Teach based on images, from files or your webcam.
Teach based on one-second-long sounds, from files or your microphone.
Teach based on images, from files or your webcam.
More models will appear here as they’re developed.
Here are a few graphs that can help you understand how well your model is working.
Don’t worry if this doesn’t make sense at first—you don’t need to use any of this to use Teachable Machine and, in fact, most people don’t :)
Teachable Machine splits your samples into two buckets. That’s why you’ll see two labels, training and test, in the graphs below.
Training samples: (85% of the samples) are used to train the model how to correctly classify new samples into the classes you’ve made.
Test samples: (15% of the samples) are never used to train the model, so after the model has been trained on the training samples, they are used to check how well the model is performing on new, never-before-seen data.
Underfit: a model is underfit when it classifies poorly because the model hasn't captured the complexity of the training samples.
Overfit: a model is overfit when it learns to classify the training samples so closely that it fails to make correct classifications on the test samples.
Epochs: One epoch means that every training sample has been fed through the model at least once. If your epochs are set to 50, for example, it means that the model you are training will work through the entire training dataset 50 times.
Accuracy per class is calculated using the test samples. Check out the vocab section to learn more about test samples.
A confusion matrix summarizes how accurate your model's predictions are. You can use this matrix to figure out which classes the model gets confused about.
The y axis (Class) represents the class of your samples. The x axis (Prediction) represents the class that the model, after learning, guesses those samples belong to. So, if a sample’s Class is "Muffin" but its Prediction is "Cupcake", that means that after learning from your data, the model misclassified that Muffin sample as a Cupcake. This usually means that those two classes share characteristics that the model picks up on, and that particular "Muffin" sample was more similar to the "Cupcake" samples.
Accuracy is the percentage of classifications that a model gets right during training. If your model classifies 70 samples right out of 100, the accuracy is 70 / 100 = 0.7.
If the model's prediction is perfect, the accuracy is one; otherwise, the accuracy is lower than one.
Loss is a measure for evaluating how well a model has learned to predict the right classifications for a given set of samples. If the model's predictions are perfect, the loss is zero; otherwise, the loss is greater than zero.
To get an intuitive sense of what this measures, imagine you have two models: A and B. Model A predicts the right classification for a sample but is only 60% confident of that prediction. Model B also predicts the right classification for the same sample but is 90% confident of that prediction. Both models have the same accuracy, but model B has a lower loss value.