Miscellaneous Topics

FileSystems

The FileSystem architecture allows support of multiple filesystems through an interface, that is chosen by URI. We currently support the local file system, AWS S3, and HTTP. Some filesystems support read only (HTTP), while others are read/write.

If you need to support other file storage systems, you can add new FileSystems via the plugin. We’re happy to take contributions on new FileSystem support if it’s generally useful!

Viewing Tensorboard

Backends that utilize TensorFlow will start up an instance of TensorBoard while training. To view Tensorboard, go to https://<domain>:6006/. If you’re running locally, then <domain> should be localhost, and if you are running remotely (for example AWS), <public_dns> is the public DNS of the machine running the training command.

Model Defaults

Model Defaults allow you to use a single key to set attributes into backends, instead of having to explicitly state them for every experiment that you want to use defaults for. This is useful for, say, using a key to refer to the pretrained model weights and hyperparameter configuration of various models. Each Backend can interpret it’s model defaults differently. For more information, see the rastervision/backend/model_defaults.json file in the repository.

You can set the model defaults to use a different JSON file, so that plugin backends can create model defaults or so that you can override the defaults provided by Raster Vision. See the RV Configuration Section for that config value.

TensorFlow Object Detection

This is a list of model defaults for use with the rv.TF_OBJECT_DETECTION backend. They come from the TensorFlow Object Detection project, and more information about what each model is can be found in the Tensorflow Object Detection Model Zoo page. Default includes pretrained model weights and TensorFlow Object Detection pipeline.conf templates for the following models:

  • rv.SSD_MOBILENET_V1_COCO

  • rv.SSD_MOBILENET_V2_COCO

  • rv.SSDLITE_MOBILENET_V2_COCO

  • rv.SSD_INCEPTION_V2_COCO

  • rv.FASTER_RCNN_INCEPTION_V2_COCO

  • rv.FASTER_RCNN_RESNET50_COCO

  • rv.RFCN_RESNET101_COCO

  • rv.FASTER_RCNN_RESNET101_COCO

  • rv.FASTER_RCNN_INCEPTION_RESNET_V2_ATROUS_COCO

  • rv.FASTER_RCNN_NAS

  • rv.MASK_RCNN_INCEPTION_RESNET_V2_ATROUS_COCO

  • rv.MASK_RCNN_INCEPTION_V2_COCO

  • rv.MASK_RCNN_RESNET101_ATROUS_COCO

  • rv.MASK_RCNN_RESNET50_ATROUS_COCO

Keras Classification

This is a list of model defaults for use with the rv.KERAS_CLASSIFICATION backend. Keras Classification only supports one model for now, but more will be added in the future. The pretrained weights come from https://github.com/fchollet/deep-learning-models

  • rv.RESNET50_IMAGENET

Tensorflow DeepLab

This is a list of model defaults for use with the rv.TF_DEEPLAB backend. They come from the TensorFlow DeepLab project, and more information about what each model is can be found in the Tensorflow DeepLab Model Zoo page. Default includes pretrained model weights and backend configurations for the following models:

  • rv.XCEPTION_65

  • rv.MOBILENET_V2

Reusing models trained by Raster Vision

To use a model trained by Raster Vision for transfer learning or fine tuning, you can use output of the TRAIN command of the experiment as a pretrained model of further experiments. The files are listed per backend here:

  • rv.KERAS_CLASSIFICATION: You can use the model_weights.hdf5 file in the train command output as a pretrained model.

  • rv.TF_OBJECT_DETECTION: Use the <experiment_id>.tar.gz that is in the train command output as a pretrained model. The default name of the file is the experiment ID, however you can change the backend configuration to use another name with the .with_fine_tune_checkpoint_name method.

  • rv.TF_DEEPLAB: Use the <experiment_id>.tar.gz that is in the train command output as a pretrained model. The default name of the file is the experiment ID, however you can change the backend configuration to use another name with the .with_fine_tune_checkpoint_name method.