Provided pretrained models of STARK in SOT #443
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…ng into model_docs
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@@ Coverage Diff @@
## master #443 +/- ##
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+ Coverage 72.04% 72.46% +0.42%
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Files 121 121
Lines 7054 7054
Branches 1332 1332
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+ Hits 5082 5112 +30
+ Misses 1564 1539 -25
+ Partials 408 403 -5
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GT9505
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Feb 28, 2022
configs/sot/stark/README.md
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| In this paper, we present a new tracking architecture with | ||
| an encoder-decoder transformer as the key component. The | ||
| encoder models the global spatio-temporal feature depen- | ||
| dencies between target objects and search regions, while | ||
| the decoder learns a query embedding to predict the spa- | ||
| tial positions of the target objects. Our method casts object | ||
| tracking as a direct bounding box prediction problem, with- | ||
| out using any proposals or predefined anchors. With the | ||
| encoder-decoder transformer, the prediction of objects just | ||
| uses a simple fully-convolutional network, which estimates | ||
| the corners of objects directly. The whole method is end- | ||
| to-end, does not need any postprocessing steps such as co- | ||
| sine window and bounding box smoothing, thus largely sim- | ||
| plifying existing tracking pipelines. The proposed tracker | ||
| achieves state-of-the-art performance on five challenging | ||
| short-term and long-term benchmarks, while running at | ||
| real-time speed, being 6× faster than Siam R-CNN. | ||
| Code and models are open-sourced at [here](https://github.com/researchmm/Stark). |
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The abstract should be in one line
| @@ -326,8 +326,6 @@ def __init__(self, | |||
| assert isinstance(frozen_modules, list) | |||
| for module in frozen_modules: | |||
| m = getattr(self, module) | |||
| # TODO: Study the influence of eval mode. The official code | |||
| # doesn't freeze BN in `frozen_modules`. | |||
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Do we have a conclusion about this TODO?
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