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The application is detecting the presence of a netted bag in an image. The image can contain fruit and vegetables, either with or without a netted bag around them, or below them (no constraints about placement of the netted bag). The question is: would you rather use a detector algorithm (YoloVx) or rather a classification model: 2 classes - netted fruit vs not-netted fruit. I'm leaning towards the first, but would like your input.

The images are very similar to the one below: netted bag fruit vegetables

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Using a detector algorithm like YoloVx would likely be more effective in this situation. A detector can locate and identify objects within an image, which is important since the netted bag can appear in various positions around or below the fruits and vegetables. YoloVx can precisely locate the netted bags and classify them as well. On the other hand, a classification model with two classes might struggle to handle the different positions and orientations of the netted bags in relation to the fruits and vegetables. So, a detector algorithm like YoloVx would be a better choice to accurately detect and identify the presence of netted bags in the images.

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  • $\begingroup$ Thanks for your answer. And what do you think about using a multi-label classificator? So making it end-to-end, basically. $\endgroup$ Aug 21, 2023 at 14:53

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