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yolov8043obb

https://github.com/yanggui19891007/yolov8043obb

Science Score: 54.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: yanggui19891007
  • License: gpl-3.0
  • Default Branch: main
  • Size: 2.15 MB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed almost 3 years ago
Metadata Files
Readme Contributing License Citation Security

README.md

[English](README.md) | [简体中文](README.zh-CN.md)
Ultralytics CI YOLOv8 Citation Docker Pulls
Run on Gradient Open In Colab Open In Kaggle

[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).

Documentation

See below for a quickstart installation and usage example, and see the YOLOv8 Docs for full documentation on training, validation, prediction and deployment.

Install 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 ```
Usage #### 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

Models

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 download automatically from the latest Ultralytics release on first use.

Detection See [Detection Docs](https://docs.ultralytics.com/tasks/detection/) for usage examples with these models. | Model | size
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(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 | - **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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.
Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0/cpu`
Segmentation See [Segmentation Docs](https://docs.ultralytics.com/tasks/segmentation/) for usage examples with these models. | Model | size
(pixels) | mAPbox
50-95 | mAPmask
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(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 | - **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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.
Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0/cpu`
Classification See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) for usage examples with these models. | Model | size
(pixels) | acc
top1 | acc
top5 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(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.
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.
Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0/cpu`

Integrations




| 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 (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. 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

YOLOv8 is available under two different licenses:

  • GPL-3.0 License: See 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.

Contact

For YOLOv8 bug reports and feature requests please visit GitHub Issues or the Ultralytics Community Forum.


Owner

  • Login: yanggui19891007
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use this software, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  - family-names: Chaurasia
    given-names: Ayush
    orcid: "https://orcid.org/0000-0002-7603-6750"
  - family-names: Qiu
    given-names: Jing
    orcid: "https://orcid.org/0000-0003-3783-7069"
  title: "YOLO by Ultralytics"
  version: 8.0.0
  # doi: 10.5281/zenodo.3908559  # TODO
  date-released: 2023-1-10
  license: GPL-3.0
  url: "https://github.com/ultralytics/ultralytics"

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Dependencies

docker/Dockerfile docker
  • pytorch/pytorch latest build
requirements.txt pypi
  • Pillow >=7.1.2
  • PyYAML >=5.3.1
  • matplotlib >=3.2.2
  • numpy >=1.18.5
  • opencv-python >=4.6.0
  • pandas >=1.1.4
  • psutil *
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • tensorboard >=2.4.1
  • thop >=0.1.1
  • torch >=1.7.0
  • torchvision >=0.8.1
  • tqdm >=4.64.0
  • wheel >=0.38.0
setup.py pypi
ultralytics/yolo/utils/nms_rotated/setup.py pypi