benchmark_shuffled/README.md

# Cambrian-W Benchmark - Shuffled Correct Order

**Version**: 2.0 (Shuffled Correct Order)  
**Date**: 2025-02-28  
**Total Videos**: 466 (40 long + 426 short)  
**Total Eval Items**: 60,687

---

## Critical Fix: Shuffled Temporal Order

**Problem**: In the original format, `subset_concepts` and `correct_order` had the same sequence, allowing models to answer "ABCD" based on position bias rather than true temporal understanding.

**Solution**: Now `correct_order` (temporal sequence) is **randomly shuffled**, while `subset_concepts` maintains fixed label order (A, B, C, D).

### Example

**Before (Flawed)**:
```json
{
  "subset_concepts": ["signpost", "shipping container", "fire hydrant", "tower"],
  "correct_order": ["signpost", "shipping container", "fire hydrant", "tower"],
  "answer": "ABCD"
}
// Model can answer "ABCD" by position bias → CORRECT (undeserved)
```

**After (Fixed)**:
```json
{
  "subset_concepts": ["signpost", "shipping container", "fire hydrant", "tower"],
  "correct_order": ["shipping container", "tower", "signpost", "fire hydrant"],
  "answer": "BDAC"
}
// Model must understand: B→D→A→C, not just output "ABCD"
```

**Label Mapping (Fixed)**:
- A = signpost
- B = shipping container
- C = fire hydrant
- D = tower

**Temporal Sequence (Shuffled)**: shipping container → tower → signpost → fire hydrant

**Answer**: "BDAC" (must map temporal order to fixed labels)

---

## Data Files

| File | Size | Videos | Eval Items | Description |
|------|------|--------|------------|-------------|
| `part1_long_videos_all.json` | 26MB | 40 | 20,705 | Long videos, all tasks, dual format |
| `part2_short_place_motion.json` | 37MB | 426 | 18,154 | Short videos, spatial & motion tasks |
| `part3_short_objects.json` | 14MB | 426 | 21,828 | Short videos, object tasks, dual format |

---

## Data Structure

### Part 1: Long Videos (40 videos, 111.9 hours)

**Source**: `new_long_video/corrected_json_2` (fully rechecked)

**Tasks**:
| Task | Format | Eval Items | Metric |
|------|--------|------------|--------|
| Object Counting | Fill-in-blank | 3,799 | MRA |
| First Appearance Recall | **Choice + Direct** | 1,559 × 2 | Acc / Exact Match |
| Last Appearance Recall | **Choice + Direct** | 1,506 × 2 | Acc / Exact Match |
| Frame Recall (Baseline) | Multiple Choice | 4,770 | Accuracy |
| Frame Recall (Rotated) | Multiple Choice | 4,770 | Accuracy |
| Motion Direction | Multiple Choice | 1,236 | Accuracy |

### Part 2: Short Videos - Place & Motion (426 videos)

**Tasks**:
| Task | Format | Eval Items | Metric |
|------|--------|------------|--------|
| Frame Recall (Baseline) | Multiple Choice | 8,040 | Accuracy |
| Frame Recall (Rotated) | Multiple Choice | 8,040 | Accuracy |
| Motion Direction | Multiple Choice | 2,074 | Accuracy |

### Part 3: Short Videos - Objects (426 videos)

**⚠️ Note**: Partially rechecked

**Tasks**:
| Task | Format | Eval Items | Metric |
|------|--------|------------|--------|
| Object Counting | Fill-in-blank | 7,396 | MRA |
| First Appearance Recall | **Choice + Direct** | 3,608 × 2 | Acc / Exact Match |
| Last Appearance Recall | **Choice + Direct** | 3,608 × 2 | Acc / Exact Match |

---

## Appearance Task Format Details

### Direct Order Format (`*_direct`)

```json
{
  "task_type": "first_appearance_recall_direct",
  "question_format": "direct_order",
  "evaluation_metric": "exact_match",
  "subset_concepts": ["signpost", "shipping container", "fire hydrant", "tower"],
  "correct_order": ["shipping container", "tower", "signpost", "fire hydrant"],
  "original_order": ["signpost", "shipping container", "fire hydrant", "tower"],
  "shuffled": true,
  "question": "[Direct-Order] Arrange by first appearance: signpost, shipping container, fire hydrant, tower",
  "checkpoints": [{
    "checkpoint": 10,
    "answer": "BDAC",
    "concepts_seen": ["shipping container", "tower", "signpost", "fire hydrant"]
  }]
}
```

**Label Mapping**:
- A → signpost
- B → shipping container
- C → fire hydrant
- D → tower

**Temporal Order**: B → D → A → C

**Prompt Template**:
```
Arrange objects by first appearance:

Objects:
A) signpost
B) shipping container
C) fire hydrant
D) tower

Output the order as a sequence of 4 letters.
Answer with only the 4 letters in order, nothing else.
```

**Expected Answer**: "BDAC"

**Why this prevents bias**: Model cannot simply output "ABCD". It must understand that "shipping container" (B) appears first, then "tower" (D), etc.

### Choice Format (`*_choice`)

```json
{
  "task_type": "first_appearance_recall_choice",
  "question_format": "single_choice",
  "evaluation_metric": "accuracy",
  "subset_concepts": ["signpost", "shipping container", "fire hydrant", "tower"],
  "question": "[Single-Choice] Arrange by first appearance: signpost, shipping container, fire hydrant, tower",
  "checkpoints": [{
    "options": [
      "A) signpost → shipping container → fire hydrant → tower",
      "B) shipping container → tower → signpost → fire hydrant",
      "C) fire hydrant → signpost → tower → shipping container",
      "D) tower → fire hydrant → shipping container → signpost"
    ],
    "answer": "B"
  }]
}
```

**Correct Option**: "B) shipping container → tower → signpost → fire hydrant"

---

## Evaluation Guide

### Loading Data

```python
import json

# Load Part 1 (long videos)
with open('benchmark_shuffled/part1_long_videos_all.json') as f:
    part1 = json.load(f)

# Access a video
video = part1['videos'][0]
task = video['tasks'][0]  # First task

if 'first_appearance_recall_direct' in task['task_type']:
    # Direct order format
    concepts = task['subset_concepts']  # Fixed order
    label_map = {c: chr(ord('A')+i) for i, c in enumerate(concepts)}
    
    for cp in task['checkpoints']:
        if cp.get('answer'):
            correct_order = cp['correct_order']  # Shuffled temporal order
            answer = cp['answer']  # Sequence like "BDAC"
            
            # Verify
            expected = ''.join([label_map[c] for c in correct_order])
            assert answer == expected, "Answer mismatch!"
```

### Direct Format Evaluation

```python
def evaluate_direct(model, task, video_path=None):
    """Evaluate direct order format task."""
    results = []
    
    concepts = task['subset_concepts']  # Fixed order
    
    # Build concept list for prompt
    concept_list = '\n'.join([f"{chr(ord('A')+i)}) {c}" for i, c in enumerate(concepts)])
    n = len(concepts)
    
    for cp in task['checkpoints']:
        if cp.get('answer') is None:
            continue
        
        # Build prompt
        prompt = f"""{task['question']}

Objects:
{concept_list}

Output the order as a sequence of {n} letters.
Answer with only the {n} letters in order, nothing else."""
        
        # Get prediction
        pred = model.generate(prompt, video_path)
        pred_seq = parse_sequence(pred)
        
        # Check correctness (exact match)
        is_correct = (pred_seq == cp['answer'])
        
        results.append({
            'gt': cp['answer'],
            'pred': pred_seq,
            'correct': is_correct
        })
    
    return results


def parse_sequence(text):
    """Parse sequence like 'BDAC' from model output."""
    import re
    text = text.upper()
    text = text.replace('→', '').replace('->', '').replace(' ', '').replace(',', '')
    match = re.search(r'[ABCD]{2,4}', text)
    if match:
        return match.group(0)
    return ''
```

### Choice Format Evaluation

```python
def evaluate_choice(model, task, video_path=None):
    """Evaluate choice format task."""
    results = []
    
    for cp in task['checkpoints']:
        if cp.get('answer') is None:
            continue
        
        # Build prompt
        prompt = f"""{task['question']}

Options:
{chr(10).join(cp['options'])}

Answer with only the letter (A, B, C, or D)."""
        
        # Get prediction
        pred = model.generate(prompt, video_path)
        pred_letter = extract_letter(pred)
        
        # Check correctness
        is_correct = (pred_letter == cp['answer'])
        
        results.append({
            'gt': cp['answer'],
            'pred': pred_letter,
            'correct': is_correct
        })
    
    return results


def extract_letter(text):
    """Extract A/B/C/D from model output."""
    text = text.upper()
    for letter in ['A', 'B', 'C', 'D']:
        if letter in text:
            return letter
    return 'A'
```

### Object Counting Evaluation

```python
def evaluate_counting(model, task, video_path=None):
    """Evaluate object counting task."""
    results = []
    
    for cp in task['checkpoints']:
        if cp.get('answer') is None:
            continue
        
        prompt = f"""{task['question']}

You are at checkpoint {cp['checkpoint']} of {cp['checkpoint_of']}.

Answer with only a single integer number."""
        
        pred = model.generate(prompt, video_path)
        nums = re.findall(r'\d+', pred)
        pred_val = int(nums[0]) if nums else 0
        gt_val = cp['answer']
        
        # MRA metric
        mra = max(0.0, 1.0 - abs(pred_val - gt_val) / max(gt_val, 1))
        
        results.append({
            'gt': gt_val,
            'pred': pred_val,
            'mra': mra
        })
    
    return results
```

---

## Metrics Calculation

### Accuracy (Choice tasks)
```python
accuracy = sum(r['correct'] for r in results) / len(results) * 100
```

### Exact Match (Direct tasks)
```python
exact_match = sum(r['correct'] for r in results) / len(results) * 100
```

### MRA (Counting tasks)
```python
mra = sum(r['mra'] for r in results) / len(results) * 100
```

---

## Dual Format Comparison

Run both formats on same checkpoints:

```python
# Load both formats
choice_task = load_task('first_appearance_recall_choice')
direct_task = load_task('first_appearance_recall_direct')

# Evaluate
choice_results = evaluate_choice(model, choice_task, video_path)
direct_results = evaluate_direct(model, direct_task, video_path)

# Compare
choice_acc = sum(r['correct'] for r in choice_results) / len(choice_results)
direct_acc = sum(r['correct'] for r in direct_results) / len(direct_results)

print(f"Choice: {choice_acc:.2%}")
print(f"Direct: {direct_acc:.2%}")
print(f"Gap: {choice_acc - direct_acc:.2%}")
```

**Expected Gap**:
- First Appearance: ~40% (60% → 20%)
- Last Appearance: ~44% (55% → 11%)

Large gap → model relies on bias/heuristics  
Small gap → model truly understands temporal order

---

## Data Quality

| Part | Quality | Recommendation |
|------|---------|----------------|
| Part 1 | ✅ Fully rechecked | Publication-ready |
| Part 2 | ✅ Standard | Publication-ready |
| Part 3 | ⚠️ Partially rechecked | Development only |

---

## Migration to Other Clusters

### 1. Transfer Package
```bash
tar -czf benchmark_shuffled.tar.gz benchmark_shuffled/
scp benchmark_shuffled.tar.gz user@cluster:/data/benchmarks/
ssh user@cluster "cd /data/benchmarks/ && tar -xzf benchmark_shuffled.tar.gz"
```

### 2. Install Dependencies
```bash
pip install transformers>=4.40.0 torch>=2.0.0 qwen-vl-utils
```

### 3. Run Evaluation
```bash
python your_eval_script.py --data benchmark_shuffled/part1_long_videos_all.json
```

---

## Citation

```bibtex
@dataset{cambrian_w_2025,
  title={Cambrian-W: Panoramic Video Understanding Benchmark},
  year={2025},
  note={Shuffled Correct Order, Dual Format Version}
}
```

---

## File Manifest

```
benchmark_shuffled/
├── README.md                    # This file
├── part1_long_videos_all.json   # 40 videos
├── part2_short_place_motion.json # 426 videos
└── part3_short_objects.json     # 426 videos
```

---

## Key Points for Implementation

1. **Fixed Labels**: `subset_concepts` order determines A, B, C, D labels
2. **Shuffled Order**: `correct_order` is temporal sequence (randomized)
3. **Answer Mapping**: Answer is sequence of labels based on `correct_order`
4. **No Position Bias**: Model cannot simply output "ABCD"

**Example Verification**:
```python
concepts = ['signpost', 'shipping container', 'fire hydrant', 'tower']
# Labels: A=signpost, B=shipping container, C=fire hydrant, D=tower

correct_order = ['shipping container', 'tower', 'signpost', 'fire hydrant']
# Temporal: B → D → A → C

answer = 'BDAC'
# Expected answer

# Verify
label_map = {c: chr(ord('A')+i) for i, c in enumerate(concepts)}
expected = ''.join([label_map[c] for c in correct_order])
assert answer == expected  # 'BDAC' == 'BDAC' ✓
```

---

**Ready for cluster deployment! 🚀**