Comments (1)
The classification scores in DiMP can be smaller than 0 or larger than 1. Since we are not enforcing any limits here but rather compute a hinge like loss between the ground truth score map [0,1] and the predictions. Since we mainly care about localizing the object in the scene (locations > threshold) negative values or values above 1 are not an issue for our task. You can certainly squeeze these score values in the [0,1] interval by clipping or using a sigmoid. Depends what works for you. For the segmentation mask you get probability values for each location in the output. It is up to you to define a classification score based on the raw segmentation values that fits your need.
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Related Issues (20)
- Train on custom dataset HOT 1
- Save video results HOT 1
- Training E.T.Track
- Initialization of tracker in a different code.
- Model of TaMOs HOT 2
- about avist HOT 1
- how to deal with No module named 'prroi_pool' HOT 2
- How to use my training model(.pth.tar file) to track with custom datasets ? HOT 1
- Can tomp process multiple images at once as batches? HOT 2
- Error building extension '_prroi_pooling' HOT 3
- Tamos has many bugs HOT 2
- Update installation for windows
- No matching checkpoint file found HOT 1
- Support for LMDB Dataset HOT 1
- TensorRT Conversion of KeepTrack HOT 2
- Documentation? HOT 1
- About TaoBurst.json HOT 1
- tomp user warning
- TypeError: list indices must be integers or slices, not str HOT 1
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