I was playing with the Image Classification. I wanted to experiment to see how I could use it with multiple images at the same time. I know it sounds simple, but it is important to build off of what he had access to in class and expand it so that we can implement it in possibly a meaningful way.
Step 1 to doing that… really when you are learning anything coding is making multiple of the thing you want to appear, appear.
I decided that I wanted to remove the image we had already (the Tesla-cat) and experiment with a just as recognizable image: a golden retriever puppy. I wanted to see how specific the classification could get. In this case, the program successfully identified the image as a Golden Retriever with a 0.94 certainty.
After seeing this, I wanted to see how well the program could do with a less recognizable animal, a Toucan. I was thinking the system would say something like : colorful bird, large bird, tropical bird, etc. I was shocked when it returned toucan with a 0.99 certainty.
I was concerned about the Toucan because in the Tesla-cat sample, the system identifies the cat as an Egyptian Cat with 0.44 certainty. I consider that a misidentification as the cat is simply an average black cat, with no real distinguishing features.
But all identification questions aside, I wanted to experiment with getting multiple pictures to appear and subsequently, getting them accurately identified.
Problems I had:
- Determining a way to separate images. (it took a while, but this ended up being the first thing I could successfully complete)
- My labels would get all messed up (so basically the labels were at first, what I thought, stuck together, but then it turned out that once I separated the two labels, there was like a third ghost label getting drawn over and over again in the loop… eventually with the help of a fellow (Konrad) I realized that the background has to be looped as well in order to get a crisp, clear label).
- Identification of individual images (I couldn’t figure out whether or not I could do things in one or multiple label functions as well as multiple or one classifier functions, it turned out to be multiple… but at first before the separation both the golden retriever and the toucan were identified as turn bills with 0.00 certainty).
I’m going to link my GitHub where I have posted this code. It’s fun to play with honestly. I think in the future I would implement arrays, but honestly for me even getting the images correctly identified at the same time was a massive victory.