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README.md

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+# ST-Evidence Benchmark Dataset
+
+ST-Evidence is a comprehensive benchmark for evaluating Spatial-Temporal Evidence generation in video understanding. It contains two tasks: **Generation (Gen)** and **Multiple Choice Question (MCQ)**.
+
+This was released for research purposes only, in support of the academic paper Evidence-Backed Video Question Answering.
+
+## Dataset Overview
+
+- **Total Videos**: ~1,300 videos at 6fps
+- **Annotations**: Question-Answer pairs with temporal segments and spatial masks
+- **Tasks**: Generation and MCQ
+- **Domains**: Diverse video content
+
+## Files Structure
+
+```
+ST-Evidence/
+├── st_evidence_gen/              # Generation Task
+│   ├── st_evidence_gen.csv       # 924KB - Annotations (entry_id, question, answer, segments, etc.)
+│   ├── videos_6fps.tar.gz        # 8.3GB - Video files at 6fps
+│   └── masks.tar.gz              # 560MB - Ground truth spatial masks
+│
+└── st_evidence_mcq/              # Multiple Choice Question Task
+    ├── st_evidence_mcq.csv       # 313KB - MCQ annotations
+    ├── mask_options.json         # 575KB - Mask options metadata
+    ├── temp_options.json         # 679KB - Temporal options metadata
+    └── options.tar.gz            # 1.5GB - Pre-rendered option masks (1,298 entries)
+```
+
+## Generation Task (st_evidence_gen)
+
+### Data Format
+
+**st_evidence_gen.csv** contains the following columns:
+- `entry_id`: Unique identifier for each question
+- `video_id`: Video identifier
+- `video_path`: Relative path to video file
+- `question`: Question text
+- `candidates`: List of answer options (for reference)
+- `answer`: Ground truth answer
+- `segment`: Temporal evidence segments [[start1, end1], [start2, end2], ...]
+
+### Usage
+
+```python
+import pandas as pd
+import tarfile
+
+# Load annotations
+df = pd.read_csv('st_evidence_gen.csv')
+
+# Extract videos
+with tarfile.open('videos_6fps.tar.gz', 'r:gz') as tar:
+    tar.extractall('videos_6fps/')
+
+# Extract ground truth masks
+with tarfile.open('masks.tar.gz', 'r:gz') as tar:
+    tar.extractall('masks/')
+```
+
+### Evaluation Metrics
+
+- **QA Accuracy**: Percentage of correct answers
+- **Temporal IoU**: Intersection over Union for temporal segments
+  - mIoU, TIoU@0.3, TIoU@0.5
+- **Temporal IoP**: Intersection over Prediction
+  - mIoP, TIoP@0.3, TIoP@0.5
+- **Spatial Quality** (if masks generated):
+  - J score (Jaccard/IoU)
+  - F score (contour-based)
+  - J&F score (average)
+
+## MCQ Task (st_evidence_mcq)
+
+### Data Format
+
+**st_evidence_mcq.csv** contains:
+- `entry_id`: Unique identifier
+- `video_id`: Video identifier
+- `video_path`: Path to video
+- `question`: Question text
+- `candidates`: Answer options
+- `answer`: Correct answer
+- `segment`: Temporal evidence
+- `mask_options`: Reference to mask options
+- `temp_options`: Reference to temporal options
+
+**mask_options.json**: Contains spatial mask options for each question
+**temp_options.json**: Contains temporal segment options for each question
+**options.tar.gz**: Pre-rendered mask visualizations for options (1,298 entries)
+
+### Usage
+
+```python
+import json
+import pandas as pd
+
+# Load MCQ annotations
+df = pd.read_csv('st_evidence_mcq.csv')
+
+# Load options
+with open('mask_options.json', 'r') as f:
+    mask_options = json.load(f)
+
+with open('temp_options.json', 'r') as f:
+    temp_options = json.load(f)
+
+# Extract option masks
+with tarfile.open('options.tar.gz', 'r:gz') as tar:
+    tar.extractall('options/')
+```
+
+### Evaluation Metrics
+
+Same as Generation task, but with multiple-choice format.
+
+## Download & Setup
+
+### Using HuggingFace Hub
+
+```python
+from huggingface_hub import snapshot_download
+
+# Download entire dataset
+snapshot_download(
+    repo_id="Salesforce/ST-Evidence",
+    repo_type="dataset",
+    local_dir="./st_evidence_data"
+)
+```
+
+### Manual Download
+
+1. Download all files from this repository
+2. Extract compressed files:
+   ```bash
+   tar -xzf videos_6fps.tar.gz
+   tar -xzf masks.tar.gz
+   tar -xzf options.tar.gz
+   ```
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@article{st-evidence2025,
+  title={ST-Evidence: A Benchmark for Spatial-Temporal Evidence in Video Understanding},
+  author={Wang, Shijie and others},
+  year={2025}
+}
+```
+
+## License
+
+CC-BY-NC 4.0
+
+
+## Version
+
+- **Version**: 1.0
+- **Release Date**: 2025-03-14
+- **Total Size**: ~10.4 GB (compressed)

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