Added reference to fastai
"\n",
"`data` is the returned data object from prepare_data function. `backbone` is used for creating the base of the UnetClassifier, which is resnet34 by default, while `pretrained_path` points to where pre-trained model is saved.\n",
"\n",
- "The `UnetClassifier` builds a dynamic U-Net from any backbone pretrained on ImageNet, automatically inferring the intermediate sizes. As you might have noticed, U-net has a lot fewer parameters than SSD, this is because all the parameters such as dropout are specified in the encoder and UnetClassifier creates the decoder part using the given encoder. You can tweak everything in the encoder and our U-net module creates decoder equivalent to that.\n",
+ "The `UnetClassifier` builds a dynamic U-Net from any backbone pretrained on ImageNet, automatically inferring the intermediate sizes. As you might have noticed, U-net has a lot fewer parameters than SSD, this is because all the parameters such as dropout are specified in the encoder and UnetClassifier creates the decoder part using the given encoder. You can tweak everything in the encoder and our U-net module creates decoder equivalent to that [2].\n",
"\n",
"For more information about the API, please go to the [API reference](https://esri.github.io/arcgis-python-api/apidoc/html/arcgis.learn.html#unetclassifier)."
]
"metadata": {},
"source": [
"## References\n",
- "- [1] Olaf Ronneberger, Philipp Fischer, Thomas Brox: U-Net: Convolutional Networks for Biomedical Image Segmentation, 2015; <a href='https://arxiv.org/abs/1505.04597'>arXiv:1505.04597</a>."
+ "- [1] Olaf Ronneberger, Philipp Fischer, Thomas Brox: U-Net: Convolutional Networks for Biomedical Image Segmentation, 2015; <a href='https://arxiv.org/abs/1505.04597'>arXiv:1505.04597</a>.\n",
+ "- [2] Howard Jeremy. Fastai - Dynamic U-Net. https://www.youtube.com/watch?v=0frKXR-2PBY. Accessed 2 September 2019."
]
}
],
