class YOLOV8Detector: def __init__(self, model_path='yolov8s.onnx'): self.model_path = model_path self.session, self.output_names = onnx_load(self.model_path) self.imgsz = [640, 640] def detect_objects(self, image): im, org_data = data_process_cv2(image, self.imgsz) y = ...
import os import torch # Ensure torch is available for CUDA checks from autodistill_grounded_sam import GroundedSAM from autodistill.detection import CaptionOntology from autodistill.utils import plot import cv2 import supervision as sv from autodistill_yolov8 import YOLOv8 IMAGE_DIR_PATH = f"2024...
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[CVPR 2024] Real-Time Open-Vocabulary Object Detection - fix yolov8 numpy decode by GreatV · Pull Request #251 · AILab-CVC/YOLO-World
@@ -306,5 +306,4 @@ def __yolov8_decode(self, conf_thres: float, num_labels: int = 80, **kwargs): reg_max: int = kwargs.get('reg_max', 16) self.__ppyoloe_decode(feats, conf_thres, num_labels, reg_max=reg_max) self.__yolov6_decode(feats, conf_thres, num_labels) ...
Inference yolov8 faster python main_faster.py --image_path="image_test/IMG_0812.JPG" --model_path="best_openvino_model/openvino_model_yolov8.xml" --output_path="debug/result_faster" Results Inference time MethodRun 1 Time (s)Run 2 Time (s) openvino + torch 10.393 4.290 openvino +...
I tried to use an image as input that is in ndarray-format. According to the docs this should work. import numpy import cv2 model = YOLO("best.pt") image2 = cv2.imread("zidane.jpg") model.predict(source=image2, show="True") I'm getting t...
StrongSORT is still very slow in itself so I see no application for it in real time scenarios (~ 0.1 seconds for just strongsort per frame on a 1660ti mobile while my custom trained yolov7 tiny needs an order of magnitude less than that. ).. ️ 1 Owner mikel-brostrom commented...