ROI-Localization
Collection
Stage 1: In-The-wild Instance Segmentation โข 3 items โข Updated
How to use Subh775/Dis-Seg-Former with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-segmentation", model="Subh775/Dis-Seg-Former") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Subh775/Dis-Seg-Former", dtype="auto")Built on top of RF-DETR Segmentation Nano by Roboflow, this optimized leaf disease segmentation model is fine-tuned for real-world agricultural deployment and exported to high-performance .onnx weights for fast CPU inference, enabling farmers to detect crop diseases in real time with precise overlay masks, affected area estimation, and actionable disease treatment insights.
Base model
Roboflow/rf-detr-seg-nano