{"id":18028474,"url":"https://github.com/interactivetech/yolov8-coreapi-v2","last_synced_at":"2025-10-09T11:13:16.689Z","repository":{"id":174719894,"uuid":"645958467","full_name":"interactivetech/yolov8-coreapi-v2","owner":"interactivetech","description":null,"archived":false,"fork":false,"pushed_at":"2023-09-28T02:11:06.000Z","size":747,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-10T05:24:56.940Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/interactivetech.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":"docs/SECURITY.md","support":null,"governance":null}},"created_at":"2023-05-26T21:24:25.000Z","updated_at":"2023-05-26T21:25:42.000Z","dependencies_parsed_at":null,"dependency_job_id":"3311adb9-98eb-4f08-8c33-bae4af03ccd8","html_url":"https://github.com/interactivetech/yolov8-coreapi-v2","commit_stats":null,"previous_names":["interactivetech/yolov8-coreapi-v2"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/interactivetech%2Fyolov8-coreapi-v2","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/interactivetech%2Fyolov8-coreapi-v2/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/interactivetech%2Fyolov8-coreapi-v2/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/interactivetech%2Fyolov8-coreapi-v2/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/interactivetech","download_url":"https://codeload.github.com/interactivetech/yolov8-coreapi-v2/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247249601,"owners_count":20908211,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-30T08:42:17.977Z","updated_at":"2025-10-09T11:13:11.641Z","avatar_url":"https://github.com/interactivetech.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n  \u003cp\u003e\n    \u003ca href=\"https://ultralytics.com/yolov8\" target=\"_blank\"\u003e\n      \u003cimg width=\"100%\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"\u003e\u003c/a\u003e\n  \u003c/p\u003e\n\n[English](README.md) | [简体中文](README.zh-CN.md)\n\u003cbr\u003e\n\n\u003cdiv\u003e\n    \u003ca href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml\"\u003e\u003cimg src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg\" alt=\"Ultralytics CI\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://zenodo.org/badge/latestdoi/264818686\"\u003e\u003cimg src=\"https://zenodo.org/badge/264818686.svg\" alt=\"YOLOv8 Citation\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://hub.docker.com/r/ultralytics/ultralytics\"\u003e\u003cimg src=\"https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker\" alt=\"Docker Pulls\"\u003e\u003c/a\u003e\n    \u003cbr\u003e\n    \u003ca href=\"https://console.paperspace.com/github/ultralytics/ultralytics\"\u003e\u003cimg src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"/\u003e\u003c/a\u003e\n    \u003ca href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://www.kaggle.com/ultralytics/yolov8\"\u003e\u003cimg src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"\u003e\u003c/a\u003e\n  \u003c/div\u003e\n  \u003cbr\u003e\n\n[Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) 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.\n\nWe hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 \u003ca href=\"https://docs.ultralytics.com/\"\u003eDocs\u003c/a\u003e for details, raise an issue on \u003ca href=\"https://github.com/ultralytics/ultralytics/issues/new/choose\"\u003eGitHub\u003c/a\u003e for support, and join our \u003ca href=\"https://discord.gg/n6cFeSPZdD\"\u003eDiscord\u003c/a\u003e community for questions and discussions!\n\nTo request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).\n\n\u003cimg width=\"100%\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png\"\u003e\u003c/a\u003e\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://github.com/ultralytics\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png\" width=\"2%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"\" /\u003e\n  \u003ca href=\"https://www.linkedin.com/company/ultralytics/\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png\" width=\"2%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"\" /\u003e\n  \u003ca href=\"https://twitter.com/ultralytics\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png\" width=\"2%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"\" /\u003e\n  \u003ca href=\"https://youtube.com/ultralytics\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png\" width=\"2%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"\" /\u003e\n  \u003ca href=\"https://www.tiktok.com/@ultralytics\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png\" width=\"2%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"\" /\u003e\n  \u003ca href=\"https://www.instagram.com/ultralytics/\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png\" width=\"2%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\" alt=\"\" /\u003e\n  \u003ca href=\"https://discord.gg/n6cFeSPZdD\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png\" width=\"2%\" alt=\"\" /\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\n## \u003cdiv align=\"center\"\u003eDocumentation\u003c/div\u003e\n\nSee 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.\n\n\u003cdetails open\u003e\n\u003csummary\u003eInstall\u003c/summary\u003e\n\nPip install the ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python\u003e=3.7**](https://www.python.org/) environment with [**PyTorch\u003e=1.7**](https://pytorch.org/get-started/locally/).\n\n```bash\npip install ultralytics\n```\n\n\u003c/details\u003e\n\n\u003cdetails open\u003e\n\u003csummary\u003eUsage\u003c/summary\u003e\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 [CLI Docs](https://docs.ultralytics.com/usage/cli) for examples.\n\n#### Python\n\nYOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/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\npath = 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 Ultralytics [release](https://github.com/ultralytics/assets/releases). See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.\n\n\u003c/details\u003e\n\n## \u003cdiv align=\"center\"\u003eModels\u003c/div\u003e\n\nAll YOLOv8 pretrained models are available here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.\n\n[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.\n\n\u003cdetails open\u003e\u003csummary\u003eDetection\u003c/summary\u003e\n\nSee [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models.\n\n| Model                                                                                | size\u003cbr\u003e\u003csup\u003e(pixels) | mAP\u003csup\u003eval\u003cbr\u003e50-95 | Speed\u003cbr\u003e\u003csup\u003eCPU ONNX\u003cbr\u003e(ms) | Speed\u003cbr\u003e\u003csup\u003eA100 TensorRT\u003cbr\u003e(ms) | params\u003cbr\u003e\u003csup\u003e(M) | FLOPs\u003cbr\u003e\u003csup\u003e(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\u003csup\u003eval\u003c/sup\u003e** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.\n  \u003cbr\u003eReproduce 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/) instance.\n  \u003cbr\u003eReproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu`\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\u003csummary\u003eSegmentation\u003c/summary\u003e\n\nSee [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage examples with these models.\n\n| Model                                                                                        | size\u003cbr\u003e\u003csup\u003e(pixels) | mAP\u003csup\u003ebox\u003cbr\u003e50-95 | mAP\u003csup\u003emask\u003cbr\u003e50-95 | Speed\u003cbr\u003e\u003csup\u003eCPU ONNX\u003cbr\u003e(ms) | Speed\u003cbr\u003e\u003csup\u003eA100 TensorRT\u003cbr\u003e(ms) | params\u003cbr\u003e\u003csup\u003e(M) | FLOPs\u003cbr\u003e\u003csup\u003e(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\u003csup\u003eval\u003c/sup\u003e** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.\n  \u003cbr\u003eReproduce 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/) instance.\n  \u003cbr\u003eReproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\u003csummary\u003eClassification\u003c/summary\u003e\n\nSee [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples with these models.\n\n| Model                                                                                        | size\u003cbr\u003e\u003csup\u003e(pixels) | acc\u003cbr\u003e\u003csup\u003etop1 | acc\u003cbr\u003e\u003csup\u003etop5 | Speed\u003cbr\u003e\u003csup\u003eCPU ONNX\u003cbr\u003e(ms) | Speed\u003cbr\u003e\u003csup\u003eA100 TensorRT\u003cbr\u003e(ms) | params\u003cbr\u003e\u003csup\u003e(M) | FLOPs\u003cbr\u003e\u003csup\u003e(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  \u003cbr\u003eReproduce 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/) instance.\n  \u003cbr\u003eReproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\u003csummary\u003ePose\u003c/summary\u003e\n\nSee [Pose Docs](https://docs.ultralytics.com/tasks/pose) for usage examples with these models.\n\n| Model                                                                                                | size\u003cbr\u003e\u003csup\u003e(pixels) | mAP\u003csup\u003epose\u003cbr\u003e50-95 | mAP\u003csup\u003epose\u003cbr\u003e50 | Speed\u003cbr\u003e\u003csup\u003eCPU ONNX\u003cbr\u003e(ms) | Speed\u003cbr\u003e\u003csup\u003eA100 TensorRT\u003cbr\u003e(ms) | params\u003cbr\u003e\u003csup\u003e(M) | FLOPs\u003cbr\u003e\u003csup\u003e(B) |\n| ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |\n| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt)       | 640                   | 50.4                  | 80.1               | 131.8                          | 1.18                                | 3.3                | 9.2               |\n| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt)       | 640                   | 60.0                  | 86.2               | 233.2                          | 1.42                                | 11.6               | 30.2              |\n| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt)       | 640                   | 65.0                  | 88.8               | 456.3                          | 2.00                                | 26.4               | 81.0              |\n| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt)       | 640                   | 67.6                  | 90.0               | 784.5                          | 2.59                                | 44.4               | 168.6             |\n| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt)       | 640                   | 69.2                  | 90.2               | 1607.1                         | 3.73                                | 69.4               | 263.2             |\n| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280                  | 71.6                  | 91.2               | 4088.7                         | 10.04                               | 99.1               | 1066.4            |\n\n- **mAP\u003csup\u003eval\u003c/sup\u003e** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org)\n  dataset.\n  \u003cbr\u003eReproduce by `yolo val pose data=coco-pose.yaml device=0`\n- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.\n  \u003cbr\u003eReproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu`\n\n\u003c/details\u003e\n\n## \u003cdiv align=\"center\"\u003eIntegrations\u003c/div\u003e\n\n\u003cbr\u003e\n\u003ca href=\"https://bit.ly/ultralytics_hub\" target=\"_blank\"\u003e\n\u003cimg width=\"100%\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"\u003e\u003c/a\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://roboflow.com/?ref=ultralytics\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png\" width=\"10%\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"15%\" height=\"0\" alt=\"\" /\u003e\n  \u003ca href=\"https://cutt.ly/yolov5-readme-clearml\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png\" width=\"10%\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"15%\" height=\"0\" alt=\"\" /\u003e\n  \u003ca href=\"https://bit.ly/yolov8-readme-comet\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png\" width=\"10%\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"15%\" height=\"0\" alt=\"\" /\u003e\n  \u003ca href=\"https://bit.ly/yolov5-neuralmagic\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png\" width=\"10%\" /\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n|                                                           Roboflow                                                           |                                                            ClearML ⭐ NEW                                                            |                                                                        Comet ⭐ NEW                                                                        |                                           Neural Magic ⭐ 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/yolov8-readme-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](https://bit.ly/yolov5-neuralmagic) |\n\n## \u003cdiv align=\"center\"\u003eUltralytics HUB\u003c/div\u003e\n\nExperience seamless AI with [Ultralytics HUB](https://bit.ly/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](https://ultralytics.com/app_install). Start your journey for **Free** now!\n\n\u003ca href=\"https://bit.ly/ultralytics_hub\" target=\"_blank\"\u003e\n\u003cimg width=\"100%\" src=\"https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png\"\u003e\u003c/a\u003e\n\n## \u003cdiv align=\"center\"\u003eContribute\u003c/div\u003e\n\nWe love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started, and fill out our [Survey](https://ultralytics.com/survey?utm_source=github\u0026utm_medium=social\u0026utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors!\n\n\u003c!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 --\u003e\n\n\u003ca href=\"https://github.com/ultralytics/yolov5/graphs/contributors\"\u003e\n\u003cimg width=\"100%\" src=\"https://github.com/ultralytics/assets/raw/main/im/image-contributors.png\"\u003e\u003c/a\u003e\n\n## \u003cdiv align=\"center\"\u003eLicense\u003c/div\u003e\n\nYOLOv8 is available under two different licenses:\n\n- **AGPL-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 requirements of AGPL-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).\n\n## \u003cdiv align=\"center\"\u003eContact\u003c/div\u003e\n\nFor YOLOv8 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues), and join our [Discord](https://discord.gg/n6cFeSPZdD) community for questions and discussions!\n\n\u003cbr\u003e\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://github.com/ultralytics\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png\" width=\"3%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"\" /\u003e\n  \u003ca href=\"https://www.linkedin.com/company/ultralytics/\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png\" width=\"3%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"\" /\u003e\n  \u003ca href=\"https://twitter.com/ultralytics\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png\" width=\"3%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"\" /\u003e\n  \u003ca href=\"https://youtube.com/ultralytics\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png\" width=\"3%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"\" /\u003e\n  \u003ca href=\"https://www.tiktok.com/@ultralytics\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png\" width=\"3%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"\" /\u003e\n  \u003ca href=\"https://www.instagram.com/ultralytics/\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png\" width=\"3%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\" alt=\"\" /\u003e\n  \u003ca href=\"https://discord.gg/n6cFeSPZdD\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png\" width=\"3%\" alt=\"\" /\u003e\u003c/a\u003e\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finteractivetech%2Fyolov8-coreapi-v2","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Finteractivetech%2Fyolov8-coreapi-v2","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finteractivetech%2Fyolov8-coreapi-v2/lists"}