Abstract: One example method provided herein comprises: receiving an input image from a client device, the input image comprising a view of one or move products, wherein the input image comprises a plurality of pixels; generating, using a trained machine learning model, a bounding box for each of the one or more products, respectively, each bounding box comprising a subset of the plurality of pixels, wherein each bounding box indicates a particular product of the one or more products; generating a segmentation M mask for the pixels within each of the bounding boxes; generating, using each segmentation mask, an isolated image of each product indicated by one of the bounding boxes, wherein each isolated image comprises substantially only a set of pixels representing the indicated product; generating, using each isolated image of each product, a classification of each of the one or more products; and displaying information related to the generated classifications.

Abstract: In one embodiment, the disclosure provides a computer-implemented method comprising: receiving a first labeled training data set comprising a first plurality of images each associated with a set of labels; programmatically training a machine learning neural Teacher model on the first labeled training data set; programmatically applying a machine learning model trained for NLP to an unlabeled data set comprising digital electronic representations of natural language text summaries of a second plurality of images, thereby generating a second labeled training data set comprising the second plurality of images; using the machine learning neural Teacher model, programmatically generating soft pseudo labels; programmatically generating derived labels using the soft pseudo labels; training one or more programmed machine learning neural Student models using the derived labels; receiving a target image; and applying an ensemble of one or more of the Student models to output one or more classifications of the target im

Abstract: Systems, methods, and apparatus are disclosed for analyzing an input image that includes a view of fecal matter. One example method includes: receiving an input image from a client device; determining that the input image comprises a view of fecal matter excreted by an animal; processing at least a portion of the input image comprising the view of the fecal matter using one or more machine learning models to generate a classification of the fecal matter or a health assessment of the animal; generating a recommendation for the animal based on the classification of the fecal matter or the health assessment of the animal; and displaying information related to the recommendation for the animal to a user. Some embodiments involve outputting confidence scores associated with one or more of the other outputs. Some embodiments implement Client-Server architecture and follow a Software as a Service (SaaS) model.