Search before asking
Question
I am working on vehicle detection in traffic scene. I have used YOLOv11x-obb (pre-trained on DOTAv1) for vehicle detection. I tried SAHI so I can detect small vehicles successfully but it failed. I used codes from #1394. I have attached codes and results. Please guide me How to improve detection with SAHI.
Platform details:
- Ultralytics 8.3.35 🚀 Python-3.10.12 torch-2.5.1+cu121 CPU (Intel Xeon 2.20GHz)
- Supervision version: 0.25.0
YOLO prediction without SAHI:
from ultralytics import YOLO
import cv2
from PIL import Image
model = YOLO("yolo11x-obb.pt")
model.hide_labels = True
model.hide_conf = True
results=model.predict("img.png",show_labels=False)
img = results[0].plot(labels=False, conf=False)
cv2_imshow(img)
YOLO prediction with SAHI
import cv2
import supervision as sv
from ultralytics import YOLO
import numpy as np
image = cv2.imread("img.png")
model = YOLO("yolo11x-obb.pt")
def callback(image_slice: np.ndarray) -> sv.Detections:
result = model(image_slice)[0]
return sv.Detections.from_ultralytics(result)
slicer = sv.InferenceSlicer(callback = callback,overlap_filter="NON_MAX_SUPPRESSION")
detections = slicer(image)
oriented_box_annotator = sv.OrientedBoxAnnotator()
annotated_frame = oriented_box_annotator.annotate(
scene=image.copy(),
detections=detections
)
sv.plot_image(annotated_frame)
Additional
No response
Search before asking
Question
I am working on vehicle detection in traffic scene. I have used YOLOv11x-obb (pre-trained on DOTAv1) for vehicle detection. I tried SAHI so I can detect small vehicles successfully but it failed. I used codes from #1394. I have attached codes and results. Please guide me How to improve detection with SAHI.
Platform details:
YOLO prediction without SAHI:
YOLO prediction with SAHI
Additional
No response