Upload pretrained checkpoint, adaptation plan and model card

README.md

@@ -15,7 +15,7 @@ tags:
 
 [![MICCAI 2025](https://img.shields.io/badge/MICCAI-2025-1f6feb.svg)](https://doi.org/10.1007/978-3-032-04965-0_58)
 
-`ResEncL-MissingPiece-MultiTalent` is a ResEnc-L backbone for **3D medical object detection**, pre-trained with **MultiTalent** supervised multi-dataset segmentation on ~87 public segmentation datasets of mixed modalities.
+`ResEncL-MissingPiece-MultiTalent` is a ResEnc-L backbone for **3D medical object detection**, pre-trained with **MultiTalent** supervised multi-dataset segmentation on 65 public segmentation datasets of mixed modalities.
 It is one of the checkpoints released with *[The Missing Piece: A Case for Pre-training in 3D Medical Object Detection](https://doi.org/10.1007/978-3-032-04965-0_58)* (MICCAI 2025).
 
 ## Model family
@@ -64,7 +64,7 @@ Downstream patch size: **128 × 128 × 128**.
 
 For reference, pre-training used Z-score normalised volumes resampled to 1 × 1 × 1 mm, with a patch size of 192 × 192 × 192.
 
-The checkpoint contains **87 per-dataset input stems** (one per pre-training dataset/modality). The adaptation plan selects the CT stem (`encoder.stem.351_0`) by default; to use a different one, pass `-o model_cfg.stem_override=encoder.stem.<id>` (see [§3.4 of the finetuning docs](https://github.com/MIC-DKFZ/nnDetection/blob/main/docs/finetuning.md)).
+The checkpoint contains **87 input stems**, one per pre-training (sub-)dataset (some of the 65 datasets were split into several stems). The adaptation plan selects the CT stem (`encoder.stem.351_0`) by default; to use a different one, pass `-o model_cfg.stem_override=encoder.stem.<id>` (see [§3.4 of the finetuning docs](https://github.com/MIC-DKFZ/nnDetection/blob/main/docs/finetuning.md)).
 
 ## Checkpoint format