Datasets:

tuhink
/

cambench_binary_eval

from datasets import load_dataset

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

# Access a sample
sample = dataset['train'][0]
print(f"Question: {sample['question']}")
print(f"Answer: {sample['label']}")
print(f"Task: {sample['task']}")

# The video field contains the path to download
video_file = sample['video']

Downloading Videos

Videos are embedded in the dataset. To download and use them:

from datasets import load_dataset
import os
import shutil

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

# Download all videos to a local directory
output_dir = "downloaded_videos"
os.makedirs(output_dir, exist_ok=True)

for idx, sample in enumerate(dataset['train']):
    video_name = sample['video_name']
    video_data = sample['video']  # This contains the video file data
    
    # Save video to local disk
    local_path = os.path.join(output_dir, video_name)
    
    # If video_data is a file path (during local testing)
    if isinstance(video_data, str) and os.path.exists(video_data):
        shutil.copy2(video_data, local_path)
    else:
        # Video data from HuggingFace - write bytes to file
        with open(local_path, 'wb') as f:
            f.write(video_data)
    
    print(f"Downloaded: {video_name} -> {local_path}")

print(f"All {len(dataset['train'])} videos downloaded to {output_dir}/")

Accessing Individual Videos

To download a specific video by name:

from datasets import load_dataset
import os

dataset = load_dataset("cambench_binary_eval")

# Find and download a specific video
target_video = "your_video_name.mp4"
for sample in dataset['train']:
    if sample['video_name'] == target_video:
        video_data = sample['video']
        
        # Save to current directory
        with open(target_video, 'wb') as f:
            f.write(video_data)
        
        print(f"Downloaded: {target_video}")
        break
else:
    print(f"Video {target_video} not found in dataset")

Batch Processing

For evaluation tasks:

from datasets import load_dataset

dataset = load_dataset("cambench_binary_eval")

correct = 0
total = 0

for sample in dataset['train']:
    video_path = sample['video']
    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%}")

📥 Alternative: Download Full Dataset

To download the entire dataset with all videos at once:

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

# Or using Python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="cambench_binary_eval", repo_type="dataset", local_dir="./cambench_data")

This will download all videos and data files to your local machine.

📊 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.

Note: Videos are provided in MP4 format. Large videos (>50MB) are automatically compressed to ensure smooth downloading and processing. All videos maintain their original temporal dynamics for accurate camera motion evaluation.