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The model's precision is slightly low but as reflected by a 0.8 recall, the model can identify if what it "sees" is a trained grocery item. It also bounds objects well (0.06 General Intersection Over Union (GIOU) ).
The Accuracy of Computer Vision Model
The accuracy concerns identifying not only what an item is but also the quantity of an item.
The model counted items in a set of validation and testing images after training in more than 200 epochs. A qualitative investigation of counting was done by manually checking the model’s output counts for several shelf images.
In large scale, well-angled, and high-resolution photos like the one to the left, the model counts the items correctly. In the depth-involved photos like the shelf of cokes, users can adjust the model output, which is single-layered, to account for the depth.
The Speed of Computer Vision Model
A quick speed ensures efficiency and user experience with the application.
Python’s timeit module, which measures the execution time of a code snippet, is implemented when the model runs on 60 images of common grocery store items in different quantities.
The model is not only fast enough to average less than 0.8 seconds but also consistent in this performance.
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