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Hello and thank you for making this repo available!
I'm having trouble replicating Figure 4(b) from the paper.
Using notebooks/dense_correspondence/experiments/multi_object/training_multi_object.ipynb as an example, I've trained a model setting the number of dimensions to 2 and the data type probabilities for SINGLE_OBJECT_WITHIN_SCENE and DIFFERENT_OBJECT both to 0.5, as described in the figure summary.
However, when evaluating the model based on the examples provided in notebooks/dense_correspondence/evaluation/evaluation_clusters_2d.ipynb, the feature space for each object appears to overlap even more than without cross-object loss enabled. Do you have any suggestions for how to remedy this?
Here is my training code and the resulting 2d cluster plot:
Hello and thank you for making this repo available!
I'm having trouble replicating Figure 4(b) from the paper.
Using
notebooks/dense_correspondence/experiments/multi_object/training_multi_object.ipynb
as an example, I've trained a model setting the number of dimensions to 2 and the data type probabilities forSINGLE_OBJECT_WITHIN_SCENE
andDIFFERENT_OBJECT
both to 0.5, as described in the figure summary.However, when evaluating the model based on the examples provided in
notebooks/dense_correspondence/evaluation/evaluation_clusters_2d.ipynb
, the feature space for each object appears to overlap even more than without cross-object loss enabled. Do you have any suggestions for how to remedy this?Here is my training code and the resulting 2d cluster plot:
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