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开源软件名称:Mask_RCNN开源软件地址:https://gitee.com/mirrors/Mask_RCNN开源软件介绍:Mask R-CNN for Object Detection and SegmentationThis 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:
The code is documented and designed to be easy to extend. If you use it in your research, please consider citing this repository (bibtex below). 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. Getting Started
Step by Step DetectionTo help with debugging and understanding the model, there are 3 notebooks(inspect_data.ipynb, inspect_model.ipynb,inspect_weights.ipynb) that provide a lot of visualizations and allow running the model step by step to inspect the output at each point. Here are a few examples: 1. Anchor sorting and filteringVisualizes every step of the first stage Region Proposal Network and displays positive and negative anchors along with anchor box refinement. 2. Bounding Box RefinementThis is an example of final detection boxes (dotted lines) and the refinement applied to them (solid lines) in the second stage. 3. Mask GenerationExamples of generated masks. These then get scaled and placed on the image in the right location. 4.Layer activationsOften it's useful to inspect the activations at different layers to look for signs of trouble (all zeros or random noise). 5. Weight HistogramsAnother useful debugging tool is to inspect the weight histograms. These are included in the inspect_weights.ipynb notebook. 6. Logging to TensorBoardTensorBoard is another great debugging and visualization tool. The model is configured to log losses and save weights at the end of every epoch. 6. Composing the different pieces into a final resultTraining on MS COCOWe're providing pre-trained weights for MS COCO to make it easier to start. You canuse those weights as a starting point to train your own variation on the network.Training and evaluation code is in # Train a new model starting from pre-trained COCO weightspython3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=coco# Train a new model starting from ImageNet weightspython3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=imagenet# Continue training a model that you had trained earlierpython3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=/path/to/weights.h5# Continue training the last model you trained. This will find# the last trained weights in the model directory.python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=last You can also run the COCO evaluation code with: # Run COCO evaluation on the last trained modelpython3 samples/coco/coco.py evaluate --dataset=/path/to/coco/ --model=last The training schedule, learning rate, and other parameters should be set in Training on Your Own DatasetStart by reading this blog post about the balloon color splash sample. It covers the process starting from annotating images to training to using the results in a sample application. In summary, to train the model on your own dataset you'll need to extend two classes:
See examples in Differences from the Official PaperThis implementation follows the Mask RCNN paper for the most part, but there are a few cases where we deviated in favor of code simplicity and generalization. These are some of the differences we're aware of. If you encounter other differences, please do let us know.
CitationUse this bibtex to cite this repository: @misc{matterport_maskrcnn_2017, title={Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow}, author={Waleed Abdulla}, year={2017}, publisher={Github}, journal={GitHub repository}, howpublished={\url{https://github.com/matterport/Mask_RCNN}},} ContributingContributions to this repository are welcome. Examples of things you can contribute:
You can also join our team and help us build even more projects like this one. RequirementsPython 3.4, TensorFlow 1.3, Keras 2.0.8 and other common packages listed in MS COCO Requirements:To train or test on MS COCO, you'll also need:
If you use Docker, the code has been verified to work onthis Docker container. Installation
Projects Using this ModelIf you extend this model to other datasets or build projects that use it, we'd love to hear from you. 4K Video Demo by Karol Majek.Images to OSM: Improve OpenStreetMap by adding baseball, soccer, tennis, football, and basketball fields.Splash of Color. A blog post explaining how to train this model from scratch and use it to implement a color splash effect.Segmenting Nuclei in Microscopy Images. Built for the 2018 Data Science BowlCode is in the Detection and Segmentation for Surgery Robots by the NUS Control & Mechatronics Lab.Reconstructing 3D buildings from aerial LiDARA proof of concept project by Esri, in collaboration with Nvidia and Miami-Dade County. Along with a great write up and code by Dmitry Kudinov, Daniel Hedges, and Omar Maher. Usiigaci: Label-free Cell Tracking in Phase Contrast MicroscopyA project from Japan to automatically track cells in a microfluidics platform. Paper is pending, but the source code is released.
Characterization of Arctic Ice-Wedge Polygons in Very High Spatial Resolution Aerial ImageryResearch project to understand the complex processes between degradations in the Arctic and climate change. By Weixing Zhang, Chandi Witharana, Anna Liljedahl, and Mikhail Kanevskiy. Mask-RCNN ShinyA computer vision class project by HU Shiyu to apply the color pop effect on people with beautiful results. Mapping Challenge: Convert satellite imagery to maps for use by humanitarian organisations.GRASS GIS Addon to generate vector masks from geospatial imagery. Based on a Master's thesis by Ondřej Pešek. |
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