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--- |
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license: cc-by-4.0 |
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task_categories: |
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- video-classification |
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- visual-question-answering |
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language: |
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- en |
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pretty_name: 'ANAKIN: manipulated videos and mask annotations' |
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size_categories: |
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- 1K<n<10K |
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--- |
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[arxiv](https://arxiv.org/abs/2303.13193) |
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# ANAKIN |
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ANAKIN is a dataset of mANipulated videos and mAsK annotatIoNs. |
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To our best knowledge, ANAKIN is the first real-world dataset of professionally edited video clips, |
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paired with source videos, edit descriptions and binary mask annotations of the edited regions. |
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ANAKIN consists of 1023 videos in total, including 352 edited videos from the |
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[VideoSham](https://github.com/adobe-research/VideoSham-dataset) |
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dataset plus 671 new videos collected from the Vimeo platform. |
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## Data Format |
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| Label | Description | |
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|----------|-------------------------------------------------------------------------------| |
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| video-id | Video ID | |
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|full* | Full length original video | |
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|trimmed | Short clip trimmed from `full` | |
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|edited| Manipulated version of `trimmed`| |
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|masks*| Per-frame binary masks, annotating the manipulation| |
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| start-time* | Trim beginning time (in seconds) | |
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| end-time* | Trim end time (in seconds) | |
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| task | Task given to the video editor | |
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|manipulation-type| One of the 5 manipulation types: splicing, inpainting, swap, audio, frame-level | |
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| editor-id | Editor ID | |
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*There are several subset configurations available. |
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The choice depends on whether you need to download full length videos and/or you only need the videos with masks available. |
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`start-time` and `end-time` will be returned for subset configs with full videos in them. |
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| config | full | masks | train/val/test | |
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| ---------- | ---- | ----- | -------------- | |
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| all | yes | maybe | 681/98/195 | |
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| no-full | no | maybe | 716/102/205 | |
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| has-masks | no | yes | 297/43/85 | |
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| full-masks | yes | yes | 297/43/85 | |
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## Example |
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The data can either be downloaded or [streamed](https://huggingface.co/docs/datasets/stream). |
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### Downloaded |
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```python |
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from datasets import load_dataset |
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from torchvision.io import read_video |
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config = 'no-full' # ['all', 'no-full', 'has-masks', 'full-masks'] |
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dataset = load_dataset("AlexBlck/ANAKIN", config, nproc=8) |
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for sample in dataset['train']: # ['train', 'validation', 'test'] |
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trimmed_video, trimmed_audio, _ = read_video(sample['trimmed'], output_format="TCHW") |
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edited_video, edited_audio, _ = read_video(sample['edited'], output_format="TCHW") |
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masks = sample['masks'] |
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print(sample.keys()) |
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``` |
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### Streamed |
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```python |
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from datasets import load_dataset |
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import cv2 |
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dataset = load_dataset("AlexBlck/ANAKIN", streaming=True) |
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sample = next(iter(dataset['train'])) # ['train', 'validation', 'test'] |
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cap = cv2.VideoCapture(sample['trimmed']) |
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while(cap.isOpened()): |
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ret, frame = cap.read() |
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# ... |
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``` |
