Masks regenerated with the full BiRefNet (was BiRefNet_lite)

README.md

@@ -53,13 +53,25 @@ of every exported episode.
 
 ## Masks
 
-`ZhengPeng7/BiRefNet_lite`, run at 1024x1024 and thresholded at 0.5, then resized back to 256. The
+`ZhengPeng7/BiRefNet` (the full model), run at 1024x1024 and thresholded at 0.5, then resized back to 256. The
 resolution matters: below roughly 512 the mask comes back essentially empty, which makes a
 foreground-weighted loss put most of its weight on the wooden table instead of the arm and the objects.
 
 The masks are model output, not ground truth. They are accurate on the arm and the manipulated objects
 and they do pick up small painted markers, but they are not a substitute for simulator segmentation.
 
+
+These masks were regenerated with the **full** BiRefNet, replacing an earlier set from
+`BiRefNet_lite`. Same resolution, same 0.5 threshold, same resize back -- only the checkpoint
+changed. The lite model drops the robot arm entirely on some frames: over the 12,034 frames
+here the full model finds more foreground in 58.2% of them and changes the mask by more than
+2% of the frame in 5.5%, and the worst case (VideoPlaceButton episode_0000 frame 00304) goes
+from a foreground fraction of 0.032 to 0.186 because lite masked the buttons and the cube but
+not the arm holding them. Mean foreground fraction rose from 0.0795 to 0.0845; per task the
+shift ranges from -0.0002 (PatternLock) to +0.0176 (BinFill).
+
+`done.json`'s `foreground_fraction`, `seg_model` and `seg_input_size` were recomputed to match.
+
 ## Provenance and licence
 
 Frames are derived from the RoboMME dataset; its licence governs their use. The masks are generated by