#### NOTE
This is a **community plugin**, an external project maintained by its respective author.
Community plugins are not part of FiftyOne core and may change independently.
Please review each plugin’s documentation and license before use.
# davis-2017
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A [FiftyOne remote zoo dataset](https://docs.voxel51.com/dataset_zoo/remote.html) for **DAVIS 2017** — the Densely Annotated VIdeo Segmentation challenge release used for the semi-supervised video object segmentation track. Data are organized as per-frame samples (or as generated video clips) with instance-level masks and boxes. Official challenge hub: [davischallenge.org/davis2017/code.html](https://davischallenge.org/davis2017/code.html).
## Source, citation, and license
- **Challenge / download page:** [https://davischallenge.org/davis2017/code.html](https://davischallenge.org/davis2017/code.html)
- **Evaluation code (reference implementation):** [https://github.com/davisvideochallenge/davis2017-evaluation](https://github.com/davisvideochallenge/davis2017-evaluation)
- **License:** same terms as the DAVIS release — see [LICENSE in davis2017-evaluation](https://github.com/davisvideochallenge/davis2017-evaluation/blob/master/LICENSE)
- **Citation:**
```default
@article{Pont-Tuset_arXiv_2017,
author = {Jordi Pont-Tuset and Federico Perazzi and Sergi Caelles and Pablo Arbel\'aez and Alexander Sorkine-Hornung and Luc {Van Gool}},
title = {The 2017 DAVIS Challenge on Video Object Segmentation},
journal = {arXiv:1704.00675},
year = {2017}
}
```
## Quick start
Install FiftyOne, then load the dataset by GitHub URL (downloads and caches automatically when needed):
```bash
pip install fiftyone
```
```python
import fiftyone as fo
import fiftyone.zoo as foz
dataset = foz.load_zoo_dataset(
"https://github.com/voxel51/davis-2017",
split="validation", # optional, for a specific split
max_samples=500, # optional, for quick exploration
format="image", # "image" (default) or "video"
)
session = fo.launch_app(dataset)
# Dynamically Grouped Dataset
grouped_view = dataset.group_by("sequence_id", order_by="frame_number")
# Explore a single sequence
seq_view = dataset.match_tags("blackswan")
```
Notes:
- Supported `split` values: `train`, `validation`, `test-dev`, `test-challenge`.
- Resolution: 480p (matches the `DAVIS-2017-*-480p.zip` archives fetched from the official host)
- Image mode creates one sample per frame. Video mode stitches each sequence into an `.mp4` and creates a local copy under the dataset tree. Attaches `ground_truth` on frames where annotations exist.
## Statistics
| Split | Sequences | Total Samples | Annotated Samples |
|----------------|-------------|-----------------|---------------------|
| train | 59 | 4,209 | 4,209 |
| validation | 30 | 1,999 | 1,999 |
| test-dev | 30 | 2,086 | 30 |
| test-challenge | 30 | 2,180 | 30 |
Training and validation splits are fully annotated. For **test-dev** and **test-challenge**, only the first frames carry dense masks (hence the small “annotated” counts in the table); remaining frames appear without `ground_truth` detections.
## Sample fields
Each sample includes:
- **`filepath`** — JPEG for that frame (image / group modes) or generated video path (`format="video"`).
- **`tags`** —
- DAVIS subset name (`train`, `val`, `test-dev`, or `test-challenge`; note the zoo split is `validation` but the tag is `val`)
- DAVIS the sequence name
- **`sequence_id`** — DAVIS sequence (clip) name. Note: Sequences do not overlap accross splits.
- **`frame_number`** — frame id within the sequence (image / group modes; frame index in video mode).
- **`ground_truth`** — [`fo.Detections`](https://docs.voxel51.com/api/fiftyone.core.labels.html#fiftyone.core.labels.Detections) with one [`Detection`](https://docs.voxel51.com/api/fiftyone.core.labels.html#fiftyone.core.labels.Detection) per object: normalized bounding box, instance mask (`mask`), and label string `{sequence_id}{object_index}`.
## Visualization
Example grid with the dataset loaded in **image** format:
