init

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+# Other custom files
+*.pdf filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -1,3 +1,72 @@
----
-license: cc-by-4.0
----
+---
+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://lc.llnl.gov/gitlab/saini5/dsc_2025.git
+```
+
+Launch Presentation consists of:
+- (1) High-Level overview **(Short Slidedeck)** of current state-of-the-art methods for a variety of tasks
+- (2) High-Level overview **(Dataset Card)** of MOVi-MC-AC
+- (3) High-Level overview of **(OneDrive Folder)** of DSC Target Dataset: <u>**Robotics Laboratory Pick and Place Dataset**</u>
+- (4) Low-Level example **(Jupyter Notebook)** of using state-of-the-art methods (SAM2) on DSC Target Dataset
+- (5) Low-Level overview **(Jupyter Notebook)** of MOVi-MC-AC + DSC Tasks
+
+**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