<div align="center">
<p>
<a href="https://ultralytics.com/yolov8" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
</p>
[English](README.md) | [简体中文](README.zh-CN.md)
<br>
<div>
<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>
<br>
[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics), developed by [Ultralytics](https://ultralytics.com),
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, image segmentation and image
classification tasks.
To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
<div align="center">
<a href="https://github.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://twitter.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://youtube.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
</div>
</div>
## <div align="center">Documentation</div>
See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for
full documentation on training, validation, prediction and deployment.
<details open>
<summary>Install</summary>
Pip install the ultralytics package including
all [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a
[**Python>=3.7**](https://www.python.org/) environment with
[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
```bash
pip install ultralytics
```
</details>
<details open>
<summary>Usage</summary>
#### CLI
YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
```bash
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](https://docs.ultralytics.com/cli) for examples.
#### Python
YOLOv8 may also be used directly in a Python environment, and accepts the
same [arguments](https://docs.ultralytics.com/cfg/) as in the CLI example above:
```python
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
success = model.export(format="onnx") # export the model to ONNX format
```
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/assets/releases). See
YOLOv8 [Python Docs](https://docs.ultralytics.com/python) for more examples.
#### Model Architectures
⭐ **NEW** YOLOv5u anchor free models are now available.
All supported model architectures can be found in the [Models](./ultralytics/models/) section.
#### Known Issues / TODOs
We are still working on several parts of YOLOv8! We aim to have these completed soon to bring the YOLOv8 feature set up
to par with YOLOv5, including export and inference to all the same formats. We are also writing a YOLOv8 paper which we
will submit to [arxiv.org](https://arxiv.org) once complete.
- [x] TensorFlow exports
- [x] DDP resume
- [ ] [arxiv.org](https://arxiv.org) paper
</details>
## <div align="center">Models</div>
All YOLOv8 pretrained models are available here. Detection and Segmentation models are pretrained on the COCO dataset,
while Classification models are pretrained on the ImageNet dataset.
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
<details open><summary>Detection</summary>
See [Detection Docs](https://docs.ultralytics.com/tasks/detection/) for usage examples with these models.
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
<br>Reproduce by `yolo val detect data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
instance.
<br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0/cpu`
</details>
<details><summary>Segmentation</summary>
See [Segmentation Docs](https://docs.ultralytics.com/tasks/segmentation/) for usage examples with these models.
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
<br>Reproduce by `yolo val segment data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
instance.
<br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0/cpu`
</details>
<details><summary>Classification</summary>
See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) for usage examples with these models.
| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 |
| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 |
| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 |
| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |
| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |
- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
<br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
instance.
<br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0/cpu`
</details>
## <div align="center">Integrations</div>
<br>
<a href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"></a>
<br>
<br>
<div align="center">
<a href="https://roboflow.com/?ref=ultralytics">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
<a href="https://cutt.ly/yolov5-readme-clearml">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
<a href="https://bit.ly/yolov5-readme-comet2">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
<a href="https://bit.ly/yolov5-neuralmagic">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a>
</div>
| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
| Label and export your custom datasets directly to YOLOv8 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv8 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions | Run YOLOv8 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
## <div align="center">Ultralytics HUB</div>
Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data
visualization, YOLOv5 and YOLOv8 (coming soon) 🚀 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](https://ultralytics.com/app_install). Start your journey for **Free** now!
<a href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
## <div align="center">Contribute</div>
We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see
our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out
our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback
on your experience. Thank you 🙏 to all our contributors!
<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
<img src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" /></a>
## <div align="center">License</div>
YOLOv8 is available under two different licenses:
- **GPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source
requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and
applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
## <div align="center">Contact</div>
For YOLOv8 bug reports and feature requests please
visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) or
the [Ultralytics Community Forum](https://community.ultralytics.com/).
<br>
<div align="center">
<a href="https://github.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
<a href="https://twitter.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="3%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
<a href="https://youtube.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
<a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width="3%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="" /></a>
</div>
Raw data
{
"_id": null,
"home_page": "https://github.com/ultralytics/ultralytics",
"name": "imagedetect",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "machine-learning,deep-learning,vision,ML,DL,AI,YOLO,YOLOv3,YOLOv5,YOLOv8,HUB,Ultralytics",
"author": "Ultralytics",
"author_email": "hello@ultralytics.com",
"download_url": "https://files.pythonhosted.org/packages/7b/eb/8fe301342d5fb7612481d76625e87501b2e775d6fb293d0c84251269b375/imagedetect-8.0.57.tar.gz",
"platform": null,
"description": "<div align=\"center\">\n <p>\n <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n <img width=\"100%\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n </p>\n\n[English](README.md) | [\u7b80\u4f53\u4e2d\u6587](README.zh-CN.md)\n<br>\n\n<div>\n <a href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml\"><img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg\" alt=\"Ultralytics CI\"></a>\n <a href=\"https://zenodo.org/badge/latestdoi/264818686\"><img src=\"https://zenodo.org/badge/264818686.svg\" alt=\"YOLOv8 Citation\"></a>\n <a href=\"https://hub.docker.com/r/ultralytics/ultralytics\"><img src=\"https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker\" alt=\"Docker Pulls\"></a>\n <br>\n <a href=\"https://console.paperspace.com/github/ultralytics/ultralytics\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"/></a>\n <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n <a href=\"https://www.kaggle.com/ultralytics/yolov8\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n </div>\n <br>\n\n[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics), developed by [Ultralytics](https://ultralytics.com),\nis a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces\nnew features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and\neasy to use, making it an excellent choice for a wide range of object detection, image segmentation and image\nclassification tasks.\n\nTo request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).\n\n<img width=\"100%\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png\"></a>\n\n<div align=\"center\">\n <a href=\"https://github.com/ultralytics\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png\" width=\"2%\" alt=\"\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"\" />\n <a href=\"https://www.linkedin.com/company/ultralytics\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png\" width=\"2%\" alt=\"\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"\" />\n <a href=\"https://twitter.com/ultralytics\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png\" width=\"2%\" alt=\"\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"\" />\n <a href=\"https://www.producthunt.com/@glenn_jocher\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png\" width=\"2%\" alt=\"\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"\" />\n <a href=\"https://youtube.com/ultralytics\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png\" width=\"2%\" alt=\"\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"\" />\n <a href=\"https://www.facebook.com/ultralytics\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png\" width=\"2%\" alt=\"\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"\" />\n <a href=\"https://www.instagram.com/ultralytics/\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png\" width=\"2%\" alt=\"\" /></a>\n </div>\n</div>\n\n## <div align=\"center\">Documentation</div>\n\nSee below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for\nfull documentation on training, validation, prediction and deployment.\n\n<details open>\n<summary>Install</summary>\n\nPip install the ultralytics package including\nall [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a\n[**Python>=3.7**](https://www.python.org/) environment with\n[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).\n\n```bash\npip install ultralytics\n```\n\n</details>\n\n<details open>\n<summary>Usage</summary>\n\n#### CLI\n\nYOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:\n\n```bash\nyolo predict model=yolov8n.pt source=\"https://ultralytics.com/images/bus.jpg\"\n```\n\n`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8\n[CLI Docs](https://docs.ultralytics.com/cli) for examples.\n\n#### Python\n\nYOLOv8 may also be used directly in a Python environment, and accepts the\nsame [arguments](https://docs.ultralytics.com/cfg/) as in the CLI example above:\n\n```python\nfrom ultralytics import YOLO\n\n# Load a model\nmodel = YOLO(\"yolov8n.yaml\") # build a new model from scratch\nmodel = YOLO(\"yolov8n.pt\") # load a pretrained model (recommended for training)\n\n# Use the model\nmodel.train(data=\"coco128.yaml\", epochs=3) # train the model\nmetrics = model.val() # evaluate model performance on the validation set\nresults = model(\"https://ultralytics.com/images/bus.jpg\") # predict on an image\nsuccess = model.export(format=\"onnx\") # export the model to ONNX format\n```\n\n[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest\nUltralytics [release](https://github.com/ultralytics/assets/releases). See\nYOLOv8 [Python Docs](https://docs.ultralytics.com/python) for more examples.\n\n#### Model Architectures\n\n\u2b50 **NEW** YOLOv5u anchor free models are now available.\n\nAll supported model architectures can be found in the [Models](./ultralytics/models/) section.\n\n#### Known Issues / TODOs\n\nWe are still working on several parts of YOLOv8! We aim to have these completed soon to bring the YOLOv8 feature set up\nto par with YOLOv5, including export and inference to all the same formats. We are also writing a YOLOv8 paper which we\nwill submit to [arxiv.org](https://arxiv.org) once complete.\n\n- [x] TensorFlow exports\n- [x] DDP resume\n- [ ] [arxiv.org](https://arxiv.org) paper\n\n</details>\n\n## <div align=\"center\">Models</div>\n\nAll YOLOv8 pretrained models are available here. Detection and Segmentation models are pretrained on the COCO dataset,\nwhile Classification models are pretrained on the ImageNet dataset.\n\n[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest\nUltralytics [release](https://github.com/ultralytics/assets/releases) on first use.\n\n<details open><summary>Detection</summary>\n\nSee [Detection Docs](https://docs.ultralytics.com/tasks/detection/) for usage examples with these models.\n\n| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |\n| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |\n| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |\n| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |\n| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |\n| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |\n| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |\n\n- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.\n <br>Reproduce by `yolo val detect data=coco.yaml device=0`\n- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)\n instance.\n <br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0/cpu`\n\n</details>\n\n<details><summary>Segmentation</summary>\n\nSee [Segmentation Docs](https://docs.ultralytics.com/tasks/segmentation/) for usage examples with these models.\n\n| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |\n| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |\n| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |\n| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |\n| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |\n| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |\n| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |\n\n- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.\n <br>Reproduce by `yolo val segment data=coco.yaml device=0`\n- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)\n instance.\n <br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0/cpu`\n\n</details>\n\n<details><summary>Classification</summary>\n\nSee [Classification Docs](https://docs.ultralytics.com/tasks/classification/) for usage examples with these models.\n\n| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |\n| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |\n| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 |\n| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 |\n| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 |\n| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |\n| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |\n\n- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.\n <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`\n- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)\n instance.\n <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0/cpu`\n\n</details>\n\n## <div align=\"center\">Integrations</div>\n\n<br>\n<a href=\"https://bit.ly/ultralytics_hub\" target=\"_blank\">\n<img width=\"100%\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"></a>\n<br>\n<br>\n\n<div align=\"center\">\n <a href=\"https://roboflow.com/?ref=ultralytics\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png\" width=\"10%\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"15%\" height=\"0\" alt=\"\" />\n <a href=\"https://cutt.ly/yolov5-readme-clearml\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png\" width=\"10%\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"15%\" height=\"0\" alt=\"\" />\n <a href=\"https://bit.ly/yolov5-readme-comet2\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png\" width=\"10%\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"15%\" height=\"0\" alt=\"\" />\n <a href=\"https://bit.ly/yolov5-neuralmagic\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png\" width=\"10%\" /></a>\n</div>\n\n| Roboflow | ClearML \u2b50 NEW | Comet \u2b50 NEW | Neural Magic \u2b50 NEW |\n| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |\n| Label and export your custom datasets directly to YOLOv8 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv8 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions | Run YOLOv8 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |\n\n## <div align=\"center\">Ultralytics HUB</div>\n\nExperience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) \u2b50, the all-in-one solution for data\nvisualization, YOLOv5 and YOLOv8 (coming soon) \ud83d\ude80 model training and deployment, without any coding. Transform images\ninto actionable insights and bring your AI visions to life with ease using our cutting-edge platform and\nuser-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now!\n\n<a href=\"https://bit.ly/ultralytics_hub\" target=\"_blank\">\n<img width=\"100%\" src=\"https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png\"></a>\n\n## <div align=\"center\">Contribute</div>\n\nWe love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see\nour [Contributing Guide](CONTRIBUTING.md) to get started, and fill out\nour [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback\non your experience. Thank you \ud83d\ude4f to all our contributors!\n\n<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->\n\n<a href=\"https://github.com/ultralytics/yolov5/graphs/contributors\">\n<img src=\"https://github.com/ultralytics/assets/raw/main/im/image-contributors.png\" /></a>\n\n## <div align=\"center\">License</div>\n\nYOLOv8 is available under two different licenses:\n\n- **GPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.\n- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source\n requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and\n applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).\n\n## <div align=\"center\">Contact</div>\n\nFor YOLOv8 bug reports and feature requests please\nvisit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) or\nthe [Ultralytics Community Forum](https://community.ultralytics.com/).\n\n<br>\n<div align=\"center\">\n <a href=\"https://github.com/ultralytics\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://www.linkedin.com/company/ultralytics\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://twitter.com/ultralytics\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://www.producthunt.com/@glenn_jocher\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://youtube.com/ultralytics\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://www.facebook.com/ultralytics\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png\" width=\"3%\" alt=\"\" /></a>\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"\" />\n <a href=\"https://www.instagram.com/ultralytics/\" style=\"text-decoration:none;\">\n <img src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png\" width=\"3%\" alt=\"\" /></a>\n</div>\n\n\n",
"bugtrack_url": null,
"license": "GPL-3.0",
"summary": "Ultralytics YOLOv8",
"version": "8.0.57",
"split_keywords": [
"machine-learning",
"deep-learning",
"vision",
"ml",
"dl",
"ai",
"yolo",
"yolov3",
"yolov5",
"yolov8",
"hub",
"ultralytics"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "c6e2fa3416b78736dbfa30e199a661db5a732712047afca501483787552967af",
"md5": "dd73267011e9d96a8cc4defc074812e1",
"sha256": "b93f143c956df34807e37f5456ec4bb729dbb2fd915b85833d5cee699cbec036"
},
"downloads": -1,
"filename": "imagedetect-8.0.57-py3-none-any.whl",
"has_sig": false,
"md5_digest": "dd73267011e9d96a8cc4defc074812e1",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 500814,
"upload_time": "2023-03-27T04:05:55",
"upload_time_iso_8601": "2023-03-27T04:05:55.471259Z",
"url": "https://files.pythonhosted.org/packages/c6/e2/fa3416b78736dbfa30e199a661db5a732712047afca501483787552967af/imagedetect-8.0.57-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "7beb8fe301342d5fb7612481d76625e87501b2e775d6fb293d0c84251269b375",
"md5": "2b4ca925b2acd9fb821655fc0a46f0f0",
"sha256": "af44cd05ea812dbcbfa0f54c80ae7cc7be746e7f01a10942061ba34cb99f41aa"
},
"downloads": -1,
"filename": "imagedetect-8.0.57.tar.gz",
"has_sig": false,
"md5_digest": "2b4ca925b2acd9fb821655fc0a46f0f0",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 444546,
"upload_time": "2023-03-27T04:06:15",
"upload_time_iso_8601": "2023-03-27T04:06:15.605998Z",
"url": "https://files.pythonhosted.org/packages/7b/eb/8fe301342d5fb7612481d76625e87501b2e775d6fb293d0c84251269b375/imagedetect-8.0.57.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-03-27 04:06:15",
"github": true,
"gitlab": false,
"bitbucket": false,
"github_user": "ultralytics",
"github_project": "ultralytics",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "imagedetect"
}
JSON
