I’m wondering if there is a proven way to find the minimum detection box size for a given object detection model. I’m hoping to figure this out so that I can build a dataset with samples that reach this limit but do not pass it.
If there is no such thing as this minimum detection box for an object detection model, I’m guessing that it is probably “created” by including small objects in the training dataset.
Specifically, I’m wondering about the SpaghettiNet large model.
Determining the minimum detection box size for an object detection model like SpaghettiNet 5 large can be quite complex and isn’t a straightforward “one size fits all” answer. It depends on various factors, including:
1. Model Architecture and Training Data:
Model architecture: The specific architecture of SpaghettiNet 5 large and its receptive field (area of the input image the model focuses on) significantly impact the minimum detectable object size. Deeper models with larger receptive fields can detect smaller objects, while shallower models with smaller receptive fields might struggle with them.
Training data: The size and variety of objects in the training dataset influence the model’s ability to detect small objects. If the training data primarily contains large objects, the model might not learn to accurately detect smaller ones.
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2. Object Specifics:
Object size: Obviously, the inherent size of the object itself plays a crucial role. Even with a powerful model and extensive training on small objects, there’s a physical limit to what can be accurately detected based on pixel resolution and inherent details.
Object clarity: Factors like occlusion, blurriness, and low contrast can make even larger objects challenging to detect, potentially requiring a larger minimum detection box size.
3. Detection Thresholds:
Confidence score: You might set a minimum confidence score threshold for object detection. Higher thresholds increase the certainty of detection but might exclude smaller objects with lower confidence scores.
Non-Maximum Suppression (NMS): This technique removes overlapping detections, and its parameters can influence the minimum detectable size. Adjusting NMS thresholds might affect how tightly bounding boxes fit around smaller objects.
Finding the Minimum Detectable Size:
While there’s no guaranteed method to pinpoint the absolute minimum detection box size for SpaghettiNet 5 large, here are some approaches to estimate it:
Analyze the receptive field: Understanding the receptive field size based on the model architecture can provide an initial guess for the minimum detectable object size.
Evaluate the training data: Analyze the size distribution of objects in the training dataset, particularly the smallest objects the model was trained on.
Experiment with test data: Gradually decrease the object size in your test data and observe the model’s detection performance. The point where consistent and accurate detection drops significantly might indicate the minimum size limit.
Benchmarking and comparison: Look for existing benchmarks or comparisons of SpaghettiNet 5 large with other object detection models on datasets with varied object sizes. This can offer insights into its relative performance on small objects.
The minimum detectable size is rarely a sharp cutoff and more like a gradual decline in accuracy as object size decreases. It’s crucial to consider your specific application and desired level of accuracy when defining a suitable minimum size for your dataset.
By considering these factors and employing the suggested approaches, you can gain a better understanding of the minimum detection box size for SpaghettiNet 5 large and tailor your dataset creation accordingly.