Fine-tuned google/siglip2-so400m-patch14-384 vision encoder with a 9-way multi-label linear head, for detecting diseases/conditions on oil-palm (kelapa sawit) seedlings in the pre-nursery phase.

Classes (multi-label — an image may have several)

code	name	note
P00	Normal
P01	LeafSpot
P02	Anthracnose
P03	BudRot	data-limited
P04	Chlorosis
P05	NettleCat	data-limited
P06	AlgalRust
P07	Fusarium	data-limited
P08	FertBurn

How to load and predict

See inference_example.py. In short: load the base SigLIP vision tower, attach the linear head from model.safetensors, preprocess with the bundled preprocessor_config.json, apply per-class thresholds from thresholds.json.

python

import json, torch
from transformers import AutoModel, AutoProcessor
from safetensors.torch import load_file
from PIL import Image
# ... (full runnable version in inference_example.py)

Decision thresholds (IMPORTANT)

This model outputs one independent sigmoid score per class. Do not use a global 0.5 cutoff. Use the per-class thresholds in thresholds.json, which were tuned on a validation split (maximizing per-class F1) and then frozen. A global threshold is known to be wrong here because the class scores are imbalanced and not comparable across classes.

Honest limitations

Data-limited classes. P03, P05, P07 have very few training positives. Their metrics are high-variance and should be treated as indicative only. Headline numbers should be reported as a macro over the six data-adequate classes.
Two-source training data. Training unions two labeled image sources with different acquisition styles and label statistics. Cross-source generalization was audited; see the thesis report. New images from a very different camera/pipeline may see reduced accuracy.
Scope. Pre-nursery seedlings only. Not validated on mature palms, other crops, or field (non-nursery) imagery.
Not a substitute for an agronomist. Intended as a screening / decision- support aid with human verification, not autonomous diagnosis.

Provenance

Undergraduate thesis, Program Studi Ilmu Komputer, FMIPA, Universitas Pakuan. Weights repackaged from training checkpoint siglip2_palm.pt.

farhan-fahlevi/siglip2-for-palm-disease-detection

Oil-Palm Seedling Disease Detector (SigLIP 2, multi-label)

Classes (multi-label — an image may have several)

How to load and predict

Decision thresholds (IMPORTANT)

Honest limitations

Provenance

farhan-fahlevi/
siglip2-for-palm-disease-detection