Link the published Rein++ inference and evaluation code

README.md

@@ -16,11 +16,11 @@ tags:
 
 Checkpoints for **Rein++: Efficient Generalization and Adaptation for Semantic Segmentation With Vision Foundation Models** (IEEE TPAMI 2026).
 
-[Paper](https://doi.org/10.1109/TPAMI.2026.3681631) · [Code repository](https://github.com/w1oves/Rein) · [Validation report](VALIDATION.md)
+[Paper](https://doi.org/10.1109/TPAMI.2026.3681631) · [Inference and evaluation code](https://github.com/w1oves/Rein/tree/train/projects/rein%2B%2B) · [Validation report](VALIDATION.md)
 
 ## First checkpoint release
 
-We release the first batch of Rein++ checkpoints for reproducing our paper results. This release includes five Rein-G models based on DINOv3-L and DINOv3-7B, and one Rein-A model, covering experiments in Tables II and VII. All six models have been evaluated on their corresponding validation sets. The remaining checkpoints and training code will be released progressively.
+We release the first batch of Rein++ checkpoints and inference and evaluation code for reproducing our paper results. This release includes five Rein-G models based on DINOv3-L and DINOv3-7B, and one Rein-A model, covering experiments in Tables II and VII. All six models have been evaluated on their corresponding validation sets. The remaining checkpoints and training code will be released progressively.
 
 ## Models
 
@@ -75,7 +75,7 @@ Use the original PyTorch files with these configurations. Base checkpoints are d
 
 ## Inference and evaluation
 
-The code repository is [w1oves/Rein](https://github.com/w1oves/Rein). The validated Rein++ inference and evaluation package will be added under `projects/rein++`; its publication follows this checkpoint release. The commands below use that package.
+Inference and evaluation code is available in [projects/rein++](https://github.com/w1oves/Rein/tree/train/projects/rein%2B%2B). Follow the subproject README for installation and data preparation.
 
 The validation environment uses Linux, Python 3.10, PyTorch 2.1.0 with CUDA 11.8, MMCV 2.1.0, MMEngine 0.10.7, MMSegmentation 1.2.2, and MMDetection 3.3.0.
 

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