RizwanMunawar

import cv2
import numpy as np
from ultralytics import YOLO

from ultralytics.utils.plotting import Annotator, colors


class YOLOSpotlight:
    """
    YOLO Interactive Object Tracker with Live Crop Display. Click on any detected object to track

RizwanMunawar

# ======================================
# Ultralytics YOLO + Tracking + Forecast
# ======================================

import cv2
import torch
import numpy as np
from collections import defaultdict, deque
from ultralytics import YOLO
from ultralytics.utils.plotting import colors

RizwanMunawar

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n-obb.pt")  # load an official model
model = YOLO("path/to/best.pt")  # load a custom model

# Predict with the model
results = model("https://ultralytics.com/images/boats.jpg")  # predict on an image
results = model("Path/to/video/file.mp4")  # predict on a video

RizwanMunawar

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n-pose.pt")  # load an official model

# Predict with the model
results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
results = model("Path/to/video/file.mp4")  # predict on a video

RizwanMunawar

import cv2  # OpenCV library for image/video processing
from ultralytics import YOLO  # Ultralytics YOLO model for object detection/segmentation
from ultralytics.utils.plotting import Annotator, colors  # Utilities for annotating and visualizing YOLO results

# Load the Ultralytics YOLO11 segmentation model
model = YOLO("yolo26n-seg.pt")

# Get the class names from the model
names = model.model.names

RizwanMunawar

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n.pt")  # load an official model

# Predict with the model
results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
results = model("path/to/video/file.mp4")  # predict on a video