Balloon Color Splash sample.

samples/balloon/README.md

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+# Color Spash Example
+
+This is an example showing the use of Mask RCNN in a real application.
+We train the model to detect balloons only, and then we use the generated 
+masks to keep balloons in color while changing the rest of the image to
+grayscale. 
+
+## Installation
+From the [Releases page](https://github.com/matterport/Mask_RCNN/releases) page:
+1. Download `mask_rcnn_balloon.h5`. Save it in the root directory of the repo (the `mask_rcnn` directory).
+2. Download `balloon_dataset.p3`. Expand it such that it's in the path `mask_rcnn/datasets/balloon/`.
+
+## Apply color splash using the provided weights
+Apply splash effect on an image:
+
+```bash
+python3 balloon.py splash --weights=/path/to/mask_rcnn/mask_rcnn_balloon.h5 --image=<file name or URL>
+```
+
+Apply splash effect on a video. Requires OpenCV 3.2+:
+
+```bash
+python3 balloon.py splash --weights=/path/to/mask_rcnn/mask_rcnn_balloon.h5 --video=<file name or URL>
+```
+
+
+## Run Jupyter notebooks
+Open the `inspect_balloon_data.ipynb` or `inspect_balloon_model.ipynb` Jupter notebooks. You can use these notebooks to explore the dataset and run through the detection pipelie step by step.
+
+## Train the Balloon model
+
+Train a new model starting from pre-trained COCO weights
+```
+python3 balloon.py train --dataset=/path/to/balloon/dataset --weights=coco
+```
+
+Resume training a model that you had trained earlier
+```
+python3 balloon.py train --dataset=/path/to/balloon/dataset --weights=last
+```
+
+Train a new model starting from ImageNet weights
+```
+python3 balloon.py train --dataset=/path/to/balloon/dataset --weights=imagenet
+```
+
+The code in `balloon.py` is set to train for 3K steps (30 epochs of 100 steps each), and using a batch size of 2. 
+Update the schedule to fit your needs.