This repository provides QLoRA fine‑tuning artifacts for fingernail‑based anemia detection using a MedSigLIP vision encoder. It includes the trained LoRA adapters, linear head, processor config, and detailed evaluation metrics. The goal is a lightweight, GPU‑efficient fine‑tuning pipeline suitable for limited VRAM setups.

fingernails_qlora/
- lora_adapters/ – LoRA adapter weights and config
- linear_head.pt – classification head
- processor/ – preprocessing config
- test_metrics.json – evaluation metrics
- train_config.json – training hyperparameters
code/fingernails/ – training and split scripts (mirrors the code repo)
README.md – technical report

Dataset (Counts Only)

Source dataset folder in the main project:

Dataset/Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana/Fingernails/

Split counts (from code/fingernails/*.csv):

Train: 3409 images (Non‑Anemia 1371, Anemia 2038)
Val: 390 images (Non‑Anemia 150, Anemia 240)
Test: 417 images (Non‑Anemia 174, Anemia 243)

Method

Backbone: MedSigLIP vision encoder (frozen backbone).
Fine‑tuning: QLoRA (LoRA adapters for parameter‑efficient tuning).
Classifier: linear head on pooled vision embeddings.
Preprocessing: resize to 448x448, RGB conversion, normalize to [-1, 1].

Experiments & Results

QLoRA Fine‑Tuning (Test Metrics)

From fingernails_qlora/test_metrics.json:

Accuracy: 0.582734
Precision: 0.582734
Recall: 1.000000
F1: 0.736364
ROC‑AUC: 0.620288

Zero‑Shot Baselines (Comparison)

Baseline (fingernails, test=417)

Accuracy: 0.417266
Precision: 0.000000
Recall: 0.000000
F1: 0.000000
ROC‑AUC: 0.440542
Confusion Matrix: [[174, 0], [243, 0]]

Baseline (ROI, test=417)

Accuracy: 0.412470
Precision: 0.478261
Recall: 0.090535
F1: 0.152249
ROC‑AUC: 0.406485
Confusion Matrix: [[150, 24], [221, 22]]

Baseline (all fingernails, test=4216)

Accuracy: 0.439042
Precision: 0.701031
Recall: 0.107894
F1: 0.187006
ROC‑AUC: 0.564913
Confusion Matrix: [[1579, 116], [2249, 272]]

Comparison Table (Test Metrics)

Experiment	Accuracy	Precision	Recall	F1	ROC‑AUC	Notes
Zero‑shot baseline (test=417)	0.417266	0.000000	0.000000	0.000000	0.440542	no training
Zero‑shot baseline (ROI, test=417)	0.412470	0.478261	0.090535	0.152249	0.406485	ROI cropped
Zero‑shot baseline (all, test=4216)	0.439042	0.701031	0.107894	0.187006	0.564913	all images
QLoRA fine‑tune (test=417)	0.582734	0.582734	1.000000	0.736364	0.620288	5 epochs

Training Command (QLoRA)

uv run python src/fingernails/qlora_finetune.py \
  --train-csv "src/fingernails/train.csv" \
  --val-csv "src/fingernails/val.csv" \
  --test-csv "src/fingernails/test.csv" \
  --model-dir medsiglip \
  --output-dir results/fingernails_qlora \
  --batch-size 2 \
  --accumulation-steps 8 \
  --epochs 5 \
  --lr 2e-4 \
  --class-weight 1.5 \
  --log-every 20

Hardware Requirements

Minimum working configuration used in this project:

GPU: 1x NVIDIA RTX 3060 (12 GB VRAM)
RAM: 16 GB or more recommended
Disk: 5+ GB (dataset + artifacts)

Limitations

For research and triage only; not for clinical diagnosis.
Metrics depend on dataset distribution and acquisition conditions.
QLoRA recall is high in this run; calibration should be validated in broader settings.

Contact

Model author: Sidharth (Hugging Face: Sidharth1743).

Sidharth1743/Fingernail_anemia_QLora

Fingernail MedSigLIP QLoRA

Overview

Contents