Package: vos-benchmark
Simple Vidoe Object Segmentation BenchmarkingQuick StartInstallationLocally (recommended):gitclonehttps://github.com/hkchengrex/vos-benchmark
pipinstall-evos-benchmarkVia pip (as a library):pip install vos-benchmarkUsing it as a script:(from the vos-benchmark root directory)pythonbenchmark.py-g<pathtoground-truthdirectory>-m<pathtopredictiondirectory>-n<numberofprocesses,16bydefault>Using it as a library:fromvos_benchmark.benchmarkimportbenchmark# both arguments are passed as a list -- multiple datasets can be specifiedbenchmark([pathtoground-truthdirectory],[<pathtopredictiondirectory>])Seebenchmark.pyfor an example, and seevos_benchmark/benchmark.pyfor the function signature with additional options.Aresults.csvwill be saved in the prediction directory.BackgroundI built this tool to accelerate evaluations (J&F) on different video object segmentation benchmarks. Previously,davis2017-evaluationis used, which has several limitations:It is slow. Evaluating predictions on the validation set of DAVIS-2017 (30 videos) takes 73.3 seconds. It would take longer for any larger dataset. Ours takes 5.36 seconds (with 16 threads).It is tailored to the DAVIS dataset. Evaluating on other datasets (like converted OVIS, UVO, or the long video dataset) requires mocking them as DAVIS (setting up "split" text files and following a non-trivial file structure). We don't care. We just take the paths to two folders (ground-truth and predictions) as input.It does not work with non-continuous object IDs. We do.I have tested this script on DAVIS-16/17 and confirmed that it produces identical results as the official evaluation script.Technical Details / TroubleshootingThis benchmarking script is simple and dumb. It does not intelligently resolve input problems. If something does not work, most likely the input is problematic. Garbage in, garbage out. Check your input (see below).
We read the input masks usingImage.openfrom PIL. Paletted png files and grayscale png should both work.We determine the objects in a frame (ground-truth or prediction) by runningnp.unique. If there are any types of antialiasing, blurring, smoothing, etc., that spawn new pixel values, this will not work.From the start of the video, we keep a list of all objects that are seen in either ground-truth or prediction. This is to support datasets where some ground-truth objects appear later in the frame. Predicting objects that are not in the ground-truth harms the final score.By default, we skip the first and the last frame during evaluation. This is in line with standard semi-supervised video object segmentation evaluation protocol as in DAVIS. This can be overridden by specifiying-dor--do_not_skip_first_and_last_frame, or passingskip_first_and_last=False.By default, we don't care if all the videos in the ground-truth folder have corresponding predictions. This is to support datasets that contain videos from different splits (e.g., DAVIS puts train/val splits together) in a single folder. If the prediction only contains videos from the validation set, we would only evaluate those videos. This can be overridden by specifying-sor--strict, or passingstrict=True. In the strict mode, an exception would be thrown if the sets of videos do not match.If a video is being evaluated, all the frames in the ground-truth folder must have corresponding predictions. Predictions that do not have corresponding predictions are simply ignored.Related projects:Official DAVIS 2017 evaluation implementation:https://github.com/davisvideochallenge/davis2017-evaluationBURST benchmark (evaluates HOTA which is not supported here):https://github.com/Ali2500/BURST-benchmarkTrackEval (a powerful tool with more functionalities):https://github.com/JonathonLuiten/TrackEvalMy video object segmentation projects:XMem, latest:https://github.com/hkchengrex/XMemSTCN:https://github.com/hkchengrex/STCNMiVOS:https://github.com/hkchengrex/MiVOS