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Thyroid VFM Lab Starter

Starter repository for the AI lab project described in AI LAB PROJECT SYNOPSIS_aatman.pdf.

The project is organized around two tracks from the synopsis:

Detection: thyroid nodule localization from ultrasound frames with YOLO.
Classification: benign vs malignant nodule diagnosis with a vision transformer adapted through LoRA.

It also includes a distillation path so a heavier teacher model can supervise a lighter student model for deployment.

What is included

A project scaffold for Python 3.10+.
YAML-based experiment configs.
VOC parsing utilities for TN5000-style annotations.
A detection data exporter for YOLO training.
Baseline entry points for classification, detection, and distillation.

Suggested repository layout

text

data/
  raw/
    JPEGImages/
    Annotations/
    ImageSets/
      Main/
configs/
scripts/
src/
outputs/

Quick start

bash

python -m venv .venv
.venv\Scripts\activate
pip install -e .
python scripts/train_classification.py --config configs/classification_vit_lora.yaml
python scripts/train_detection.py --config configs/detection_yolov8.yaml
python scripts/train_distillation.py --config configs/distillation_mobilenet.yaml

Dataset assumptions

Official TN5000-style images live under data/raw/JPEGImages.
Official VOC XML annotations live under data/raw/Annotations.
Official split files live under data/raw/ImageSets/Main as train.txt, val.txt, and test.txt.
Each split file contains one image stem or relative image path per line.
The loader also accepts simplified aliases such as images, annotations, and splits.
For classification, the current baseline uses the first annotated object as the image label.
TN5000 labels may appear as 0 and 1 in XML; the loader normalizes them to benign and malignant.

If TN5000 is packaged differently on your machine, update the config paths before training.

Model notes

The classification baseline uses a Hugging Face ViT checkpoint with LoRA adapters on attention projections.
The detection baseline converts VOC annotations to YOLO format before training.
The distillation baseline expects a fine-tuned teacher checkpoint and trains a timm student model with hard-label and KL distillation loss.

Next steps

Place TN5000 in data/raw.
Verify actual class names in the XML annotations.
Decide whether the lab should prioritize detection or classification for the first reportable result.
Add experiment tracking and calibration/OOD evaluation once the first baseline is running.

Untraceable09/AI_Project