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: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: receiving historical delivery records over a predetermined time period from partners associated with items offered to subregions through an online platform; generating nodes for combinations each comprising a respective one of the partners, a respective one of the items offered by the partners, and a respective one of the subregions; generating, using a machine learning model, a respective classification for each respective node on whether to tag the each respective node as deliverable in a predetermined time window; and automatically tagging a portion of the nodes as deliverable in the predetermined time window in the online platform. Other embodiments are disclosed.

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.