Mask R-CNN for Object Detection and Segmentation
这是一个基于 Python 3, Keras, TensorFlow 实现的 Mask R-CNN。这个模型为图像中的每个对象实例生成边界框和分割掩码。它基于 Feature Pyramid Network (FPN) and a ResNet101 backbone.
这个项目包括包括：
在FPN和ResNet101上构建的Mask R-CNN的源代码。
MS COCO的训练代码
MS COCO 预先训练的权重
Jupyter notebooks 来可视化在每一个步骤的检测管道
用于多GPU训练的ParallelModel类
MS COCO指标评估（AP）
训练您自己的数据集的例子
代码被记录和设计为易于扩展。 如果您在研究中使用它，请考虑引用此项目。 如果您从事3D视觉工作，您可能会发现我们最近发布的Matterport3D数据集也很有用。 这个数据集是由我们的客户拍摄的三维重建空间创建的，这些客户同意将这些数据公开供学术使用。 你可以在这里看到更多的例子。
This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It’s based on Feature Pyramid Network (FPN) and a ResNet101 backbone.
The repository includes:
- Source code of Mask R-CNN built on FPN and ResNet101.
- Training code for MS COCO
- Pre-trained weights for MS COCO
- Jupyter notebooks to visualize the detection pipeline at every step
- ParallelModel class for multi-GPU training
- Evaluation on MS COCO metrics (AP)
- Example of training on your own dataset
The code is documented and designed to be easy to extend. If you use it in your research, please consider referencing this repository. If you work on 3D vision, you might find our recently released Matterport3D dataset useful as well. This dataset was created from 3D-reconstructed spaces captured by our customers who agreed to make them publicly available for academic use. You can see more examples here.
项目地址：matterport/Mask_RCNN
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