pytorch-mask-rcnn
pytorch-mask-rcnn
这是的Pytorch实现，大部分基于Matterport的 。 Matterport的存储库是在Keras和TensorFlow上的实现。 自述文件的以下部分摘自《 Matterport自述文件》。 有关要求的详细信息，有关此存储库的MS COCO培训和检测结果的信息，请参见文档末尾。
Mask R-CNN模型为图像中对象的每个实例生成边界框和分割蒙版。 它基于功能金字塔网络（FPN）和ResNet101主干网。
接下来的四幅图像显示了检测管道中的不同阶段：
1.锚点排序和过滤
区域提议网络提议可能属于某个对象的边界框。 可以看到正锚和负锚以及锚框的细化。
2.边界框优化
这是最终检测框（虚线）的示例，并在第二阶段对其进行了改进（实线）。
3.遮罩生成
生成的蒙版示例。 然后将它们缩放并放置在正确位置的图像上。
4.将不同的部分组合成最终结果
要求
文件下载
资源详情
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