You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Right now the pre-trained MMDetection models are performing quite poorly for tracking, and the built in config files need to be fixed, but if you train your own detection model and write your own config, they work. All you have to do is call `tracker = init_track_model(TRACKER_CONFIG, device=device)' and then for each frame call:
@rolson24 is there chance tracker can be empty ? If yes we should do same thing we did for mask as well.
I tested it and it seems like if there are no tracks, then pred_track_instances won't exist, so it will just use the pred_instances which are the detection results. But it probably wouldn't hurt to add it especially if there is a tracker that implements pred_track_instances slightly differently.
Thanks for working on this and the related issue #1381! Since we're deprecating the tracker module in supervision (see #2215) in favor of the new standalone trackers package, tracking integrations like this are best continued there. The from_mmdetection Detections support part may still be relevant separately — feel free to open a focused PR for that if needed. Thanks for your contribution! 🙏
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Description
Feature request from #1381
Add support for MMDetection trackers by modifying
from_mmdetectionto support importing MMDetectionTrackDataSamplesinto supvervision Detections. This also allows for easy integration with the [MASA tracker] (https://github.com/siyuanliii/masa/tree/1d5d42cdd75276c9a8e918639907916f75a761da)Type of change
How has this change been tested, please provide a testcase or example of how you tested the change?
Tested with this colab notebook
Any specific deployment considerations
Right now the pre-trained MMDetection models are performing quite poorly for tracking, and the built in config files need to be fixed, but if you train your own detection model and write your own config, they work. All you have to do is call `tracker = init_track_model(TRACKER_CONFIG, device=device)' and then for each frame call: