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Ultralytics YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.

We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!

To request an Enterprise License please complete the form at Ultralytics Licensing.

Documentation

See below for a quickstart installation and usage example, and see the YOLOv8 Docs for full documentation on training, validation, prediction and deployment for the original version. This forked version contains only a few additions, which are detailed in the section Forked code additions

Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

For alternative installation methods including Conda, Docker, and Git, please refer to the Quickstart Guide.

Usage

CLI

YOLOv8 may be used directly in the Command Line Interface (CLI) with a yolo command:

yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'

yolo can be used for a variety of tasks and modes and accepts additional arguments, i.e. imgsz=640. See the YOLOv8 CLI Docs for examples.

Python

YOLOv8 may also be used directly in a Python environment, and accepts the same arguments as in the CLI example above:

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.yaml")  # build a new model from scratch
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

# Use the model
model.train(data="coco128.yaml", epochs=3)  # train the model
metrics = model.val()  # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
path = model.export(format="onnx")  # export the model to ONNX format

See YOLOv8 Python Docs for more examples.

Forked code additions

This sections illustrates the changes added to main code in this fork.

There are three main additions:

1. Custom augmentations: This permits the addition of new augmentations not present in the ultralytics pipeline.
2. Excluding bounding boxes: This enables the training process to remove specific classes from the images.
3. Biasing data using labels: This allows the user to select what classes are going to appear more freqently in the training.

These three additions can be used in the code as follows:

from ultralytics.ultralytics import YOLO

model = YOLO("yolov8n.yaml")
#if not self.standard_params.resume:
#    self.model.args = hyper_params

#self.model.overrides = args.to_dict()

# The code seems to reset all the parameters of the models when training for some reason
# So, we need to pass them to override them
input_data = dict(getattr(model, 'args', model.model.args))

input_data['data'] = dataset

# New options for this forked code
input_data['cfg']['shuffle_class'] = True
input_data['cfg']['class_rate'] = generator_class_rate
input_data['cfg']['ambiguous_classes'] = ambiguous_classes
input_data['cfg']['mapping_classes'] = mapping_classes
input_data['cfg']['delete_cache'] = True  # This will force the cache of the data to be removed. It is useful if the dataset has been updated, since ultralytic cahced the dataset

input_data['methods'] = {'custom_augmentation': custom_augmentation}

model.train(**input_data)

The custom augmentation is a function that receives the label object of ultralytics (output of the model when predicting) and returns the same type of object. This object contains the image and the boxes, masks, etc... The bounding boxes are in the format center, width, height.

An illustration of an augmentation function is presented below:

def custom_augmentation(label):
    bbox = label['instances'].bboxes
    image = label['img']
    .
    .
    .
    # Output of the model is the same as the input, the image and bboxes, mask etc... must be modified and stored
    return modified_label

In order to perform the removal of bounding boxes from images the parameters ambiguous_classes and mapping_classes are used. The purpose of this addition is the following: Imagine that we have a small dataset of people. In this dataset, each individual person is labelled, but in the dataset there are also hands, heads and other parts of the human body, but very few of them. In order to get an initial model, it would be convenient not to use these objects, but we do want to keep using those images because complete complete persons may be in those images as well.

To solve this issue, this code allows the user to select a set of classes that are going to be removed, meaning the objects in the images will be replaced with random noise.

The bounding boxes in training and validation contain the classes that are being represented. These classes are going to be integers from 0 to nc_complete, in contrast to nc as in the original code. The nc represents the number of classes that are going to be detected whereas nc_complete is the total number of classes in the dataset.

In addition, there will be a variable containing the classes that must be removed from the images, named excluded_classes and a mapper that will change the class index of the classes to be detected so that the index is continuous.

An example of these variables is shown below:

ambiguous_classes = [2, 3, 8, 9, 11]
mapping_classes = {0: 0, 1: 1, 4: 2, 5: 3, 6: 4, 7: 5, 10: 6, 12: 7}

The nc_complete must be passed to the yaml file containing the dataset. An example of this yaml file is:

names:
- barrier
- barrier_angle
- barrier_connector
- barrier_pad
- manhole
- plastic_barrier
- plastic_barrier_angle
- water_barrier
nc: 8
nc_complete: 13
train: PATH/TO/training.txt
val: PATH/TO/validation.txt

Lastly, the shuffle_class and generator_class_rate are used to bias the dataset when needed. It allows to pass the relative rate of each class. For instance, if we have 3 classes: barrier, manhole and water_barrier and we want the class manhole to be used twice as much as the other two since this class seems to be harder for the model to train it properly. Then,

generator_class_rate = {'barrier': 1, 'manhole': 2, 'water_barrier': 1}

Notice the shuffle_class is used to radomly select data from a class or to use all the dataset as is. So, if shuffle_class is False (default behaviour and the only one in the original code) then all the dataset will be used, for instance, if there are 100 images (80 of class barrier and 20 of the rests) the 100 images will be used per epoch. When shuffle_class is True, the training will select each class with equal probability regardless of the number of images of each class. So, 'barrier', 'manhole', 'water_barrier' will be selected with the same probability even when 'barrier' is more common. If generator_class_rate is provided then the classes will be selected following that rate instead of with equal probability.

Models

YOLOv8 Detect, Segment and Pose models pretrained on the COCO dataset are available here, as well as YOLOv8 Classify models pretrained on the ImageNet dataset. Track mode is available for all Detect, Segment and Pose models.

All Models download automatically from the latest Ultralytics release on first use.

Detection (COCO)

See Detection Docs for usage examples with these models trained on COCO, which include 80 pre-trained classes.

Model	size (pixels)	mAPval 50-95	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv8n	640	37.3	80.4	0.99	3.2	8.7
YOLOv8s	640	44.9	128.4	1.20	11.2	28.6
YOLOv8m	640	50.2	234.7	1.83	25.9	78.9
YOLOv8l	640	52.9	375.2	2.39	43.7	165.2
YOLOv8x	640	53.9	479.1	3.53	68.2	257.8

• mAP^val values are for single-model single-scale on COCO val2017 dataset.
  Reproduce by yolo val detect data=coco.yaml device=0
• Speed averaged over COCO val images using an Amazon EC2 P4d instance.
  Reproduce by yolo val detect data=coco.yaml batch=1 device=0|cpu

Detection (Open Image V7)

See Detection Docs for usage examples with these models trained on Open Image V7, which include 600 pre-trained classes.

Model	size (pixels)	mAPval 50-95	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv8n	640	18.4	142.4	1.21	3.5	10.5
YOLOv8s	640	27.7	183.1	1.40	11.4	29.7
YOLOv8m	640	33.6	408.5	2.26	26.2	80.6
YOLOv8l	640	34.9	596.9	2.43	44.1	167.4
YOLOv8x	640	36.3	860.6	3.56	68.7	260.6

• mAP^val values are for single-model single-scale on Open Image V7 dataset.
  Reproduce by yolo val detect data=open-images-v7.yaml device=0
• Speed averaged over Open Image V7 val images using an Amazon EC2 P4d instance.
  Reproduce by yolo val detect data=open-images-v7.yaml batch=1 device=0|cpu

Segmentation (COCO)

See Segmentation Docs for usage examples with these models trained on COCO-Seg, which include 80 pre-trained classes.

Model	size (pixels)	mAPbox 50-95	mAPmask 50-95	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv8n-seg	640	36.7	30.5	96.1	1.21	3.4	12.6
YOLOv8s-seg	640	44.6	36.8	155.7	1.47	11.8	42.6
YOLOv8m-seg	640	49.9	40.8	317.0	2.18	27.3	110.2
YOLOv8l-seg	640	52.3	42.6	572.4	2.79	46.0	220.5
YOLOv8x-seg	640	53.4	43.4	712.1	4.02	71.8	344.1

• mAP^val values are for single-model single-scale on COCO val2017 dataset.
  Reproduce by yolo val segment data=coco-seg.yaml device=0
• Speed averaged over COCO val images using an Amazon EC2 P4d instance.
  Reproduce by yolo val segment data=coco-seg.yaml batch=1 device=0|cpu

Pose (COCO)

See Pose Docs for usage examples with these models trained on COCO-Pose, which include 1 pre-trained class, person.

Model	size (pixels)	mAPpose 50-95	mAPpose 50	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv8n-pose	640	50.4	80.1	131.8	1.18	3.3	9.2
YOLOv8s-pose	640	60.0	86.2	233.2	1.42	11.6	30.2
YOLOv8m-pose	640	65.0	88.8	456.3	2.00	26.4	81.0
YOLOv8l-pose	640	67.6	90.0	784.5	2.59	44.4	168.6
YOLOv8x-pose	640	69.2	90.2	1607.1	3.73	69.4	263.2
YOLOv8x-pose-p6	1280	71.6	91.2	4088.7	10.04	99.1	1066.4

• mAP^val values are for single-model single-scale on COCO Keypoints val2017 dataset.
  Reproduce by yolo val pose data=coco-pose.yaml device=0
• Speed averaged over COCO val images using an Amazon EC2 P4d instance.
  Reproduce by yolo val pose data=coco-pose.yaml batch=1 device=0|cpu

Classification (ImageNet)

See Classification Docs for usage examples with these models trained on ImageNet, which include 1000 pretrained classes.

Model	size (pixels)	acc top1	acc top5	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B) at 640
YOLOv8n-cls	224	66.6	87.0	12.9	0.31	2.7	4.3
YOLOv8s-cls	224	72.3	91.1	23.4	0.35	6.4	13.5
YOLOv8m-cls	224	76.4	93.2	85.4	0.62	17.0	42.7
YOLOv8l-cls	224	78.0	94.1	163.0	0.87	37.5	99.7
YOLOv8x-cls	224	78.4	94.3	232.0	1.01	57.4	154.8

• acc values are model accuracies on the ImageNet dataset validation set.
  Reproduce by yolo val classify data=path/to/ImageNet device=0
• Speed averaged over ImageNet val images using an Amazon EC2 P4d instance.
  Reproduce by yolo val classify data=path/to/ImageNet batch=1 device=0|cpu

Integrations

Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with Roboflow, ClearML, Comet, Neural Magic and OpenVINO, can optimize your AI workflow.

Roboflow	ClearML ⭐ NEW	Comet ⭐ NEW	Neural Magic ⭐ NEW
Label and export your custom datasets directly to YOLOv8 for training with Roboflow	Automatically track, visualize and even remotely train YOLOv8 using ClearML (open-source!)	Free forever, Comet lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions	Run YOLOv8 inference up to 6x faster with Neural Magic DeepSparse

Ultralytics HUB

Experience seamless AI with Ultralytics HUB ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly Ultralytics App. Start your journey for Free now!

Contribute

We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our Contributing Guide to get started, and fill out our Survey to send us feedback on your experience. Thank you 🙏 to all our contributors!

License

Ultralytics offers two licensing options to accommodate diverse use cases:

• AGPL-3.0 License: This OSI-approved open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the LICENSE file for more details.
• Enterprise License: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through Ultralytics Licensing.

Contact

For Ultralytics bug reports and feature requests please visit GitHub Issues, and join our Discord community for questions and discussions!