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Using AI Models for Object Classification in MaixPy |
For example, if there are two images in front of you, one with an apple and the other with an airplane, the task of object classification is to input these two images into an AI model one by one. The model will then output two results, one for apple and one for airplane.
MaixPy provides a pre-trained 1000
classification model based on the imagenet
dataset, which can be used directly:
from maix import camera, display, image, nn
classifier = nn.Classifier(model="/root/models/mobilenetv2.mud")
cam = camera.Camera(classifier.input_width(), classifier.input_height(), classifier.input_format())
dis = display.Display()
while 1:
img = cam.read()
res = classifier.classify(img)
max_idx, max_prob = res[0]
msg = f"{max_prob:5.2f}: {classifier.labels[max_idx]}"
img.draw_string(10, 10, msg, image.COLOR_RED)
dis.show(img)
Result video:
Here, the camera captures an image, which is then passed to the classifier
for recognition. The result is displayed on the screen.
For more API usage, refer to the documentation for the maix.nn module.
Please go to MaixHub to learn and train classification models. When creating a project, select Classification Model
.