YOLOv8 Detection

Real-time object detection with YOLOv8 and YOLOv11 on Clore.ai

Run real-time object detection with Ultralytics YOLOv8 and YOLOv11.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Update: YOLOv11 (2025) — 22% Faster

YOLOv11 is now available via the same ultralytics package. It delivers 22% faster inference and improved mAP over YOLOv8, with the same simple API. New features include Oriented Bounding Box (OBB) detection. Upgrade by running pip install -U ultralytics.

Renting on CLORE.AI

1. Visit CLORE.AI Marketplace

2. Filter by GPU type, VRAM, and price

3. Choose On-Demand (fixed rate) or Spot (bid price)

4. Configure your order:

   • Select Docker image

   • Set ports (TCP for SSH, HTTP for web UIs)

   • Add environment variables if needed

   • Enter startup command

5. Select payment: CLORE, BTC, or USDT/USDC

6. Create order and wait for deployment

Access Your Server

• Find connection details in My Orders

• Web interfaces: Use the HTTP port URL

• SSH: ssh -p <port> root@<proxy-address>

What is YOLOv8?

YOLOv8 is a high-performance YOLO model offering:

• Object detection

• Instance segmentation

• Pose estimation

• Image classification

• Object tracking

What is YOLOv11?

YOLOv11 (2025) is the latest generation, adding:

• 22% faster inference vs YOLOv8

• Higher mAP across all model sizes

• Oriented Bounding Box (OBB) detection — new task

• Improved architecture (C3k2 blocks, SPPF, C2PSA)

• Same ultralytics package, drop-in replacement

Supported Tasks (YOLOv11)

Task	Suffix	Description
detect	(none)	Object detection with bounding boxes
segment	-seg	Instance segmentation with masks
classify	-cls	Image classification
pose	-pose	Human pose estimation
obb	-obb	NEW Oriented bounding boxes (rotated detection)

Model Sizes

YOLOv8 Models

Model	Size	mAP	Speed (RTX 3090)
YOLOv8n	3.2M	37.3	~1ms
YOLOv8s	11.2M	44.9	~2ms
YOLOv8m	25.9M	50.2	~4ms
YOLOv8l	43.7M	52.9	~6ms
YOLOv8x	68.2M	53.9	~8ms

YOLOv11 Models

Model	Size	mAP	Speed (RTX 3090)
yolo11n	2.6M	39.5	~0.8ms
yolo11s	9.4M	47.0	~1.5ms
yolo11m	20.1M	51.5	~3.2ms
yolo11l	25.3M	53.4	~4.7ms
yolo11x	56.9M	54.7	~6.5ms

YOLOv8 vs YOLOv11 Comparison

Metric	YOLOv8x	yolo11x	Improvement
Parameters	68.2M	56.9M	-17% smaller
mAP50-95 (COCO)	53.9	54.7	+0.8 mAP
Inference (RTX 3090)	~8ms	~6.5ms	+22% faster
FPS (RTX 3090, 640px)	~150	~183	+22% faster
OBB Task	❌	✅	New in v11

Quick Deploy

Docker Image:

pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime

Ports:

22/tcp
7860/http

Command (YOLOv11):

pip install ultralytics gradio && \
python -c "
import gradio as gr
from ultralytics import YOLO
from PIL import Image

model = YOLO('yolo11m.pt')

def detect(image):
    results = model(image)
    return Image.fromarray(results[0].plot())

demo = gr.Interface(fn=detect, inputs=gr.Image(type='pil'), outputs=gr.Image(), title='YOLOv11 Detection')
demo.launch(server_name='0.0.0.0', server_port=7860)
"

Accessing Your Service

After deployment, find your http_pub URL in My Orders:

1. Go to My Orders page

2. Click on your order

3. Find the http_pub URL (e.g., abc123.clorecloud.net)

Use https://YOUR_HTTP_PUB_URL instead of localhost in examples below.

Installation

pip install ultralytics

Same package for both YOLOv8 and YOLOv11. Upgrade to get YOLOv11:

pip install -U ultralytics

YOLOv11 Object Detection

Basic Detection with yolo11m

from ultralytics import YOLO
from PIL import Image

# Load YOLOv11 medium model
model = YOLO('yolo11m.pt')

# Run inference
results = model('image.jpg')

# Show results
results[0].show()

# Save results
results[0].save('output.jpg')

Get Detections

results = model('image.jpg')

for result in results:
    boxes = result.boxes
    for box in boxes:
        # Coordinates
        x1, y1, x2, y2 = box.xyxy[0].tolist()

        # Confidence
        conf = box.conf[0].item()

        # Class
        cls = int(box.cls[0].item())
        name = model.names[cls]

        print(f"{name}: {conf:.2f} at ({x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f})")

Batch Processing

from ultralytics import YOLO
import os

model = YOLO('yolo11m.pt')

input_dir = './images'
output_dir = './detected'
os.makedirs(output_dir, exist_ok=True)

# Process all images
results = model(input_dir, save=True, project=output_dir)

YOLOv11 Tasks

Instance Segmentation

from ultralytics import YOLO

# Load YOLOv11 segmentation model
model = YOLO('yolo11m-seg.pt')

results = model('image.jpg')

for result in results:
    masks = result.masks  # Segmentation masks
    if masks is not None:
        for mask in masks.data:
            # mask is a binary tensor
            pass

Pose Estimation

from ultralytics import YOLO

# Load YOLOv11 pose model
model = YOLO('yolo11m-pose.pt')

results = model('image.jpg')

for result in results:
    keypoints = result.keypoints
    if keypoints is not None:
        for kp in keypoints.data:
            # 17 keypoints: nose, eyes, ears, shoulders, elbows, wrists, hips, knees, ankles
            pass

Classification

from ultralytics import YOLO

# Load YOLOv11 classification model
model = YOLO('yolo11m-cls.pt')

results = model('image.jpg')
for result in results:
    # Top-1 class and confidence
    top1 = result.probs.top1
    top1conf = result.probs.top1conf.item()
    print(f"Class: {result.names[top1]} ({top1conf:.2f})")

Oriented Bounding Box (OBB) — NEW in YOLOv11

OBB detects objects at any rotation angle — perfect for aerial/satellite imagery, document scanning, and text detection.

from ultralytics import YOLO

# Load YOLOv11 OBB model
model = YOLO('yolo11m-obb.pt')

results = model('aerial_image.jpg')

for result in results:
    obb = result.obb
    if obb is not None:
        for box in obb:
            # Rotated box: x_center, y_center, width, height, angle
            xywhr = box.xywhr[0].tolist()
            conf = box.conf[0].item()
            cls = int(box.cls[0].item())
            print(f"{result.names[cls]}: {conf:.2f} rotated box: {xywhr}")

Video Processing

Process Video

from ultralytics import YOLO

model = YOLO('yolo11m.pt')

# Process video
results = model('video.mp4', save=True)

Real-Time Webcam

from ultralytics import YOLO
import cv2

model = YOLO('yolo11n.pt')  # Use nano model for real-time

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    results = model(frame)
    annotated = results[0].plot()

    cv2.imshow('YOLOv11', annotated)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Save Processed Video

from ultralytics import YOLO
import cv2

model = YOLO('yolo11m.pt')

cap = cv2.VideoCapture('input.mp4')
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

out = cv2.VideoWriter('output.mp4', cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    results = model(frame)
    annotated = results[0].plot()
    out.write(annotated)

cap.release()
out.release()

Object Tracking

from ultralytics import YOLO

model = YOLO('yolo11m.pt')

# Track objects in video
results = model.track('video.mp4', save=True, tracker='bytetrack.yaml')

# Access tracking IDs
for result in results:
    boxes = result.boxes
    if boxes.id is not None:
        track_ids = boxes.id.tolist()

Custom Training

Prepare Dataset

# dataset.yaml
path: /workspace/dataset
train: images/train
val: images/val

names:
  0: cat
  1: dog
  2: bird

Train YOLOv11

from ultralytics import YOLO

# Load pretrained YOLOv11 model
model = YOLO('yolo11n.pt')

# Train
results = model.train(
    data='dataset.yaml',
    epochs=100,
    imgsz=640,
    batch=16,
    device=0
)

Training Arguments

model.train(
    data='dataset.yaml',
    epochs=100,
    imgsz=640,
    batch=16,
    device=0,
    workers=8,
    patience=50,         # Early stopping
    save=True,
    save_period=10,      # Save every N epochs
    cache=True,          # Cache images
    amp=True,            # Mixed precision
    lr0=0.01,
    lrf=0.01,
    momentum=0.937,
    weight_decay=0.0005,
    warmup_epochs=3.0,
    box=7.5,
    cls=0.5,
    dfl=1.5,
    augment=True,
    hsv_h=0.015,
    hsv_s=0.7,
    hsv_v=0.4,
    flipud=0.0,
    fliplr=0.5,
    mosaic=1.0,
    mixup=0.0,
)

Export Model

from ultralytics import YOLO

model = YOLO('yolo11m.pt')

# Export to different formats
model.export(format='onnx')           # ONNX
model.export(format='tensorrt')       # TensorRT
model.export(format='openvino')       # OpenVINO
model.export(format='coreml')         # CoreML
model.export(format='tflite')         # TensorFlow Lite

API Server

from fastapi import FastAPI, UploadFile
from fastapi.responses import JSONResponse
from ultralytics import YOLO
from PIL import Image
import io

app = FastAPI()
model = YOLO('yolo11m.pt')

@app.post("/detect")
async def detect(file: UploadFile):
    contents = await file.read()
    image = Image.open(io.BytesIO(contents))

    results = model(image)

    detections = []
    for box in results[0].boxes:
        detections.append({
            "class": model.names[int(box.cls[0])],
            "confidence": float(box.conf[0]),
            "bbox": box.xyxy[0].tolist()
        })

    return JSONResponse(content={"detections": detections})

@app.post("/segment")
async def segment(file: UploadFile):
    contents = await file.read()
    image = Image.open(io.BytesIO(contents))

    model_seg = YOLO('yolo11m-seg.pt')
    results = model_seg(image)

    # Return annotated image
    annotated = results[0].plot()
    # ... convert and return

# Run: uvicorn server:app --host 0.0.0.0 --port 8000

Performance Optimization

TensorRT Export

model = YOLO('yolo11m.pt')
model.export(format='tensorrt', half=True)

# Use exported model
model_trt = YOLO('yolo11m.engine')
results = model_trt('image.jpg')

Batch Inference

# Process multiple images at once
images = ['img1.jpg', 'img2.jpg', 'img3.jpg', 'img4.jpg']
results = model(images, batch=4)

Performance Benchmarks

YOLOv11 FPS (640px input)

Model	GPU	FPS
yolo11n	RTX 3090	~1100
yolo11s	RTX 3090	~730
yolo11m	RTX 3090	~370
yolo11x	RTX 3090	~183
yolo11x	RTX 4090	~305

YOLOv8 FPS (640px input) — Previous Generation

Model	GPU	FPS
YOLOv8n	RTX 3090	~900
YOLOv8s	RTX 3090	~600
YOLOv8m	RTX 3090	~300
YOLOv8x	RTX 3090	~150
YOLOv8x	RTX 4090	~250

Troubleshooting

Out of Memory

# Use smaller model
model = YOLO('yolo11n.pt')

# Or reduce image size
results = model('image.jpg', imgsz=320)

Slow Processing

• Use TensorRT export

• Use smaller model (yolo11n or yolo11s)

• Reduce image size

Low Accuracy

• Use larger model (yolo11x instead of yolo11n)

• Train on custom data

• Increase image size

Cost Estimate

Typical CLORE.AI marketplace rates (as of 2025):

GPU	Hourly Rate	Daily Rate	4-Hour Session
RTX 3060	~$0.03	~$0.70	~$0.12
RTX 3090	~$0.06	~$1.50	~$0.25
RTX 4090	~$0.10	~$2.30	~$0.40
A100 40GB	~$0.17	~$4.00	~$0.70
A100 80GB	~$0.25	~$6.00	~$1.00

Prices vary by provider and demand. Check CLORE.AI Marketplace for current rates.

Save money:

• Use Spot market for flexible workloads (often 30-50% cheaper)

• Pay with CLORE tokens

• Compare prices across different providers

Next Steps

• Segment Anything - Advanced segmentation

• Detectron2 - More detection options

• Real-ESRGAN - Enhance detected objects