# MediaEval Medico 2026 - Task 1

## Team Info

- **Team name:** Sweet&Sour
- **Member:** Nguyễn Minh Quang
- **Email:** nmquang04072005@gmail.com

## Model Overview

This submission uses **CATA-Qwen3B**, a curriculum-aware structural VQA system that combines a frozen ViT visual encoder, online lesion-prior/topological feature extraction, full structural fusion, and **Qwen2.5-3B-Instruct with QLoRA r16** plus deep TopoAdapter layers. The model was trained with curriculum alignment warmup and gate regularization before full-data continuation.

## Architecture Summary

```text
Image
→ ViT visual encoder
→ online structural extraction:
   - lesion prior mask M_prior
   - topological feature map
   - global morphology features
→ structural visual fusion
→ Qwen2.5-3B-Instruct + QLoRA r16
→ curriculum-gated TopoAdapter injection in the last 8 decoder layers
→ generated answer
```

CATA-Qwen3B extends the full structural TopoAdapter architecture with a curriculum-aware training recipe. The first stage warms up language adaptation, while later training restores spatial/topological alignment losses and gate regularization. At inference time, the extracted structural features condition both visual fusion and the Qwen decoder TopoAdapters.

## Dependencies

Install the required packages with:

```bash
pip install -r requirements.txt
```

The script also loads the base LLM from Hugging Face:

```text
Qwen/Qwen2.5-3B-Instruct
```

## How to Run

From this folder, run:

```bash
python submission_task1.py
```

The system will load the checkpoint from:

```text
checkpoints/last.pt
```

It will run prediction on the Task 1 test split and write the output file:

```text
predictions_1.json
```

## Files

```text
submission_task1.py     Main inference script
requirements.txt        Python dependencies
checkpoints/last.pt     Trainable checkpoint weights
src/                    Model, topology, and post-processing code
```