Simplify Model Card - remove specific metrics, keep essential model information

README.md

@@ -9,41 +9,6 @@ tags:
 - energy-efficient
 - cvpr-2025
 pipeline_tag: object-detection
-widget:
-- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
-  example_title: Object Tracking Example
-datasets:
-- MOT16
-- MOT17
-- DAVIS2017
-- LaSOT
-- GOT-10k
-metrics:
-- accuracy
-- energy-efficiency
-model-index:
-- name: ViStream
-  results:
-  - task:
-      type: object-tracking
-      name: Multiple Object Tracking
-    dataset:
-      type: MOT16
-      name: MOT16
-    metrics:
-    - type: MOTA
-      value: 65.8
-      name: Multiple Object Tracking Accuracy
-  - task:
-      type: object-tracking
-      name: Single Object Tracking
-    dataset:
-      type: LaSOT
-      name: LaSOT
-    metrics:
-    - type: Success
-      value: 58.4
-      name: Success Rate
 ---
 
 # ViStream: Law-of-Charge-Conservation Inspired Spiking Neural Network for Visual Streaming Perception
@@ -122,45 +87,18 @@ For complete usage examples, see the [GitHub repository](https://github.com/Inte
 
 ### Training Data
 
-The model was trained on multiple datasets:
-- **MOT datasets:** MOT16, MOT17 for multiple object tracking
-- **SOT datasets:** LaSOT, GOT-10k for single object tracking  
-- **VOS datasets:** DAVIS2017 for video object segmentation
-- **Pose datasets:** PoseTrack for human pose tracking
+The model was trained on multiple datasets for various visual streaming perception tasks including object tracking, video object segmentation, and pose tracking.
 
 ### Training Procedure
 
-**Training Hyperparameters:**
+**Training Details:**
 - Framework: PyTorch
-- Optimization: Energy-efficient SNN training
-- Architecture: ResNet-based backbone with spike quantization
+- Optimization: Energy-efficient SNN training with Law of Charge Conservation
+- Architecture: ResNet-based backbone with spike quantization layers
 
 ## Evaluation
 
-### Testing Data, Factors & Metrics
-
-**Datasets:**
-- MOT16/17 for multiple object tracking
-- LaSOT, GOT-10k for single object tracking
-- DAVIS2017 for video object segmentation
-
-**Metrics:**
-- **Tracking Accuracy:** MOTA, MOTP, Success Rate
-- **Energy Efficiency:** SOP (Synaptic Operations), Power Consumption
-- **Speed:** FPS, Latency
-
-### Results
-
-| Task | Dataset | Metric | ViStream | ANN Baseline |
-|------|---------|--------|----------|--------------|
-| MOT | MOT16 | MOTA | 65.8% | 66.1% |
-| SOT | LaSOT | Success | 58.4% | 58.7% |
-| VOS | DAVIS17 | J&F | 72.3% | 72.8% |
-
-**Energy Efficiency:**
-- **3.2x** reduction in synaptic operations
-- **2.8x** improvement in energy efficiency
-- Minimal accuracy degradation (<1%)
+The model demonstrates competitive performance across multiple visual streaming perception tasks while achieving significant energy efficiency improvements compared to traditional ANN-based approaches. Detailed evaluation results are available in the [CVPR 2025 paper](https://openaccess.thecvf.com/content/CVPR2025/papers/You_VISTREAM_Improving_Computation_Efficiency_of_Visual_Streaming_Perception_via_Law-of-Charge-Conservation_CVPR_2025_paper.pdf).
 
 ## Model Card Authors