Contextual-Inference-V2 | Contextual Inference version

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.
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).
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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)