Merge pull request #291 from zhouzi180/master

docs(README): announce Scoliosis1K-Pose; fix(sconet): label dtype/device

README.md

@@ -14,6 +14,7 @@ The extension [paper](https://arxiv.org/pdf/2405.09138) has been accepted to TPA
 
 ## What's New
 - **[Sep 2025]** [BiggerGait](https://arxiv.org/pdf/2505.18132) has been accepted to NeurIPS2025🎉 and is available at [here](opengait/modeling/models/BiggerGait_DINOv2.py). [Here are checkpoints](https://huggingface.co/opengait/OpenGait).
+- **[Jun 2025]** [Scoliosis1K-Pose](https://arxiv.org/abs/2509.00872) has been accepted to MICCAI2025🎉. Extends [ScoNet](https://arxiv.org/pdf/2407.05726) by introducing pose annotations and clinical priors for interpretable scoliosis screening. Dataset is available on the [project homepage](https://zhouzi180.github.io/Scoliosis1K/).
 - **[Jun 2025]** [LidarGait++](https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_LidarGait_Learning_Local_Features_and_Size_Awareness_from_LiDAR_Point_CVPR_2025_paper.pdf) has been accepted to CVPR2025🎉 and open-source in [configs/lidargaitv2](./configs/lidargaitv2/README.md).
 - **[Jun 2025]** The extension paper of [OpenGait](https://arxiv.org/pdf/2405.09138), further strengthened by the advancements of [DeepGaitV2](https://github.com/ShiqiYu/OpenGait/blob/master/opengait/modeling/models/deepgaitv2.py), SkeletonGait, and [SkeletonGait++](opengait/modeling/models/skeletongait%2B%2B.py), has been accepted for publication in TPAMI🎉. We sincerely acknowledge the valuable contributions and continuous support from the OpenGait community.
 - **[Feb 2025]** The diffusion-based [DenoisingGait](https://arxiv.org/pdf/2505.18582) has been accepted to CVPR2025🎉 Congratulations to [Dongyang](https://scholar.google.com.hk/citations?user=1xA5KxAAAAAJ)! This is his SECOND paper!

opengait/modeling/models/sconet.py

@@ -20,7 +20,8 @@ class ScoNet(BaseModel):
 
         # Label mapping: negative->0, neutral->1, positive->2 
         label_ids = np.array([{'negative': 0, 'neutral': 1, 'positive': 2}[status] for status in labels])
-
+        label_ids = torch.from_numpy(label_ids).cuda().long()
+        
         sils = ipts[0]
         if len(sils.size()) == 4:
             sils = sils.unsqueeze(1)