out how to use them properly and run the segmentation there.
- This visualisation is slightly ugly (I'm not an HTML expert), so please forgive me for that.
- The details about the algorithm can be found here
- I had to modify the code though as they were using CUDA, which is a parallel computing platform that uses the GPU, and after two days of trying to set it up on my Mac I found out Mac doesn't support that at all 🎉
- That, however, made it work slower than it could have with CUDA support
As you can see from the visualisation, some images were segmented correctly. However, the algorithm failed for quite a lot of them:
- Clear, high quality, and simple images were segmented correctly
- The algorithm saw some bright light and shadows as objects
- Since those images were taken by visitors, a lot of them were very poorly taken, so those didn't work well
- In certain images with, for example, a crumbled blanket, the algorithm did not detect that blanket as one whole object, but rather as many little pieces. Again, the shadows are probably what tricked the algorithm into thinking it was many little objects
- Images that were flipped in one way or another didn't work as well -- a very major issue
- Although in a lot of images objects were detected correctly, their actual "category" is not always correct