| --- |
| license: other |
| license_name: custom-split-licensing |
| license_link: LICENSE |
| configs: |
| - config_name: default |
| data_files: |
| - split: eval |
| path: |
| - "ego4d/frames/**" |
| - "ego4d/annotations/**" |
| - "mose/frames/**" |
| - "mose/annotations/**" |
| - "lvos/frames/**" |
| - "lvos/annotations/**" |
| --- |
| |
| <div align="center"> |
| <h1> Is This Tracker On? A Benchmark Protocol for Dynamic Tracking </h1> |
| </div> |
|
|
| <div align="center"> |
| <!-- <a href="#model">Model</a> • --> |
| 🌐 <a href="https://glab-caltech.github.io/ITTO/">Project Website</a> | |
| <!-- 🏆 <a href="https://hkust-nlp.github.io/agentboard/static/leaderboard.html">Leaderboard</a> | --> |
| 💻 <a href="https://github.com/ilonadem/itto/tree/main">Code</a> | |
| 📚 <a href=" ">Paper</a> |
|
|
| </div> |
|
|
| # Dataset card for ITTO |
|
|
| ITTO is a challenging new benchmark suite for evaluating and diagnosing the capabilities and limitations of point tracking methods. |
|
|
| ## Installation: |
|
|
| ITTO contains three component datasets: (1) MOSE, (2) L-VOS, (3) Ego4D. You will have to install the L-VOS and Ego4D components of the dataset individually due to licensing permissions. |
|
|
| 1. First, install the dataset: |
|
|
| ``` |
| git clone https://huggingface.co/datasets/demalenk/itto |
| ``` |
|
|
| 2. Next, install the L-VOS portion. Note that this will create temporary files in intermediate steps: |
|
|
| ``` |
| bash lvos/install_lvos.sh |
| ``` |
|
|
| 3. Next, install the Ego4D portion of the dataset. Note that you have need access to obtain access to Ego4D data. License requests can take a few hours to a few days, and can be obtained here: https://ego4d-data.org/docs/start-here/ |
|
|
| Make sure that you have the ego4d CLI installed by running: |
| ``` |
| pip install ego4d |
| ``` |
|
|
| ``` |
| bash ego4d/install_ego4d.sh |
| ``` |
|
|
| ## Running Evaluations with ITTO |
|
|
| We provide evaluation scripts in the [ITTO github repo](https://github.com/ilonadem/itto), which contains dataloaders and model evaluation scripts for the numbers reported in the paper. |
|
|
|
|
| ## Citing ITTO |
|
|
| ITTO aggregates videos from three public datasets: [MOSE](https://github.com/henghuiding/MOSE-api), [LVOS](https://github.com/LingyiHongfd/LVOS), and [Ego4D](https://ego4d-data.org/). Please cite all of them in addition to citing ITTO. Each component keeps its original license and usage terms. We provide all license information in [`LICENSE.md`](LICENSE). |
