Which mountain dew won

While doing this,each player selects attributes that they believe would make the perfect Mountain Dew drink flavor, logo, name, color, etc. When phase two began in January , three flavor finalists had been chosen, but no other attributes were decided. The second phase revolved primarily around recruiting other people to join in on the DEWmocracy campaign. In the end, the three flavors had been given fan-chosen names, colors, and overall designs.

These three drinks were Voltage , Supernova , and Revolution. All three were released to the public during the summer of , and drinkers were given the objective to go to the official website and vote for which flavor they liked most. This lasted until the end of July, and Voltage was announced to be the official winner on August 19, , the second placer being Supernova, with Revolution in 3rd place.

A second DEWmocracy promotion was launched in to select another new flavor. Diet Supernova won. During the summer of , Mountain Dew started its " Back by Popular DEWmand " promotion, and re-released three flavors that its fans have been demanding since they were taken off the shelves.

Also during , Mountain Dew launched its " Throwback Shack " website, which gave visitors a chance to enter to win many Mountain Dew items of interest, including a "secret stash" of 12 brand new glass bottles of Revolution.

Mountain Dew Wiki Explore. Current Flavors. When we heard that a task called for careful visual inspection and counting, we knew computer vision would be a helpful tool. So, we did what any developer would do: trained an object detection model to recognize bottles that appear throughout the scene.

In this case, we're using a computer vision model to help us find any bottles we may have otherwise missed. The viewer should still identify the unique occurrences of each bottle across the scene when tweeting a submission.

Per the Official Rules , any type of bottle counts — but each bottle should only be counted once. For example, the bottle in the car John Cena drinks from is present multiple different times, but it should only be counted once towards the tally. First, we need a dataset of images from the ad. In this case, we can grab the exact video file of the commercial.

We'll need to split the video file into individual image frames in order to annotate the images and train a model. I created a dataset and dropped the Mountain Dew video into Roboflow, which asks what frame rate I'd like to sample.

I decided on doing three frames per second, which creates 92 images from the roughly second Super Bowl spot. Having each of the individual frames from the video is independently helpful: it means we can have a closer look at all of the places where the Mountain Dew bottles may be present.

Once we have these images, we need to annotate all of the bottles we can find in each of the scenes. While this is fairly similar to counting all of the bottles manually, remember we might not be perfect in finding all of the bottles with our own eyes.

So, hopefully, in teaching a computer vision model what bottles look like and then asking that same model to find bottles for us, we'll see any we may have missed. After labeling and deleting one completely black image from the vid , we have annotations across 91 images. We've open sourced this final Mountain Dew bottles image dataset :. Once we have our images collected and labeled, we can train a model to find bottles for us. Before training, however, we can use image augmentation to increase the size of our training dataset.

By applying random distortions like brightness changes , perspective changes , flips , and more, we can increase the volume and variability of our training dataset so that our model has more examples to learn from. We then made use of Roboflow Train , which gives us the option to one-click have a trained model available.