Install the Ultralytics Package

!pip install ultralytics

Import All the Requried Libraries

import ultralytics
ultralytics.checks()
from ultralytics import YOLO
from IPython.display import Image

Download Dataset from Roboflow

!pip install roboflow

from roboflow import Roboflow
rf = Roboflow(api_key="R5OIUVXGw9WhBkN2T30D")
project = rf.workspace("alpaco5-f3woi").project("part-autolabeld")
version = project.version(5)
dataset = version.download("yolov11")

Train YOLO11 Model on a Custom Dataset

!yolo task=segment mode=train data={dataset.location}/data.yaml model="yolo11n-seg.pt" epochs=25 imgsz=640

Examine Training Results

Image("/content/runs/segment/train/confusion_matrix.png", width=600)

Image("/content/runs/segment/train/labels.jpg", width=600)

Image("/content/runs/segment/train/results.png", width=600)

Image("/content/runs/detect/train/train_batch0.jpg", width=600)

Image("/content/runs/detect/train/val_batch0_pred.jpg", width=600)

Image("/content/runs/detect/train/val_batch1_pred.jpg", width=600)

Validate Fine-Tuned Model

!yolo task=detect mode=val model="/content/runs/detect/train/weights/best.pt" data={dataset.location}/data.yaml

Inference with Custom Model on Images

!yolo task=detect mode=predict model="/content/runs/detect/train/weights/best.pt" conf=0.25 source={dataset.location}/test/images save=True

import glob
import os
from IPython.display import Image as IPyImage, display

latest_folder = max(glob.glob('/content/runs/detect/predict*/'), key=os.path.getmtime)
for img in glob.glob(f'{latest_folder}/*.jpg')[1:4]:
display(IPyImage(filename=img, width=600))
print("\n")

!gdown "https://drive.google.com/uc?id=1ACZp9gmtjEdIY-SHp8K0xdR0D8Gjd3j3&confirm=t"

!yolo task=detect mode=predict model= "/content/runs/detect/train/weights/best.pt" conf=0.25 source=image2.jpg save=True

Image("/content/runs/detect/predict2/image2.jpg", width=600)

Inference with Custom Model on Videos

!gdown "https://drive.google.com/uc?id=1Nyl18zr7zOJHh8uHtSic3dsJSY4LI_t-&confirm=t"

!gdown "https://drive.google.com/uc?id=1SxcrDanPAr-PZfJjWBxoa2t8lUSM2dZn&confirm=t"

!yolo task=detect mode=predict model= "/content/runs/detect/train/weights/best.pt" conf=0.25 source="PPE_Part1.mp4" save=True

from IPython.display import HTML
from base64 import b64encode
import os

Input video path

save_path = '/content/runs/detect/predict3/PPE_Part1.avi'

Compressed video path

compressed_path = "/content/result_compressed.mp4"

os.system(f"ffmpeg -i {save_path} -vcodec libx264 {compressed_path}")

Show video

mp4 = open(compressed_path,'rb').read()
data_url = "data

/mp4;base64," + b64encode(mp4).decode()
HTML("""



""" % data_url)