Tags: mapbox/robosat
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v1.2.0 This release brings incredible new features and improvements from the community accumulated over the last months. We recommend to upgrade. The pre-built docker images are the recommended way of using robosat: - https://github.com/mapbox/robosat#installation - https://hub.docker.com/r/mapbox/robosat/ Changes - `rs train`: state of the art losses and metrics. Lovasz loss as default, many many more small features and fixes in training and related tools. Thanks https://github.com/ocourtin - `rs extract`: fully automatated road training dataset creation Thanks https://github.com/DragonEmperorG - `rs extract`: batch feature extraction for datasets too big for memory Thanks http://github.com/daniel-j-h - `rs rasterize`: batch rasterization for datasets too big for memory Thanks http://github.com/daniel-j-h - Infrastructure: improved docker images, pre-trained weights in images, upgrades to CUDA 10.1, cudnn 7, and pytorch 1.1. Thanks http://github.com/daniel-j-h
v1.1.0 Changes - `rs train`: new `--checkpoint` to re-start training (fine-tune) from a trained model checkpoint. Thanks https://github.com/ocourtin - `rs train`: memory usage reduction during validation by disabling expensive gradient computation. Thanks https://github.com/Jesse-jApps - `rs train`, `rs predict`: speedups using multiple workers and doing metric calculation on GPU. Thanks https://github.com/ocourtin - `rs merge`: polygon orientation fixes to respect the GeoJSON specification (right-hand rule). Thanks https://github.com/marsbroshok