ultralytics/examples/tutorial.ipynb

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Welcome to the Ultralytics YOLO11 🚀 notebook! YOLO11 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.

YOLO11 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.

We hope that the resources in this notebook will help you get the most out of YOLO11. Please browse the YOLO11 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!

Setup

Pip install ultralytics and dependencies and check software and hardware.

In [1]:

%pip install ultralytics
import ultralytics
ultralytics.checks()

Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
Setup complete ✅ (2 CPUs, 12.7 GB RAM, 41.1/112.6 GB disk)

1. Predict

YOLO11 may be used directly in the Command Line Interface (CLI) with a yolo command for a variety of tasks and modes and accepts additional arguments, i.e. imgsz=640. See a full list of available yolo arguments and other details in the YOLO11 Predict Docs.

In [2]:

# Run inference on an image with YOLO11n
!yolo predict model=yolo11n.pt source='https://ultralytics.com/images/zidane.jpg'

Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...
100% 5.35M/5.35M [00:00<00:00, 72.7MB/s]
Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs

Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...
100% 49.2k/49.2k [00:00<00:00, 5.37MB/s]
image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 63.4ms
Speed: 14.5ms preprocess, 63.4ms inference, 820.9ms postprocess per image at shape (1, 3, 384, 640)
Results saved to runs/detect/predict
💡 Learn more at https://docs.ultralytics.com/modes/predict

        

2. Val

Validate a model's accuracy on the COCO dataset's val or test splits. The latest YOLO11 models are downloaded automatically the first time they are used. See YOLO11 Val Docs for more information.

In [ ]:

# Download COCO val
import torch
torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')  # download (780M - 5000 images)
!unzip -q tmp.zip -d datasets && rm tmp.zip  # unzip

In [3]:

# Validate YOLO11n on COCO8 val
!yolo val model=yolo11n.pt data=coco8.yaml

Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs

Dataset 'coco8.yaml' images not found ⚠️, missing path '/content/datasets/coco8/images/val'
Downloading https://ultralytics.com/assets/coco8.zip to '/content/datasets/coco8.zip'...
100% 433k/433k [00:00<00:00, 15.8MB/s]
Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 1188.35file/s]
Dataset download success ✅ (1.4s), saved to /content/datasets

Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...
100% 755k/755k [00:00<00:00, 17.7MB/s]
val: Scanning /content/datasets/coco8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 142.04it/s]
val: New cache created: /content/datasets/coco8/labels/val.cache
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:04<00:00,  4.75s/it]
                   all          4         17       0.57       0.85      0.847      0.632
                person          3         10      0.557        0.6      0.585      0.272
                   dog          1          1      0.548          1      0.995      0.697
                 horse          1          2      0.531          1      0.995      0.674
              elephant          1          2      0.371        0.5      0.516      0.256
              umbrella          1          1      0.569          1      0.995      0.995
          potted plant          1          1      0.847          1      0.995      0.895
Speed: 1.0ms preprocess, 73.8ms inference, 0.0ms loss, 561.4ms postprocess per image
Results saved to runs/detect/val
💡 Learn more at https://docs.ultralytics.com/modes/val

3. Train

Train YOLO11 on Detect, Segment, Classify and Pose datasets. See YOLO11 Train Docs for more information.

In [ ]:

#@title Select YOLO11 🚀 logger {run: 'auto'}
logger = 'Comet' #@param ['Comet', 'TensorBoard']

if logger == 'Comet':
  %pip install -q comet_ml
  import comet_ml; comet_ml.init()
elif logger == 'TensorBoard':
  %load_ext tensorboard
  %tensorboard --logdir .

In [7]:

# Train YOLO11n on COCO8 for 3 epochs
!yolo train model=yolo11n.pt data=coco8.yaml epochs=3 imgsz=640

Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
engine/trainer: task=detect, mode=train, model=yolo11n.pt, data=coco8.yaml, epochs=3, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train3, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train3

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]      
  3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
  4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]     
  5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
  6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]                 
 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 13                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1, False]          
 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]           
 17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]          
 20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]           
 23        [16, 19, 22]  1    464912  ultralytics.nn.modules.head.Detect           [80, [64, 128, 256]]          
YOLO11n summary: 319 layers, 2,624,080 parameters, 2,624,064 gradients, 6.6 GFLOPs

Transferred 499/499 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLO11n...
AMP: checks passed ✅
train: Scanning /content/datasets/coco8/labels/train.cache... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<?, ?it/s]
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
val: Scanning /content/datasets/coco8/labels/val.cache... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<?, ?it/s]
Plotting labels to runs/detect/train/labels.jpg... 
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)
TensorBoard: model graph visualization added ✅
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to runs/detect/train
Starting training for 3 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
        1/3     0.719G      1.004      3.249      1.367         30        640: 100% 1/1 [00:00<00:00,  1.16it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  5.07it/s]
                   all          4         17       0.58       0.85      0.849      0.631

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
        2/3     0.715G       1.31      4.043      1.603         35        640: 100% 1/1 [00:00<00:00,  6.88it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  9.08it/s]
                   all          4         17      0.581       0.85      0.851       0.63

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
        3/3     0.692G      1.134      3.174      1.599         18        640: 100% 1/1 [00:00<00:00,  6.75it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 11.60it/s]
                   all          4         17      0.582       0.85      0.855      0.632

3 epochs completed in 0.003 hours.
Optimizer stripped from runs/detect/train/weights/last.pt, 5.5MB
Optimizer stripped from runs/detect/train/weights/best.pt, 5.5MB

Validating runs/detect/train/weights/best.pt...
Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 23.42it/s]
                   all          4         17      0.579       0.85      0.855      0.615
                person          3         10      0.579        0.6      0.623      0.268
                   dog          1          1      0.549          1      0.995      0.697
                 horse          1          2      0.553          1      0.995      0.675
              elephant          1          2      0.364        0.5      0.528      0.261
              umbrella          1          1      0.571          1      0.995      0.895
          potted plant          1          1      0.857          1      0.995      0.895
Speed: 0.2ms preprocess, 4.3ms inference, 0.0ms loss, 1.2ms postprocess per image
Results saved to runs/detect/train
💡 Learn more at https://docs.ultralytics.com/modes/train

4. Export

Export a YOLO11 model to any supported format below with the format argument, i.e. format=onnx. See YOLO11 Export Docs for more information.

• 💡 ProTip: Export to ONNX or OpenVINO for up to 3x CPU speedup.
• 💡 ProTip: Export to TensorRT for up to 5x GPU speedup.

Format	format Argument	Model	Metadata	Arguments
PyTorch	-	yolo11n.pt	✅	-
TorchScript	torchscript	yolo11n.torchscript	✅	imgsz, optimize, batch
ONNX	onnx	yolo11n.onnx	✅	imgsz, half, dynamic, simplify, opset, batch
OpenVINO	openvino	yolo11n_openvino_model/	✅	imgsz, half, int8, batch
TensorRT	engine	yolo11n.engine	✅	imgsz, half, dynamic, simplify, workspace, int8, batch
CoreML	coreml	yolo11n.mlpackage	✅	imgsz, half, int8, nms, batch
TF SavedModel	saved_model	yolo11n_saved_model/	✅	imgsz, keras, int8, batch
TF GraphDef	pb	yolo11n.pb	❌	imgsz, batch
TF Lite	tflite	yolo11n.tflite	✅	imgsz, half, int8, batch
TF Edge TPU	edgetpu	yolo11n_edgetpu.tflite	✅	imgsz
TF.js	tfjs	yolo11n_web_model/	✅	imgsz, half, int8, batch
PaddlePaddle	paddle	yolo11n_paddle_model/	✅	imgsz, batch
NCNN	ncnn	yolo11n_ncnn_model/	✅	imgsz, half, batch

In [5]:

!yolo export model=yolo11n.pt format=torchscript

Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CPU (Intel Xeon 2.20GHz)
YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs

PyTorch: starting from 'yolo11n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (5.4 MB)

TorchScript: starting export with torch 2.4.1+cu121...
TorchScript: export success ✅ 2.4s, saved as 'yolo11n.torchscript' (10.5 MB)

Export complete (4.2s)
Results saved to /content
Predict:         yolo predict task=detect model=yolo11n.torchscript imgsz=640  
Validate:        yolo val task=detect model=yolo11n.torchscript imgsz=640 data=coco.yaml  
Visualize:       https://netron.app
💡 Learn more at https://docs.ultralytics.com/modes/export

5. Python Usage

YOLO11 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLO11 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See detailed Python usage examples in the YOLO11 Python Docs.

In [ ]:

from ultralytics import YOLO

# Load a model
model = YOLO('yolo11n.yaml')  # build a new model from scratch
model = YOLO('yolo11n.pt')  # load a pretrained model (recommended for training)

# Use the model
results = model.train(data='coco8.yaml', epochs=3)  # train the model
results = model.val()  # evaluate model performance on the validation set
results = model('https://ultralytics.com/images/bus.jpg')  # predict on an image
results = model.export(format='onnx')  # export the model to ONNX format

6. Tasks

YOLO11 can train, val, predict and export models for the most common tasks in vision AI: Detect, Segment, Classify and Pose. See YOLO11 Tasks Docs for more information.

1. Detection

YOLO11 detection models have no suffix and are the default YOLO11 models, i.e. yolo11n.pt and are pretrained on COCO. See Detection Docs for full details.

In [ ]:

# Load YOLO11n, train it on COCO128 for 3 epochs and predict an image with it
from ultralytics import YOLO

model = YOLO('yolo11n.pt')  # load a pretrained YOLO detection model
model.train(data='coco8.yaml', epochs=3)  # train the model
model('https://ultralytics.com/images/bus.jpg')  # predict on an image

2. Segmentation

YOLO11 segmentation models use the -seg suffix, i.e. yolo11n-seg.pt and are pretrained on COCO. See Segmentation Docs for full details.

In [ ]:

# Load YOLO11n-seg, train it on COCO128-seg for 3 epochs and predict an image with it
from ultralytics import YOLO

model = YOLO('yolo11n-seg.pt')  # load a pretrained YOLO segmentation model
model.train(data='coco8-seg.yaml', epochs=3)  # train the model
model('https://ultralytics.com/images/bus.jpg')  # predict on an image

3. Classification

YOLO11 classification models use the -cls suffix, i.e. yolo11n-cls.pt and are pretrained on ImageNet. See Classification Docs for full details.

In [ ]:

# Load YOLO11n-cls, train it on mnist160 for 3 epochs and predict an image with it
from ultralytics import YOLO

model = YOLO('yolo11n-cls.pt')  # load a pretrained YOLO classification model
model.train(data='mnist160', epochs=3)  # train the model
model('https://ultralytics.com/images/bus.jpg')  # predict on an image

4. Pose

YOLO11 pose models use the -pose suffix, i.e. yolo11n-pose.pt and are pretrained on COCO Keypoints. See Pose Docs for full details.

In [ ]:

# Load YOLO11n-pose, train it on COCO8-pose for 3 epochs and predict an image with it
from ultralytics import YOLO

model = YOLO('yolo11n-pose.pt')  # load a pretrained YOLO pose model
model.train(data='coco8-pose.yaml', epochs=3)  # train the model
model('https://ultralytics.com/images/bus.jpg')  # predict on an image

4. Oriented Bounding Boxes (OBB)

YOLO11 OBB models use the -obb suffix, i.e. yolo11n-obb.pt and are pretrained on the DOTA dataset. See OBB Docs for full details.

In [ ]:

# Load YOLO11n-obb, train it on DOTA8 for 3 epochs and predict an image with it
from ultralytics import YOLO

model = YOLO('yolo11n-obb.pt')  # load a pretrained YOLO OBB model
model.train(data='dota8.yaml', epochs=3)  # train the model
model('https://ultralytics.com/images/boats.jpg')  # predict on an image

Appendix

Additional content below.

In [ ]:

# Pip install from source
!pip install git+https://github.com/ultralytics/ultralytics@main

In [ ]:

# Git clone and run tests on updates branch
!git clone https://github.com/ultralytics/ultralytics -b main
%pip install -qe ultralytics

In [ ]:

# Run tests (Git clone only)
!pytest ultralytics/tests

In [ ]:

# Validate multiple models
for x in 'nsmlx':
  !yolo val model=yolo11{x}.pt data=coco.yaml