fassabilf/clip-pelatnas-p2-2026

CLIP Pelatnas P2 2026 — ARIA Multimodal Crisis 🤖👁️📝

Pelatnas IOAI 2026 | Sesi 30 Mei 2026

Repository ini berisi semua materi untuk sesi Multimodal Learning (CLIP) di Pelatnas P2 IOAI 2026, termasuk dataset preparation script, tutorial notebook, dan Kaggle competition pages.

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Isi Repository

├── prep_dataset.py       # Problem-setter: generate seluruh competition dataset
├── clip_tutorial.ipynb   # Tutorial notebook (8 sections)
├── create_notebook.py    # Script untuk regenerate notebook dari source
├── upload_kaggle.py      # Upload dataset ke Kaggle
├── requirements.txt      # Python dependencies
└── pages/
    ├── description.md    # Kaggle Overview tab (narasi ARIA)
    ├── evaluation.md     # Kaggle Evaluation tab
    ├── data.md           # Kaggle Data tab
    └── rules.md          # Kaggle Rules tab

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Kompetisi

Item	Detail
Topik	Multimodal Learning — CLIP
Total items	800 (200 per task)
Metrik	Accuracy (flat)
Baseline	~70–80% (zero-shot CLIP ViT-B/32)

4 Task

#	Task	Dataset	Train	Test
1	Zero-shot Classification	STL-10	Hanya class names	200 images
2	Linear Probing	STL-10	1.000 labeled images	200 images
3	Image-Text Retrieval	Flickr8k	6.000 image-caption pairs	200 queries (4-way)
4	MCQA	ScienceQA	2.000 questions	200 questions

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Setup

pip install -r requirements.txt
pip install git+https://github.com/openai/CLIP.git

Buat Competition Dataset

python prep_dataset.py
# Output: ./output/clip-pelatnas-p2/   ← upload ke Kaggle
#         ./output/solution.csv         ← simpan private

Upload ke Kaggle

# Setup credentials dulu:
mkdir -p ~/.kaggle
echo '{"username":"YOUR_USERNAME","key":"YOUR_KEY"}' > ~/.kaggle/kaggle.json
chmod 600 ~/.kaggle/kaggle.json

python upload_kaggle.py

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Narasi Kompetisi

Lihat pages/description.md untuk narasi lengkap tentang ARIA (AI research station yang rusak akibat solar flare) yang perlu direkonstruksi oleh para kadet.

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Difficulty Calibration

Task	Zero-shot CLIP	Target P2
Zero-shot	~85–95%	>90%
Linear probe	~90–95%	>93%
Retrieval	~75–85%	>85%
MCQA	~35–50%	>55%
Overall	~70–80%	>85%