Dis Seg Former Deprecated
This model is finetuned on the top of RF-DETR-seg-Preview model, utilizing leaf-disease-segmentation dataset in the coco-segmentation format from roboflow.
Model source
- Kind
- Other
- File size
- 485 KB–518 MB
- Version
- vCheckpoint best ema
- Updated
- Nov 4, 2026
- Source
- Hugging Face
Source description
This Model is now Deprecated; New version of segmentation models are released by Roboflow
Leaf Disease Segmentation
License PyTorch Hugging Face Model
This model is finetuned on the top of RF-DETR-seg-Preview model, utilizing leaf-disease-segmentation dataset in the coco-segmentation format from roboflow.
Inference results:
image
Inference Instruction using Colab T4
!pip install -q rfdetr==1.3.0 supervision==0.26.1 roboflow==1.2.10 --quiet
%mkdir output
%cd output
!wget https://huggingface.co/Subh775/Dis-Seg-Former/resolve/main/checkpoint_best_total.pth
from rfdetr import RFDETRSegPreview
import torch
model = RFDETRSegPreview(
pretrain_weights="checkpoint_best_total.pth",
num_classes=1,
segmentation_head=True
)
model.optimize_for_inference()
import supervision as sv
from PIL import Image
def annotate(image: Image.Image, detections: sv.Detections, classes: dict[int, str]) -> Image.Image:
color = sv.ColorPalette.from_hex([
"#ffff00", "#ff9b00", "#ff8080", "#ff66b2", "#ff66ff", "#b266ff",
"#9999ff", "#3399ff", "#66ffff", "#33ff99", "#66ff66", "#99ff00"
])
text_scale = sv.calculate_optimal_text_scale(resolution_wh=image.size)
mask_annotator = sv.MaskAnnotator(color=color)
polygon_annotator = sv.PolygonAnnotator(color=sv.Color.WHITE)
label_annotator = sv.LabelAnnotator(
color=color,
text_color=sv.Color.BLACK,
text_scale=text_scale,
text_position=sv.Position.CENTER_OF_MASS
)
labels = [
f"{classes.get(class_id, 'unknown')} {confidence:.2f}"
for class_id, confidence in zip(detections.class_id, detections.confidence)
]
out = image.copy()
out = mask_annotator.annotate(out, detections)
out = polygon_annotator.annotate(out, detections)
out = label_annotator.annotate(out, detections, labels)
out.thumbnail((1000, 1000))
return out
import torchvision.transforms as T
import supervision as sv
import torch.nn.functional as F
transform = T.Compose([
T.Resize((512, 512)),
T.ToTensor()
])
def run_inference(image_path):
img = Image.open(image_path).convert("RGB")
detections = model.predict(img, threshold=0.5)
return img, detections
classes = {
0: "Disease",
}
def segment_and_visualize(image_path):
img, detections = run_inference(image_path)
annotated = annotate(img, detections, classes)
return annotated
result = segment_and_visualize("res.jpg")
result
Sources
1 source
Sources
1 sourceVerified Nov 4
Download files
Model artifacts
6 artifacts
Model artifacts
6 artifactsSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: apache-2.0 language: - en pipeline_tag: image-segmentation tags: - ROI-extraction --- ### This Model is now Deprecated; New version of segmentation models are released by Roboflow # Leaf Disease Segmentation [](https://opensource.org/licenses/Apache-2.0) [](https://pytorch.org/) [](https://huggingface.co/spaces/LeafNet75/Diseased_ROI_extraction) This model is finetuned on the top of `RF-DETR-seg-Preview` model, utilizing [leaf-disease-segmentation](https://app.roboflow.com/daru-ka-adda/leaf-disease-seg-finhu/1) dataset in the `coco-segmentation` format from roboflow. Inference results:  ### Inference Instruction using Colab T4 ```python !pip install -q rfdetr==1.3.0 supervision==0.26.1 roboflow==1.2.10 --quiet ``` ```python %mkdir output %cd output !wget https://huggingface.co/Subh775/Dis-Seg-Former/resolve/main/checkpoint_best_total.pth ``` ```python from rfdetr import RFDETRSegPreview import torch model = RFDETRSegPreview( pretrain_weights="checkpoint_best_total.pth", num_classes=1, segmentation_head=True ) model.optimize_for_inference() ``` ```python import supervision as sv from PIL import Image def annotate(image: Image.Image, detections: sv.Detections, classes: dict[int, str]) -> Image.Image: color = sv.ColorPalette.from_hex([ "#ffff00", "#ff9b00", "#ff8080", "#ff66b2", "#ff66ff", "#b266ff", "#9999ff", "#3399ff", "#66ffff", "#33ff99", "#66ff66", "#99ff00" ]) text_scale = sv.calculate_optimal_text_scale(resolution_wh=image.size) mask_annotator = sv.MaskAnnotator(color=color) polygon_annotator = sv.PolygonAnnotator(color=sv.Color.WHITE) label_annotator = sv.LabelAnnotator( color=color, text_color=sv.Color.BLACK, text_scale=text_scale, text_position=sv.Position.CENTER_OF_MASS ) labels = [ f"{classes.get(class_id, 'unknown')} {confidence:.2f}" for class_id, confidence in zip(detections.class_id, detections.confidence) ] out = image.copy() out = mask_annotator.annotate(out, detections) out = polygon_annotator.annotate(out, detections) out =...
Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Repo LeafNet75/Dis-Seg-Former-Deprecated
