update README w/ links

README.md

@@ -43,10 +43,27 @@ configs:
 
 ## Dataset Description
 
-VANTAGE-BENCH is the first public benchmark purpose-built for evaluating visual understanding on video captured by fixed infrastructure cameras. It spans three real-world domains — warehouse, smart city / Intelligent Transportation Systems (ITS), and smart spaces — across six spatio-temporal video understanding tasks including video question answering (VQA), temporal grounding, dense video captioning, event verification, spatial grounding, and spatio-temporal tracking.
+VANTAGE-BENCH is the first public benchmark purpose-built for evaluating visual understanding on video captured by fixed infrastructure cameras. It spans three real-world domains — warehouse, smart city / Intelligent Transportation Systems (ITS), and smart spaces — across 8 tasks spanning semantic, temporal, spatial, and spatio-temporal evaluation, including video question answering (VQA), temporal grounding, dense video captioning, event verification, spatial grounding, and spatio-temporal tracking.
 
 This dataset is for evaluation purposes only.
 
+## Quick Links
+
+- **Official Website:** https://vantage-bench.org/
+- **Official Leaderboard:** https://huggingface.co/spaces/clemson-computing/VANTAGE-Bench-Leaderboard
+- **Prepare LMUData for VLMEvalKit:**  
+  To prepare an inference-ready, no-ground-truth LMUData layout for running VANTAGE-Bench with VLMEvalKit:
+
+  ```bash
+  python scripts/run_lmudata.py --all
+  ```
+
+  Full setup instructions, disk requirements, troubleshooting, and task-specific notes are available in:
+
+  ```text
+  scripts/RUN_LMUData.md
+  ```
+
 ## Dataset Owner(s)
 
 NVIDIA Corporation
@@ -89,30 +106,38 @@ VANTAGE-BENCH/
 | Category | Task | Metric |
 |----------|------|--------|
 | Semantic | VQA | Accuracy |
-| Semantic | Event Verification | F1 Score |
+| Semantic | Event Verification | Macro F1 |
 | Temporal | Dense Video Captioning | SODA-c |
-| Temporal | Temporal Localization | mAP@tIoU |
+| Temporal | Temporal Localization | mIoU |
 | Spatial | 2D Object Localization | F1@0.5 |
 | Spatial | 2D Referring Expressions | mIoU |
-| Spatial | 2D Spatial Pointing | Pointing Accuracy |
+| Spatial | 2D Spatial Pointing | Accuracy |
 | Spatio-Temporal | Single Object Tracking | AUC |
 
-Expected submission formats and the leaderboard will be published soon.
+Expected submission formats are described in `scripts/RUN_LMUData.md`. Results are submitted to the [official leaderboard](https://huggingface.co/spaces/clemson-computing/VANTAGE-Bench-Leaderboard).
 
 ### Metric Notes
 
 - **Accuracy**: Percentage of correct predictions.
 - **SODA-c**: Metric for dense video captioning quality across event coverage and language quality.
-- **mAP@tIoU**: Mean Average Precision measured over temporal IoU thresholds.
-- **F1 Score**: Harmonic mean of precision and recall.
+- **Macro F1**: Unweighted mean of per-class F1 scores (harmonic mean of precision and recall).
 - **F1@0.5**: F1 score at an IoU threshold of 0.5.
-- **mIoU**: Mean Intersection over Union — average overlap between predicted and ground-truth bounding boxes.
-- **Pointing Accuracy**: Percentage of correctly selected target regions.
+- **mIoU**: Mean Intersection over Union — average overlap between predicted and ground-truth bounding boxes (also used for temporal localization spans).
 - **AUC**: Area under the ROC curve, measuring the model's ability to distinguish correct detections or tracks from incorrect ones across varying confidence thresholds.
 
 ### Evaluation Server
 
-The [VANTAGE-Bench GitHub repository](https://github.com/anon-benchmark/VANTAGE-bench) provides a sample evaluation pipeline for generating model predictions. Predictions are submitted to the official leaderboard, which will go live by the end of May 2026.
+The VANTAGE-Bench evaluation workflow is designed for inference and server-side scoring. Users should first prepare an inference-ready LMUData layout using:
+
+```bash
+python scripts/run_lmudata.py --all
+```
+
+Then run VLMEvalKit inference with `--mode infer`. Generated predictions can be submitted to the official leaderboard:
+
+https://huggingface.co/spaces/clemson-computing/VANTAGE-Bench-Leaderboard
+
+See `scripts/RUN_LMUData.md` for setup, disk requirements, troubleshooting, and task-specific notes.
 
 ## Dataset Format
 
@@ -167,7 +192,9 @@ Video (mp4) and Images (jpg).
 
 ## References
 
-- HuggingFace dataset: [nvidia/PhysicalAI-VANTAGE-Bench](https://huggingface.co/datasets/nvidia/PhysicalAI-VANTAGE-Bench)
+- **Official Website:** https://vantage-bench.org/
+- **Official Leaderboard:** https://huggingface.co/spaces/clemson-computing/VANTAGE-Bench-Leaderboard
+- **Hugging Face Dataset:** https://huggingface.co/datasets/nvidia/PhysicalAI-VANTAGE-Bench
 
 <img src="./assets/vantage_bench_tasks.png" alt="VANTAGE-BENCH task overview across Semantic, Temporal, Spatial, and Spatio-Temporal understanding categories" width="100%">