Abstract: An electronic device may determine whether a machine-learning model is operating within predefined limits. In particular, the electronic device may receive, from another electronic device, instructions for the machine-learning model, a reference input and a predetermined output of the machine-learning model for the reference input. Note that the instructions may include an architecture of the machine-learning model, weights associated with the machine-learning model and/or a set of pre-processing transformations for use when executing the machine-learning model on images. In response, the electronic device may configure the machine-learning model based on the instructions. Then, the electronic device may calculate an output of the machine-learning model for the reference input. Next, the electronic device may determine whether the machine-learning model is operating within predefined limits based on the output and the predetermined output.

Abstract: An electronic device may determine whether a machine-learning model is operating within predefined limits. In particular, the electronic device may receive, from another electronic device, instructions for the machine-learning model, a reference input and a predetermined output of the machine-learning model for the reference input. Note that the instructions may include an architecture of the machine-learning model, weights associated with the machine-learning model and/or a set of pre-processing transformations for use when executing the machine-learning model on images. In response, the electronic device may configure the machine-learning model based on the instructions. Then, the electronic device may calculate an output of the machine-learning model for the reference input. Next, the electronic device may determine whether the machine-learning model is operating within predefined limits based on the output and the predetermined output.

Abstract: An electronic device may determine whether a machine-learning model is operating within predefined limits. In particular, the electronic device may receive, from another electronic device, instructions for the machine-learning model, a reference input and a predetermined output of the machine-learning model for the reference input. Note that the instructions may include an architecture of the machine-learning model, weights associated with the machine-learning model and/or a set of pre-processing transformations for use when executing the machine-learning model on images. In response, the electronic device may configure the machine-learning model based on the instructions. Then, the electronic device may calculate an output of the machine-learning model for the reference input. Next, the electronic device may determine whether the machine-learning model is operating within predefined limits based on the output and the predetermined output.

Abstract: After analyzing images or videos, a computer system may display or present visual performance feedback with an interactive visual representation of a data-analysis pipeline, where the visual representation includes separate and coupled data-analysis operations in a set of data-analysis operations that includes the one or more machine-learning models. Moreover, in response to a user-interface command the specifies a given data-analysis operation, the computer system may display or present a group of images or videos and associated performance information for the given data-analysis operation, where a given image or video corresponds to an instance of the given data-analysis operation. Furthermore, when the computer system receives user feedback about one at least one of the images or videos in the group of images or videos, the computer system performs a remedial action based at least in part on the user feedback. For example, the computer system may dynamically modify the data-analysis pipeline.

Abstract: A computer system may train and use a machine-learning model to quantitatively analyze an image. In particular, the computer system may generate the machine-learning model based on a set of reference images that include content with instances of a quantitative feature attribute and one or more feedback metrics that specify locations of the instances of the quantitative feature attribute in the reference images and numerical values associated with the instances of the quantitative feature attribute. Then, after receiving the image from an electronic device, the computer system may analyze the image using the machine-learning model to perform measurements of one or more additional instances of the quantitative feature attribute in the image. Moreover, the computer system may provide a measurement result for the image, the measurement result including a second numerical value associated with the one or more additional instances of the quantitative feature attribute in the image.

Abstract: After analyzing images or videos, a computer system may display or present visual performance feedback with an interactive visual representation of a data-analysis pipeline, where the visual representation includes separate and coupled data-analysis operations in a set of data-analysis operations that includes the one or more machine-learning models. Moreover, in response to a user-interface command the specifies a given data-analysis operation, the computer system may display or present a group of images or videos and associated performance information for the given data-analysis operation, where a given image or video corresponds to an instance of the given data-analysis operation. Furthermore, when the computer system receives user feedback about one at least one of the images or videos in the group of images or videos, the computer system performs a remedial action based at least in part on the user feedback. For example, the computer system may dynamically modify the data-analysis pipeline.

Abstract: A computer system may train and use a machine-learning model to quantitatively analyze an image. In particular, the computer system may generate the machine-learning model based on a set of reference images that include content with instances of a quantitative feature attribute and one or more feedback metrics that specify locations of the instances of the quantitative feature attribute in the reference images and numerical values associated with the instances of the quantitative feature attribute. Then, after receiving the image from an electronic device, the computer system may analyze the image using the machine-learning model to perform measurements of one or more additional instances of the quantitative feature attribute in the image. Moreover, the computer system may provide a measurement result for the image, the measurement result including a second numerical value associated with the one or more additional instances of the quantitative feature attribute in the image.

Abstract: An electronic device may determine whether a machine-learning model is operating within predefined limits. In particular, the electronic device may receive, from another electronic device, instructions for the machine-learning model, a reference input and a predetermined output of the machine-learning model for the reference input. Note that the instructions may include an architecture of the machine-learning model, weights associated with the machine-learning model and/or a set of pre-processing transformations for use when executing the machine-learning model on images. In response, the electronic device may configure the machine-learning model based on the instructions. Then, the electronic device may calculate an output of the machine-learning model for the reference input. Next, the electronic device may determine whether the machine-learning model is operating within predefined limits based on the output and the predetermined output.

Abstract: A system may use a configurable detected to identify a feature in a received image and an associated candidate tag based on user-defined items of interest, and to determine an associated accuracy metric. Moreover, based on the accuracy metric, costs of requesting the feedback from one or more individuals and a feedback threshold, the system may use a scheduler to selectively obtain feedback, having a feedback accuracy, about the candidate tag from the one or more individuals. Then, the system may generate a revised tag based on the feedback when the feedback indicates the candidate tag is incorrect. Next, the system presents a result with the feature and the candidate tag or the revised tag to another electronic device. Furthermore, based on a quality metric, the system may update labeled data that are to be used to retrain the configurable detector.

Abstract: A system may use a configurable detected to identify a feature in a received image and an associated candidate tag based on user-defined items of interest, and to determine an associated accuracy metric. Moreover, based on the accuracy metric, costs of requesting the feedback from one or more individuals and a feedback threshold, the system may use a scheduler to selectively obtain feedback, having a feedback accuracy, about the candidate tag from the one or more individuals. Then, the system may generate a revised tag based on the feedback when the feedback indicates the candidate tag is incorrect. Next, the system presents a result with the feature and the candidate tag or the revised tag to another electronic device. Furthermore, based on a quality metric, the system may update labeled data that are to be used to retrain the configurable detector.

Abstract: A system may use a configurable detected to identify a feature in a received image and an associated candidate tag based on user-defined items of interest, and to determine an associated accuracy metric. Moreover, based on the accuracy metric, costs of requesting the feedback from one or more individuals and a feedback threshold, the system may use a scheduler to selectively obtain feedback, having a feedback accuracy, about the candidate tag from the one or more individuals. Then, the system may generate a revised tag based on the feedback when the feedback indicates the candidate tag is incorrect. Next, the system presents a result with the feature and the candidate tag or the revised tag to another electronic device. Furthermore, based on a quality metric, the system may update labeled data that are to be used to retrain the configurable detector.