README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: data.jsonl
task_categories:
  - visual-question-answering
  - video-classification
language:
  - en
size_categories:
  - n<1K

CameraBench Binary Evaluation Dataset

A balanced VQA dataset for evaluating camera motion understanding in videos.

📊 Dataset Statistics

• Total Questions: 384
• Unique Videos: 119
• Unique Questions: 31
• Yes Answers: 192 (50.0%)
• No Answers: 192 (50.0%)
• Balance Ratio: 1.00
• Total Size: 126.16 MB (0.12 GB)
• Average Video Size: 1.06 MB

🎯 Task Categories

This dataset covers various camera motion tasks including:

• Static: 42 questions
• Move In: 29 questions
• Pan Left: 24 questions
• Tilt Up: 24 questions
• Move Out: 21 questions
• Move Right: 19 questions
• Roll Counterclockwise: 18 questions
• Pan Right: 17 questions
• Zoom Out: 16 questions
• Move Left: 16 questions
• Has Pan Left: 15 questions
• Roll Clockwise: 15 questions
• Zoom In: 14 questions
• Tilt Down: 14 questions
• Is The Fixed Camera Shaking Or Not: 13 questions
• Has Forward Motion: 13 questions
• Has Pan Right: 12 questions
• Is Scene Static Or Not: 11 questions
• Move Up: 11 questions
• Move Down: 11 questions
• Is The Camera Stable Or Shaky: 9 questions
• Has Truck Left: 8 questions
• Has Backward Motion: 7 questions
• Has Truck Right: 6 questions
• Has Forward Vs Backward Ground: 4 questions
• Has Zoom Out Not Move Vs Has Move Not Zoom Out: 2 questions
• Is Camera Movement Slow Or Fast: 2 questions

📝 Dataset Format

The dataset consists of:

• videos/: Directory containing all MP4 video files
• metadata.jsonl: JSONL file with question annotations

Each record in metadata.jsonl contains:

• video_name: Original video filename
• video_path: Relative path to video file (e.g., videos/video.mp4)
• question: Binary question about camera motion
• label: Answer ("Yes" or "No")
• task: Task category
• label_name: Detailed label identifier

🚀 Usage

Loading the Dataset

import json
import os

# Load metadata
metadata = []
with open("metadata.jsonl", "r") as f:
    for line in f:
        metadata.append(json.loads(line))

# Access a sample
sample = metadata[0]
print(f"Question: {sample['question']}")
print(f"Answer: {sample['label']}")
print(f"Task: {sample['task']}")
print(f"Video path: {sample['video_path']}")

Downloading the Dataset

Download the entire dataset using huggingface-cli or git:

# Using huggingface-cli
huggingface-cli download tuhink/cambench_binary_eval --repo-type dataset --local-dir ./cambench_data

# Or using git
git clone https://huggingface.co/datasets/tuhink/cambench_binary_eval

This will download all videos and metadata to your local machine.

Loading Videos

import json
import cv2

# Load metadata
with open("metadata.jsonl", "r") as f:
    metadata = [json.loads(line) for line in f]

# Load a video
sample = metadata[0]
video_path = sample['video_path']  # e.g., "videos/video_name.mp4"

# Use OpenCV to read the video
cap = cv2.VideoCapture(video_path)
while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break
    # Process frame
    pass
cap.release()

Batch Processing

For evaluation tasks:

import json

# Load all questions
with open("metadata.jsonl", "r") as f:
    dataset = [json.loads(line) for line in f]

correct = 0
total = 0

for sample in dataset:
    video_path = sample['video_path']
    question = sample['question']
    ground_truth = sample['label']
    
    # Your model inference here
    # prediction = your_model(video_path, question)
    
    # if prediction == ground_truth:
    #     correct += 1
    # total += 1

# accuracy = correct / total if total > 0 else 0
# print(f"Accuracy: {accuracy:.2%}")

Using with HuggingFace Datasets Library

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("tuhink/cambench_binary_eval")

# Access samples
for sample in dataset['train']:
    print(f"Question: {sample['question']}")
    print(f"Answer: {sample['label']}")
    print(f"Video: {sample['video_path']}")

📊 Evaluation

This dataset is designed for binary classification tasks. Evaluate your model using:

• Accuracy
• Precision/Recall
• F1 Score
• Per-task performance

📄 License

Please refer to the original CameraBench dataset for licensing information.

🙏 Citation

If you use this dataset, please cite the original CameraBench paper.

📧 Contact

For questions or issues, please open an issue on the repository.

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Note: All videos are provided in original MP4 format. The dataset maintains temporal dynamics for accurate camera motion evaluation.