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README.md

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+# TimeBlind Benchmark
+
+TimeBlind: A video VQA benchmark for evaluating temporal understanding in vision-language models.
+
+<p align="center">
+
+[🏠**Home Page**(coming soon)]() | [🤗**HuggingFace**](https://huggingface.co/datasets/BaiqiL/TimeBlind) | [**📖Paper**(coming soon)]() | [🖥️ **Code**](https://github.com/Baiqi-Li/TimeBlind)
+
+</p>
+
+## Setup
+
+```bash
+git clone https://github.com/Baiqi-Li/TimeBlind.git
+cd TimeBlind
+git clone https://huggingface.co/datasets/BaiqiL/TimeBlind
+```
+
+## Data Format
+
+Each sample in `TimeBlind/data.jsonl` contains:
+- `index`: unique sample index (0, 1, 2, ...)
+- `video_path`: path to video file (e.g., `TimeBlind/videos/vid_00000_0.mp4`)
+- `question`: the question
+- `answer`: the ground truth answer
+- `type`: `"yes_no"` or `"multiple_choice"`
+
+## Evaluation
+
+see evaluate.py in our github page for more details!
+
+```python
+import json
+from utils import _load_json_list, build_answers, get_scores, add_question_suffix
+
+data = _load_json_list("TimeBlind/data.jsonl")
+predictions = []
+
+for sample in data:
+    video_path = sample["video_path"]  
+    question = add_question_suffix(sample["question"], sample["type"])
+    
+    # Replace with your model inference
+    model_output = your_model(video_path, question)
+    
+    predictions.append({
+        "index": sample["index"],
+        "video_path": video_path,
+        "question": question,
+        "model_output": model_output,
+    })
+
+json.dump(predictions, open("predictions.json", "w"), indent=2)
+
+answers = build_answers(predictions, data)
+scores = get_scores(answers)
+print(scores)  # {'Q_Acc': ..., 'V_Acc': ..., 'Acc': ..., 'I_Acc': ...}
+```
+
+## Metrics
+I-Acc serves as our primary metric.
+
+- **Acc**: Binary VQA accuracy
+- **Q_Acc**: Question accuracy
+- **V_Acc**: Video accuracy
+- **I_Acc**: **Instance accuracy**
+