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I am trying to estimate how many images I need to label for an object detection task. I understand a lot of variables are at play, but I'd like to find some papers that have already explored this further. Specifically it would be helpful to find charts for specific model architectures on specific classes that plot:

Mean Average Precision vs Training Set Size (num instances)

What data exists to help me estimate how much data I need to train an object detection model?

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  • $\begingroup$ Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking. $\endgroup$
    – Anshuman Kumar
    Mar 17 at 5:10

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I found a paper that might be helpful. The paper is titled “Sensitivity Analysis of Dataset Size vs. Model Performance”. It discusses the relationship between training dataset size and model performance, especially for nonlinear models. It also provides an approach to quantifying the relationship between model performance and dataset size for a given model and prediction problem.

I also found another paper. It discusses the relationship between batch size and training time for object detection models. The paper also provides an approach to quantifying the relationship between model performance and dataset size for a given model and prediction problem.

I hope this helps! https://machinelearningmastery.com/sensitivity-analysis-of-dataset-size-vs-model-performance/

https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660477.pdf

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