How amount of train data influence segmentation metrics in computer vision?
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The amount and quality of training data significantly impact segmentation metrics in computer vision. Studies highlight that differences in image properties like scale, contrast, brightness, and saturation between training and prediction data can negatively affect segmentation results. While fully-supervised learning enables accurate pixel-wise segmentation, acquiring exhaustive datasets for accurate segmentation can be costly. Data augmentation and simulation are used to address the challenge of acquiring labeled data, with augmented real data outperforming simulated abstract data in segmentation tasks. Moreover, training on augmented data has shown to improve segmentation quality, with the quality of segmentation being a key factor in model performance. Therefore, ensuring a sufficient amount of diverse and high-quality training data is crucial for achieving accurate and robust segmentation metrics in computer vision tasks.
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| Papers (5) | Insight |
|---|---|
| Increasing the amount of weakly-labeled images during training enhances segmentation performance in computer vision, as observed in the study on heterogeneous dataset training. | |
Open access•Posted Content | The paper demonstrates stable segmentation performance with decreasing annotation fractions, showcasing robustness to varying amounts of training data in computer vision segmentation tasks. |
| The quality and properties of training data, such as brightness and saturation levels, significantly impact segmentation metrics in computer vision, affecting precision, recall, and F-score. | |
| Training on augmented real data improved segmentation metrics in computer vision for neuron images. Augmented data outperformed simulated data, enhancing segmentation quality, especially for neurites. | |
| Training on augmented real data improved segmentation metrics in neuron images. Quality of neurite segmentation was crucial, with neurites being challenging to learn due to their small representation in images. |
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