Clarify M2H-MX multi-task outputs

README.md

@@ -1,19 +1,32 @@
 ---
 license: other
-pipeline_tag: image-segmentation
+library_name: pytorch
 tags:
+- multi-task-learning
+- dense-prediction
 - monocular-depth-estimation
 - semantic-segmentation
-- multi-task-learning
+- surface-normal-estimation
+- edge-detection
+- geometric-perception
 - robotics
 - scene-graph
 - dinov3
 ---
 
-# M2H-MX Weights
+# M2H-MX Multi-Task Weights
 
 This repository hosts model-only weights for **M2H-MX: Multi-Task Semantic and Geometric Perception for Real-Time Monocular 3D Scene Graph Construction**.
 
+M2H-MX is a **multi-task dense visual perception model**, not a semantic-segmentation-only model. Given a monocular RGB image, the network can predict:
+
+- metric depth or disparity, depending on the dataset configuration;
+- semantic segmentation logits;
+- surface normals;
+- edge maps.
+
+Depth and semantics are the primary deployment outputs used by Mono-Hydra++ or a compatible mapping backend for metric-semantic mapping and downstream 3D scene graph construction. Surface normals and edges are auxiliary training heads used to improve geometric and semantic consistency. The network improves the dense evidence used by the mapping backend; it does not directly predict the 3D scene graph.
+
 Code and instructions: https://github.com/BavanthaU/m2h_mx
 
 ## Artifacts
@@ -33,7 +46,7 @@ These are model-only state dictionaries. They do not include optimizer, schedule
 From the code repository:
 
 ```bash
-python3 scripts/download_weights.py --repo-id Bavantha11/m2h-mx
+python3 scripts/download_weights.py --repo-id Bavantha11/m2h-mx --verify
 ```
 
 ## Citation