Are evaluations just for Text-text, or is there an efficient ways to evaluate image-text, like for MobileClip2, or YOLOE? At first blush, it seems like that would just be redoing training, testing, and validation of the dataset, or am I missing something? (newbe)

Can Apple Watch please add support for cycling VO2max algorithm and tracking? I sheared my meniscus trail running, ending my running career, and I’m solely cycling for cardio now. I’ve now had to adopt a xxxxxx watch which supports cycling VO2max calc and tracking, and I miss my Apple Watch. Enabling BT pairing to a power meter would provide HR-Power-Cadence (or with a head unit synched to a pm ++ Speed and VAM)

I'm trying to track identified objects in realtime with bounding rects, with no 3D integration, but still has poor update performance. I'm trying to understand how to optimize frame updates. (I'm a new programmer) Using: Foundation, AVFoundation, ARKit, CoreVideo, Vision, CoreImage, CoreVideo with YOLOE-11s object detection currently throttled to 2fps. (target iOS, testing on 16pro) YOLOE-11S CoreML model detects objects with class labels + bounding boxes Labels are matched against ObjectCatalog.json for relevance Matched objects are promoted from blue (detected) to green (identified) Log warnings: ARSession <0x110afdb80>: The delegate of ARSession is retaining 13 ARFrames. The camera will stop delivering camera images if the delegate keeps holding on to too many ARFrames. This could be a threading or memory management issue in the delegate and should be fixed. Skipping integration due to poor slam at time: 619447.208339 vio_initialized(1) map_size(0) tracking_state_is_nominal(0) is_3dof(0) reinitialize_attempts(6) slam_mode(RegularSLAM)