Usage

python

from transformers import AutoImageProcessor, SegformerForSemanticSegmentation
import torch, torch.nn.functional as F
from PIL import Image
import numpy as np

repo = "GlobalWheat/GWFSS_model_v1.0"
processor = AutoImageProcessor.from_pretrained(repo)
model = SegformerForSemanticSegmentation.from_pretrained(repo).eval()

img = Image.open("example.jpg").convert("RGB")
inputs = processor(images=img, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
    up = F.interpolate(logits, size=(img.height, img.width), mode="bilinear", align_corners=False)
pred = up.argmax(1)[0].cpu().numpy()  # (H, W) class IDs

This version is based on huggingface Segformer which could be slightly different from the one we used for our paper. The paper version was implemented based on the mmsegmentation. You can find the model weight for mmsegmentation library in this repo as well.

Related Paper

This dataset is associated with the following paper: The Global Wheat Full Semantic Organ Segmentation (GWFSS) Dataset https://doi.org/10.1016/j.plaphe.2025.100084

GlobalWheat/GWFSS_Segformer_b0

Usage

Related Paper

GlobalWheat/
GWFSS_Segformer_b0