Liked on YouTube: How To Create Personal Image Classifier App In MIT App Inventor 2 [ AI App ]

How To Create Personal Image Classifier App In MIT App Inventor 2 [ AI App ]

How To Create Personal Image Classifier App In MIT App Inventor 2. This AI unit is broken into three parts. In part 1, students learn how to create and train their own image classification model to identify and classify images. In part 2, students use their model in an app using MIT App Inventor to see how their model performs. In part 3, students create another app using the same model. In this app, the image classification becomes a game, where users must match the emotional expression to score points. In Part 1, you will train your own personal image classification model to recognize facial expressions Watch the demo video below: 1Open your browser and go to https://ift.tt/34wsme1. Remember - your model doesn’t know anything yet, so you need to start with training. You can track our progress with the progress bar. 2First, add facial expressions (be creative!) for the model to learn using the add labels box. When starting out, try creating a model with just 2 or 3 expressions, but you can add as many as you’d like. 3Now, you can give examples for these labels. Click on any of your labels, pose in front of the webcam, and click "Add Example" to add an image to that label for your model to learn from (generally, more is better, but you can come back later). 4When you are satisfied with adding examples, click on the next button at the bottom to move onto the next step. 5In this step, you may change details about the model you are using. This is beyond the scope of this workshop, so just use the default settings and click “Train Model” to have the model learn the labels and images you added earlier. 6After clicking “Train Model” you can see some basic information about the training process, including how long it is taking and the “loss” of the model as the training progresses. The loss is a measurement of how well the model is performing on the images you gave it. When the training is finished, you will automatically be moved to the testing step. Here, you can add additional examples to test your model. This allows you to see how well your model performs on images it has not seen before. 7Adding examples in testing works the same way as in training. Just click on any of your labels and then the “Add Example” button to add an image for that label. When you are done, you can click on the “Predict” button to move on to the results step. The results page has a few useful tools that you can use to analyze the predictions of your model on the testing images. 8Look on the left side of the screen. Start by clicking through the individual results a few times with the previous and next buttons. Most likely, some of your testing images will be classified incorrectly. 9You can use the buttons “Label Correctness” and “Confidence Graph” to display useful tools that may allow us to figure out why some images were incorrectly (or correctly) labelled. By default. “Label Correctness” is displayed in the center of this page. TheWith the help of these tools, see if you can make your model even better. Go back to the training step to add more images (possibly multiple times), or if you want, completely start over. 15Here are some exercises that we encourage you to try with your partner in the process: Pick a testing image that is labelled incorrectly. Why do you think it’s labelled incorrectly? Try to improve the model to predict it correctly. Try testing images in different conditions (eg. your faces at different distances, different parts of the screen, different backgrounds, etc.). What happens? Based on what you see in the analysis tools, come up with hypotheses as to why some images are being labelled incorrectly. Then, see if you can trick the model into making a wrong prediction (eg. put an image of you doing expression “A” into the expression “B” label in the testing tab and see if you can make the model think it’s really expression “B”). Discuss with your partner about whether or not you think you can make the model be 100% accurate. If so, how? If not, what do you think is “good enough”? 16When you are satisfied with your model (or when time is up), download it with the “Download Model” button on the bottom right. Get excited to use the model you just built in an app!
via YouTube https://www.youtube.com/watch?v=K8qsy2ftG18