The Top-1 error rate is a term used to describe the top-1 accuracy of an algorithm on a classification task.
Usually, the classifier outputs a score or confidence value for each class (“I’m 90% sure this image is of an animal”, “I’m 0.1% sure that this image is of a human”, etc.).
The correct response is considered to be in the Top-1 if the classifier’s top guess is right (e.g., the highest score is for the “animal” class, and the test image is indeed of an animal).
The right answer is considered to be in the Top-5 if it is at least one of the classifier’s top five guesses.
The Top-1 error is the proportion of the time the classifier does not provide the highest score to the correct class. The Top-5 error rate is the percentage of times the classifier failed to include the proper class among its top five guesses.
In simple terms, when you use a neural network to classify anything, you receive something that looks like a probability distribution for all of the classes.
For instance, you might obtain anything along the lines of:
80% – cats
55% – dogs
30% – birds
10% – deers
The top-1 number indicates how many times the network has predicted the correct label with the highest probability.
Machine learning algorithms are used extensively in current approaches to object recognition. We can collect larger datasets, build more powerful models, and utilize better overfitting prevention approaches to improve their performance. Until recently, annotated image datasets were modest — on the order of tens of thousands of photos.
Simple recognition tasks, especially when augmented with label-preserving transformations, may be solved pretty successfully with datasets of this size. On the MNIST digit-recognition challenge, for example, the current best error rate (0.3 percent) approaches human performance.
Although the drawbacks of small picture datasets have long been acknowledged, annotated datasets containing millions of photos have only lately become practical to acquire. LabelMe, which contains hundreds of thousands of fully segmented photos, and ImageNet, which contains over 15 million labeled high-resolution photos in over 22,000 categories, are two of the new larger datasets.
ImageNet is a collection of approximately 15 million high-resolution photos that have been classified into around 22,000 categories. Using Amazon’s Mechanical Turk crowd-sourcing technique, the photographs were gathered from the internet and labeled by human labelers.
An annual competition named the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) has been organized as part of the Pascal Visual Object Challenge since 2010. ILSVRC makes use of a subset of ImageNet, containing approximately 1000 photos in each of 1000 categories. A total of 1.2 million training photos, 50,000 validation images, and 150,000 testing photos are available.
On ImageNet, two error rates are commonly reported: top-1 and top-5, where the top-5 error rate is the percentage of test images for which the right label is not among the model’s top five most likely labels.
If the target label is the model’s top prediction, the model is said to have properly categorized the image.
To begin, you use the CNN to generate a prediction and acquire the predicted class multinomial distribution → ∑pclass = 1
You now check if the top class (the one with the highest probability) is the same as the target label in the case of the top-1 score.
If you’re looking for a top-5 score, you’ll want to see if the target label is one of your top five predictions (the 5 ones with the highest probabilities).
The top score is calculated by dividing the number of times a projected label matched the target label by the number of data points analyzed in both situations.
Finally, when using 5-CNNs, you must first average their predictions before computing the top-1 and top-5 scores using the same approach.