Mask_RCNN_Test | Mask R-CNN on Python

 by   Jakaria08 Python Version: Current License: Non-SPDX

kandi X-RAY | Mask_RCNN_Test Summary

kandi X-RAY | Mask_RCNN_Test Summary

Mask_RCNN_Test is a Python library. Mask_RCNN_Test has no bugs, it has no vulnerabilities, it has build file available and it has low support. However Mask_RCNN_Test has a Non-SPDX License. You can download it from GitHub.

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.
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              Mask_RCNN_Test has a low active ecosystem.
              It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.
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              It had no major release in the last 6 months.
              Mask_RCNN_Test has no issues reported. There are no pull requests.
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              The latest version of Mask_RCNN_Test is current.

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              Mask_RCNN_Test has no bugs reported.

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              Mask_RCNN_Test has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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              Mask_RCNN_Test has a Non-SPDX License.
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              Mask_RCNN_Test releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.

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            Mask_RCNN_Test Examples and Code Snippets

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            Install Mask_RCNN_Test

            demo.ipynb Is the easiest way to start. It shows an example of using a model pre-trained on MS COCO to segment objects in your own images. It includes code to run object detection and instance segmentation on arbitrary images. train_shapes.ipynb shows how to train Mask R-CNN on your own dataset. This notebook introduces a toy dataset (Shapes) to demonstrate training on a new dataset. (model.py, utils.py, config.py): These files contain the main Mask RCNN implementation. inspect_data.ipynb. This notebook visualizes the different pre-processing steps to prepare the training data. inspect_model.ipynb This notebook goes in depth into the steps performed to detect and segment objects. It provides visualizations of every step of the pipeline. inspect_weights.ipynb This notebooks inspects the weights of a trained model and looks for anomalies and odd patterns.
            demo.ipynb Is the easiest way to start. It shows an example of using a model pre-trained on MS COCO to segment objects in your own images. It includes code to run object detection and instance segmentation on arbitrary images.
            train_shapes.ipynb shows how to train Mask R-CNN on your own dataset. This notebook introduces a toy dataset (Shapes) to demonstrate training on a new dataset.
            (model.py, utils.py, config.py): These files contain the main Mask RCNN implementation.
            inspect_data.ipynb. This notebook visualizes the different pre-processing steps to prepare the training data.
            inspect_model.ipynb This notebook goes in depth into the steps performed to detect and segment objects. It provides visualizations of every step of the pipeline.
            inspect_weights.ipynb This notebooks inspects the weights of a trained model and looks for anomalies and odd patterns.
            Run setup from the repository root directory. Download pre-trained COCO weights (mask_rcnn_coco.h5) from the releases page. (Optional) To train or test on MS COCO install pycocotools from one of these repos. They are forks of the original pycocotools with fixes for Python3 and Windows (the official repo doesn't seem to be active anymore).
            Clone this repository
            Install dependencies pip3 install -r requirements.txt
            Run setup from the repository root directory python3 setup.py install
            Download pre-trained COCO weights (mask_rcnn_coco.h5) from the releases page.
            (Optional) To train or test on MS COCO install pycocotools from one of these repos. They are forks of the original pycocotools with fixes for Python3 and Windows (the official repo doesn't seem to be active anymore). Linux: https://github.com/waleedka/coco Windows: https://github.com/philferriere/cocoapi. You must have the Visual C++ 2015 build tools on your path (see the repo for additional details)

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