基于MASK-RCNN框架训练自己的数据与任务.zip

# Color Splash 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. [This blog post](https://engineering.matterport.com/splash-of-color-instance-segmentation-with-mask-r-cnn-and-tensorflow-7c761e238b46) describes this sample in more detail.  ## 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.zip`. 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.