Abstract: Systems, methods, and computer-readable storage media for object detection and classification, and particularly produce detection and classification. A system configured according to this disclosure can receiving, at a processor, an image of an item. The system can then perform, across multiple pre-trained neural networks, feature detection on the image, resulting in feature maps of the image. These feature maps can be concatenated and combined, then input into an additional neural network for feature detection on the combined feature map, resulting in tiered neural network features. The system then classifies, via the processor, the item based on the tiered neural network features.

Abstract: In some embodiments, apparatuses, systems, and methods are provided herein useful to assessing products. In some embodiments, an enclosure for use in assessing products comprises a housing including a door configured to allow placement of a product within the housing, a product holding surface located within the housing allowing pictures to be taken through the product holding surface and configured to support the product, a first image capture device configured to capture an image of the product from a first perspective, a second image capture device configured to capture an image of the product from a second perspective, and wherein the image of the product from the second perspective is captured through the product holding surface, and a lighting element, wherein the lighting element is located within the housing, and wherein the lighting element is configured to provide lighting within the housing.

Abstract: Systems, methods, and computer-readable storage media for object detection and classification, and particularly produce detection and classification. A system configured according to this disclosure can receiving, at a processor, an image of an item. The system can then perform, across multiple pre-trained neural networks, feature detection on the image, resulting in feature maps of the image. These feature maps can be concatenated and combined, then input into an additional neural network for feature detection on the combined feature map, resulting in tiered neural network features. The system then classifies, via the processor, the item based on the tiered neural network features.

Abstract: Systems, methods, and computer-readable storage media for object detection and classification, and particularly produce detection and classification. A system configured according to this disclosure can receiving, at a processor, an image of an item. The system can then perform, across multiple pre-trained neural networks, feature detection on the image, resulting in feature maps of the image. These feature maps can be concatenated and combined, then input into an additional neural network for feature detection on the combined feature map, resulting in tiered neural network features. The system then classifies, via the processor, the item based on the tiered neural network features.

Abstract: In some embodiments, apparatuses, systems, and methods are provided herein useful to assessing products. In some embodiments, an enclosure for use in assessing products comprises a housing including a door configured to allow placement of a product within the housing, a product holding surface located within the housing allowing pictures to be taken through the product holding surface and configured to support the product, a first image capture device configured to capture an image of the product from a first perspective, a second image capture device configured to capture an image of the product from a second perspective, and wherein the image of the product from the second perspective is captured through the product holding surface, and a lighting element, wherein the lighting element is located within the housing, and wherein the lighting element is configured to provide lighting within the housing.

Abstract: In some embodiments, apparatuses, systems, and methods are provided herein useful to assessing products. In some embodiments, an enclosure for use in assessing products comprises a housing including a door configured to allow placement of a product within the housing, a product holding surface located within the housing allowing pictures to be taken through the product holding surface and configured to support the product, a first image capture device configured to capture an image of the product from a first perspective, a second image capture device configured to capture an image of the product from a second perspective, and wherein the image of the product from the second perspective is captured through the product holding surface, and a lighting element, wherein the lighting element is located within the housing, and wherein the lighting element is configured to provide lighting within the housing.

Abstract: In some embodiments, apparatuses, systems, and methods are provided herein useful to assessing products. In some embodiments, an enclosure for use in assessing products comprises a housing including a door configured to allow placement of a product within the housing, a product holding surface located within the housing allowing pictures to be taken through the product holding surface and configured to support the product, a first image capture device configured to capture an image of the product from a first perspective, a second image capture device configured to capture an image of the product from a second perspective, and wherein the image of the product from the second perspective is captured through the product holding surface, and a lighting element, wherein the lighting element is located within the housing, and wherein the lighting element is configured to provide lighting within the housing.

Abstract: In some embodiments, apparatuses, systems, and methods are provided herein useful to assessing products. In some embodiments, an enclosure for use in assessing products comprises a housing including a door configured to allow placement of a product within the housing, a product holding surface located within the housing allowing pictures to be taken through the product holding surface and configured to support the product, a first image capture device configured to capture an image of the product from a first perspective, a second image capture device configured to capture an image of the product from a second perspective, and wherein the image of the product from the second perspective is captured through the product holding surface, and a lighting element, wherein the lighting element is located within the housing, and wherein the lighting element is configured to provide lighting within the housing.

Abstract: Systems, methods, and computer-readable storage media for object detection and classification, and particularly produce detection and classification. A system configured according to this disclosure can receiving, at a processor, an image of an item. The system can then perform, across multiple pre-trained neural networks, feature detection on the image, resulting in feature maps of the image. These feature maps can be concatenated and combined, then input into an additional neural network for feature detection on the combined feature map, resulting in tiered neural network features. The system then classifies, via the processor, the item based on the tiered neural network features.

Abstract: Examples of the disclosure provide a system and method for entity-specific inventory optimization. An inventory optimization module obtains inventory data for an individual entity at a given time, including variance date codes associated with an individual item, and identifies at least one item of a plurality of items that exceeds an inventory threshold based on the inventory data. An elasticity estimation module receives the identified item from the inventory optimization module, identifies a value response curve for the identified at least one item based at least in part on entity-cluster data, generates an entity-specific elasticity value for the item, and generates an inventory optimization recommendation based on the generated entity-specific elasticity value and the variance date codes of the inventory data.

Abstract: Examples of the disclosure provide a context-specific forecasting device including a consumption analytics module and a data control module. The consumption analytics module receives a data request for a consumption unit, including an attempted data request value associated with an individual environment and corresponding to a given period of time. The consumption analytics module identifies a forecasted consumption value for the consumption unit corresponding to the individual environment and the given period of time. The consumption analytics module determines a threshold factor for the consumption unit corresponding to the individual environment and the given period of time, and generates an allowance threshold for the data request. The data control module then determines whether to adjust the data request based on the generated allowance threshold and the attempted data request value.

Abstract: In some embodiments, methods and systems of forecasting consumer demand for products at fresh food departments of a grocery store include determining an actual past demand for the product by obtaining a total number of the product sold by the fresh food department in one or more weeks preceding a current week, then calculating a seasonality index for the one or more weeks, deseasonalizing the total number of the product sold in the one or more weeks based on the calculated seasonal index to obtain an initial weekly demand forecast for the product during a single week following the current week, and adding a buffer quantity of the product to the initial weekly demand forecast for the product during the single week following the current week to obtain a refined weekly demand forecast for the product for the single week following the current week.