论文标题
用于火车时间模型评估的有效图形友好的可可度量计算
Efficient Graph-Friendly COCO Metric Computation for Train-Time Model Evaluation
论文作者
论文摘要
作为现代深度学习框架的静态计算图的一部分，评估可可平均平均精度（MAP）和可可召回指标会带来一系列独特的挑战。这些挑战包括需要保持动态大小的状态以计算平均平均精度，依赖全局数据集级别统计信息来计算指标，并管理批次中图像之间的界限数量不同。结果，研究人员和从业人员将可可指标评估为培训后评估步骤是普遍的实践。通过使用图形友好的算法来计算可可平均的平均精度和回忆，可以在训练时评估这些指标，从而提高通过训练曲线图对指标演变的可见性，并在原型进行新模型版本时降低迭代时间。 我们的贡献包括平均平均精度的准确近似算法，可可平均平均精度和可可召回的开源实现，广泛的数值基准，以验证我们实现的准确性以及包括平均平均精度和召回的火车时间评估的开源培训循环。
Evaluating the COCO mean average precision (MaP) and COCO recall metrics as part of the static computation graph of modern deep learning frameworks poses a unique set of challenges. These challenges include the need for maintaining a dynamic-sized state to compute mean average precision, reliance on global dataset-level statistics to compute the metrics, and managing differing numbers of bounding boxes between images in a batch. As a consequence, it is common practice for researchers and practitioners to evaluate COCO metrics as a post training evaluation step. With a graph-friendly algorithm to compute COCO Mean Average Precision and recall, these metrics could be evaluated at training time, improving visibility into the evolution of the metrics through training curve plots, and decreasing iteration time when prototyping new model versions. Our contributions include an accurate approximation algorithm for Mean Average Precision, an open source implementation of both COCO mean average precision and COCO recall, extensive numerical benchmarks to verify the accuracy of our implementations, and an open-source training loop that include train-time evaluation of mean average precision and recall.
