Bird species identification

One of Birdbuddy's core features is its bird-recognizing capability. It’s a handy tool for anyone trying to learn more about birds in a fun and accessible way. Birdbuddy's bird recognition is achieved through machine learning; the results of this process are only as good as the data input you give it when you train it. In Birdbuddy’s case, training images from our own worldwide camera network were used to create a bird-recognizing model that was then also tested in practice on Birdbuddy feeders. Birdbuddy's model is now capable of identifying many different bird species from all over the world and will continue to improve as it starts gathering ever more data.

You can also upload your own photos or record bird song and send them to our models manually with Photo and Sound ID.

Our AI model can currently identify over six thousand species! 

The recognition process

Once a feathery visitor lands on the feeder and its pictures are taken by Birdbuddy's camera module,  it is time for Birdbuddy to run its model and recognize the species.

When this process is completed, you will be able to select your favorite pictures of the identified visitor and add them to your Collections.

To give you a rough idea of the current pipeline, all images go through four stages of processing to ensure the best result:

1. First, we determine whether the image is interesting or not (if there is at least one bird in the photo). What’s interesting is highly subjective, of course, so even this simple step is something we struggled with at first. We landed on what we feel is a good definition for that (a bird is in the frame and in focus, at least one eye and the beak are visible). What’s awesome about this is that the images that are normally interesting to a human are also ones that AI will have a much easier time doing inference of the species on. 

2. Secondly, the photo goes through a bird detector model that marks the birds in the photo and crops them out. There will be cases of up to 6 specimens and up to 4 different species in the same image. We have trained the custom bird detection model to detect many birds in the same image. Every detected bird in the image is then passed through our bird species classifier.

3. In the next stage, we determine the species for every detection in the picture and further sort the cropped photos. With the help of advanced tagging and cropping tools, we classify them as either interesting, not interesting, or invalid (phantom detections). 

4. At least 2 thousand images per species are then introduced into the neural network. They are the material on which the model learns to distinguish between species. This is how Birdbuddy knows to take a snapshot when a bird appears in its field of view: the potential of this technology is only limited by the data we choose to feed it, so it will only get more accurate and complex as time goes on.

So far, the team has processed millions of images, fine-tuning the model to recognize bird species that regularly visit bird feeders.