2025年1月23日 · The Python was intended for the premium gun market, and was hand-fitted and polished by the best gunsmiths at Colt. It featured a ventilated rib on the barrel, a fine blued/nickel/stainless finish, a crisp double-action & single-action trigger pull, and high accuracy with a variety of .357 Magnum loads.

2020年6月8日 · 希望通过本文能了解到:general focal loss的背景和原理；代码的实现，如何加在自己的模型中；general focal loss v1- 2020.6.8general focal loss v2- 2020.11.251.背景v1主要解决：大多数目标检测模型中，要同时预测目标置信度scoreobjscore_ {obj}scoreobj 和类别置信度scorei (i=1...n)score_ {i} (i =1...n)scorei (i=1...n)，训练的时候分开单独训_dfloss.

2020年12月26日 · mmdetection 已经实现了GFL，简单的说是继承的onestage，loss改成作者提出的qfl+dfl，正负样本选择继承ATSS。 在ATSS中，作者对基于中心点的FCOS和基于anchor的retinanet进行各种对比试验，发现anchor free和base anchor的本质区别是正负样本的选择，于是提出了一种更为合适的正负样本策略，这里可以参考我前面那一篇博客： ATSS。 算法再一次提升，在这个基础之上PAA又做了改进。 至此ATSS和PAA一套效果已经大幅度超过了FasterRcnn …

在2x + multi-scale的训练模式下，在COCO test-dev上，Resnet50 backbone用GFL一把干到了 43.1 AP，这是一个非常可观的性能。 同时，基于ResNeXt-101-32x4d-DCN backbone，能够有48.2的AP且在2080Ti单GPU上有10FPS的测速，还是相当不错的speed-accuracy trade-off了。

2021年2月1日 · Generalized Focal Loss (GFL) 最后，QFL和DFL其实可以统一地表示为GFL 目标检测中用到的GFL形式为： 3 Experiment 3-1 QFL 3-2 DFL 3-3 GFL 4 附录 4-1 Distributions 作者讨论了Dirac delta, Gaussian, 和提出的 General distributions三种分布，得出下面一些结论

2020年6月8日 · We then propose Generalized Focal Loss (GFL) that generalizes Focal Loss from its discrete form to the continuous version for successful optimization. On COCO test-dev, GFL achieves 45.0\% AP using ResNet-101 backbone, surpassing state-of-the-art SAPD (43.5\%) and ATSS (43.6\%) with higher or comparable inference speed, under the same backbone ...

To call the solver within a Python program, the simplest way is to use the easy.solve_gfl method: There are lots of other configuration options that affect the optimization procedure, but honestly they make little practical difference for most people.

2023年5月17日 · Python code to extract dialogue spoken by a specified character from the hit game Girl's Frontline. Add a description, image, and links to the gfl topic page so that developers can more easily learn about it. To associate your repository with the gfl topic, visit your repo's landing page and select "manage topics."

GFL检测头结构 Comments 主要对网络中 Representation 和 Focal Loss 损失函数进行修改，解决两个问题： 分类分数和检测框分数的预测在训练和测试阶段割裂。 过于绝对的bbox标签不适合现实中一些模糊的边界。 主要的创新点：

GFocalV2 (GFLV2) is a next generation of GFocalV1 (GFLV1), which utilizes the statistics of learned bounding box distributions to guide the reliable localization quality estimation. Again, …