---
license: cc-by-4.0
task_categories:
- depth-estimation
- image-feature-extraction
- image-to-video
- mask-generation
- object-detection
- zero-shot-object-detection
tags:
- image
- video
- Multicamera
- Video Object Segmentation
- Promptable Segmentation
- Amodal Segmentation
- Amodal Content
- Amodal Object Representations
- Object Retrieval
language:
- en
pretty_name: LLNL 2025 Data Science Challenge
size_categories:
- 1M<n<10M
viewer: false
---

**This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.**

---

# Data Science Challenge 2025

<div align="center">

[![Alt Text](ex_vis.png)](https://huggingface.co/datasets/Amar-S/MOVi-MC-AC)

**Full Dataset Release:** [**MOVi-MC-AC**](https://huggingface.co/datasets/Amar-S/MOVi-MC-AC)

</div>

---

Welcome to the **2025 Lawrence Livermore National Laboratory Data Science Challenge!**

Be sure to `git-lfs install` before cloning the repo, otherwise the large files (managed by LFS) won't be cloned properly!

```
git-lfs install
git clone https://huggingface.co/datasets/Amar-S/LLNL_DSC_2025
```

See [MOVi-MC-AC/DSC_2025.ipynb](MOVi-MC-AC/DSC_2025.ipynb) to get started!

**This notebook serves as the technical introduction to LLNL's MOVi-MC-AC Dataset (last bullet above), covering**:
- (1) Introduction to Image Processing / Computer Vision
- (2) Example Baseline Experiment
- (3) DSC Challenge & Tasking
  - **Task 1.1**: (Image-based) Modal Mask -> Amodal Mask
  - **Task 1.2**: (Image-based) Modal Content (RGB) -> Amodal Content (RGB)
  - **Task 2.1**: (Video-based) Modal Mask -> Amodal Mask
  - **Task 2.2**: (Video-based) Modal Content (RGB) -> Amodal Content (RGB)
  - **Transfer Test**: Apply Models on DSC Target Dataset:
    - Gather Modal Masks from some SotA method (SAM2)
    - Predict Amodal Masks, using Modal Masks
  - **Bonus Task 3**: Create Modal Masks with SAM2
  - **Bonus Task 4**: Re-ID of Objects