An image sensor is used to capture an image that includes a plurality of objects. Presence and location data is identified for the plurality of objects. The image and the presence and location data is utilized to create individual representations of the plurality of objects. The plurality of objects are classified through employment of the individual representations. A machine learning model is updated with the classification data generated by classifying the plurality of objects.

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: obtaining clothing article data stream data from one or more internet of things (IoT) device disposed in a computing environment, wherein the computing environment is collocated with a residence of a user, the clothing article data stream data representing one or more clothing article of the user; examining data of the clothing article data stream data to determine at least one clothing article parameter value of the one or more clothing article; in dependence at least one clothing article parameter value of the one or more clothing article, and providing user profile data that specifies predicted behavior of the user.

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: obtaining clothing article data stream data from one or more internet of things (IoT) device disposed in a computing environment, wherein the computing environment is collocated with a residence of a user, the clothing article data stream data representing one or more clothing article of the user; examining data of the clothing article data stream data to determine at least one clothing article parameter value of the one or more clothing article; in dependence at least one clothing article parameter value of the one or more clothing article, and providing user profile data that specifies predicted behavior of the user.

An analysis device for visualizing an accuracy of a trained determination device includes an acquisition unit acquiring an image pair of a non-defective product image and a defective product image, an extraction unit extracting an image region of a defective part of the defective product, a generation unit generating a plurality of image regions of pseudo-defective parts, a compositing unit synthesizing each of the image regions of the plurality of pseudo-defective parts with the non-defective product image to generate a plurality of composite images having different feature quantities, an unit outputting the plurality of composite images to the determination device and acquiring a label corresponding to each of the plurality of composite images from the determination device, and a display control unit displaying an object indicating the label corresponding to each of the plurality of composite images in an array based on the feature quantities.

A method for training an image segmentation model includes calling an encoder to perform feature extraction on a sample image and a scale image to obtain a sample image feature and a scale image feature. The method also includes performing class activation graph calculation to obtain a sample class activation graph and a scale class activation graph. The method also includes calling a decoder to obtain a sample segmentation result of the sample image, and calling the decoder to obtain a scale segmentation result of the scale image. The method also includes calculating a class activation graph loss and calculating a scale loss. The method also includes training the decoder based on the class activation graph loss and the scale loss.

An object detecting method includes: obtaining an object image of an object; obtaining an object feature map by performing feature extraction on the object image; obtaining decoded features by performing feature mapping on the object feature map by adopting a mapping network of an object recognition model; obtaining positions of prediction boxes by inputting the decoded features into a first prediction layer of the object recognition model to perform object regression prediction; and obtaining classes of objects within the prediction boxes by inputting the decoded features into a second prediction layer of the object recognition model to perform object class prediction.

A signal color determination device includes a processor configured to acquire a captured image. If a part of a plurality of signal lamps of a traffic signal is detected from the captured image, the processor determines a signal color of the traffic signal.

A signal color determination device includes a processor configured to acquire a captured image. If a part of a plurality of signal lamps of a traffic signal is detected from the captured image, the processor determines a signal color of the traffic signal.

A computer system is provided comprising a classification model management server computer configured, by instructions, to: receive a new image from a user device; apply a first digital model to first regions within the new image for classifying each of the first regions into a particular class; apply a second digital model to second regions within the new image for classifying each of the second regions into a particular class; and transmit classification data related to the class of the first regions and the class of the second regions to the user device. In connection therewith, the second regions each generally correspond to a combination of multiple first regions.

A computer system is provided comprising a classification model management server computer configured, by instructions, to: receive a new image from a user device; apply a first digital model to first regions within the new image for classifying each of the first regions into a particular class; apply a second digital model to second regions within the new image for classifying each of the second regions into a particular class; and transmit classification data related to the class of the first regions and the class of the second regions to the user device. In connection therewith, the second regions each generally correspond to a combination of multiple first regions.