feat(reid): add Deep OC-SORT adaptive appearance fusion to BoT-SORT

README.md

@@ -7,7 +7,7 @@
 
 </div>
 
-`trackers` gives you clean-room, benchmarked implementations of SORT, ByteTrack, OC-SORT, BoT-SORT, C-BIoU, and McByte — so occlusions, fast motion, and moving cameras stop being your problem to solve from scratch. It speaks `supervision.Detections` natively, slotting into any detector you already use — YOLO, DETR, RT-DETR, or anything else — without glue code. One consistent interface, whether you're a researcher comparing algorithms, an engineer shipping a production pipeline, or a hobbyist building something cool. Requires Python ≥ 3.10.
+`trackers` gives you clean-room, benchmarked implementations of SORT, ByteTrack, OC-SORT, C-BIoU, BoT-SORT, and McByte — so occlusions, fast motion, and moving cameras stop being your problem to solve from scratch. It speaks `supervision.Detections` natively, slotting into any detector you already use — YOLO, DETR, RT-DETR, or anything else — without glue code. One consistent interface, whether you're a researcher comparing algorithms, an engineer shipping a production pipeline, or a hobbyist building something cool. Requires Python ≥ 3.10.
 
 ## Why trackers?
 
@@ -83,21 +83,24 @@ For all CLI options, see the [tracking guide](https://trackers.roboflow.com/deve
 
 ## Algorithms
 
-Each tracker below is a faithful implementation of its original paper's motion and association pipeline; appearance/ReID branches are not included where papers offer them — see each tracker's docs page for the exact scope. Pick the one that fits your scene, or run the benchmark to find out which performs best on your data.
+Each tracker below is a faithful implementation of its original paper's motion and association pipeline; appearance/ReID branches are off by default, though BoT-SORT accepts one through `reid_model` (see the [ReID guide](https://trackers.roboflow.com/latest/guides/reid/)). Each tracker's docs page states the exact scope. Pick the one that fits your scene, or run the benchmark to find out which performs best on your data.
 
-<!-- BENCH-XREF copy-of: [docs/evaluations/results.md](docs/evaluations/results.md) mot17/sportsmot/soccernet/dancetrack-default tables, HOTA column only, all 6 rows (SORT/ByteTrack/OC-SORT/BoT-SORT/C-BIoU/McByte). Also duplicated in [docs/index.md](docs/index.md)'s Algorithms table (all 6 rows). Update results.md first, then mirror both copies. -->
+<!-- BENCH-XREF copy-of: [docs/evaluations/results.md](docs/evaluations/results.md) mot17/sportsmot/soccernet/dancetrack-default tables, HOTA column only, all 7 rows (SORT/ByteTrack/OC-SORT/BoT-SORT/C-BIoU/McByte/BoT-SORT + ReID). Also duplicated in [docs/index.md](docs/index.md)'s Algorithms table (all 6 rows). Update results.md first, then mirror both copies. -->
 
-|                   Algorithm                   |                                   Description                                    | MOT17 HOTA | SportsMOT HOTA | SoccerNet HOTA | DanceTrack HOTA |
-| :-------------------------------------------: | :------------------------------------------------------------------------------: | :--------: | :------------: | :------------: | :-------------: |
-|   [SORT](https://arxiv.org/abs/1602.00763)    |                   Kalman filter + Hungarian matching baseline.                   |    58.4    |      70.8      |      81.6      |      47.2       |
-| [ByteTrack](https://arxiv.org/abs/2110.06864) |         Two-stage association using high and low confidence detections.          |    60.1    |      73.0      |      84.0      |      53.3       |
-|  [OC-SORT](https://arxiv.org/abs/2203.14360)  |                  Observation-centric recovery for lost tracks.                   |    61.9    |      71.7      |      78.4      |      54.1       |
-| [BoT-SORT](https://arxiv.org/abs/2206.14651)  |                           Camera motion compensation.                            |    63.7    |      73.8      |      84.5      |      57.8       |
-|  [C-BIoU](https://arxiv.org/abs/2211.14317)   |           Cascaded buffered IoU matching for fast or irregular motion.           |    63.0    |      73.1      |      82.6      |      56.7       |
-|  [McByte](https://arxiv.org/abs/2506.01373)   | Mask-conditioned tracking — adds propagated SAM/Cutie masks as a matching cue.\* |  **64.1**  |    **76.5**    |    **85.0**    |    **67.2**     |
+|                               Algorithm                               |                                   Description                                    | MOT17 HOTA | SportsMOT HOTA | SoccerNet HOTA | DanceTrack HOTA |
+| :-------------------------------------------------------------------: | :------------------------------------------------------------------------------: | :--------: | :------------: | :------------: | :-------------: |
+|               [SORT](https://arxiv.org/abs/1602.00763)                |                   Kalman filter + Hungarian matching baseline.                   |    58.4    |      70.8      |      81.6      |      47.2       |
+|             [ByteTrack](https://arxiv.org/abs/2110.06864)             |         Two-stage association using high and low confidence detections.          |    60.1    |      73.0      |      84.0      |      53.3       |
+|              [OC-SORT](https://arxiv.org/abs/2203.14360)              |                  Observation-centric recovery for lost tracks.                   |    61.9    |      71.7      |      78.4      |      54.1       |
+|              [C-BIoU](https://arxiv.org/abs/2211.14317)               |           Cascaded buffered IoU matching for fast or irregular motion.           |    63.0    |      73.1      |      82.6      |      56.7       |
+|             [BoT-SORT](https://arxiv.org/abs/2206.14651)              |                           Camera motion compensation.                            |    63.7    |      73.8      |      84.5      |      57.8       |
+| [BoT-SORT + ReID](https://trackers.roboflow.com/develop/guides/reid/) |       Appearance embeddings fused with motion, for harder association.\*\*       |    63.9    |      75.5      |      84.3      |      57.0       |
+|              [McByte](https://arxiv.org/abs/2506.01373)               | Mask-conditioned tracking — adds propagated SAM/Cutie masks as a matching cue.\* |  **64.1**  |    **76.5**    |    **85.0**    |    **67.2**     |
 
 \*McByte needs optional heavyweight deps (`torch`, SAM, Cutie) not installed by default. It tops HOTA on all four benchmarks above — see the [McByte docs](https://trackers.roboflow.com/develop/trackers/mcbyte/) for setup.
 
+\*\*BoT-SORT + ReID is listed at library defaults with the default encoder `fastreid_mot17_sbs50`, BoT-SORT's own ReID model trained on MOT17. At its best tuned configuration on each test set it reaches 64.6, 77.8, 88.4 and 60.8 HOTA, above BoT-SORT on all four. See the [ReID guide](https://trackers.roboflow.com/develop/guides/reid/).
+
 All scores use default parameters on the standard split. Detections come from a YOLOX detector (MOT17, SportsMOT, DanceTrack) or oracle ground-truth boxes (SoccerNet) — absolute numbers shift with detector quality. See the [tracker comparison](https://trackers.roboflow.com/develop/evaluations/results/) for tuned numbers and methodology.
 
 ## Evaluate