{"id":13645038,"url":"https://github.com/SamSamhuns/yolov5_adversarial","last_synced_at":"2025-04-21T11:32:41.514Z","repository":{"id":65317923,"uuid":"584749859","full_name":"SamSamhuns/yolov5_adversarial","owner":"SamSamhuns","description":"Generate adversarial patches against YOLOv5 🚀 ","archived":false,"fork":true,"pushed_at":"2024-12-25T09:29:51.000Z","size":26898,"stargazers_count":46,"open_issues_count":0,"forks_count":7,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-12-25T10:27:04.114Z","etag":null,"topics":["adversarial-attacks","adversarial-machine-learning","object-detection"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":"ultralytics/yolov5","license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SamSamhuns.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":null,"support":null}},"created_at":"2023-01-03T12:23:34.000Z","updated_at":"2024-12-25T09:30:18.000Z","dependencies_parsed_at":"2023-02-14T18:01:37.509Z","dependency_job_id":null,"html_url":"https://github.com/SamSamhuns/yolov5_adversarial","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamSamhuns%2Fyolov5_adversarial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamSamhuns%2Fyolov5_adversarial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamSamhuns%2Fyolov5_adversarial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SamSamhuns%2Fyolov5_adversarial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SamSamhuns","download_url":"https://codeload.github.com/SamSamhuns/yolov5_adversarial/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250048024,"owners_count":21366162,"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":["adversarial-attacks","adversarial-machine-learning","object-detection"],"created_at":"2024-08-02T01:02:24.095Z","updated_at":"2025-04-21T11:32:41.428Z","avatar_url":"https://github.com/SamSamhuns.png","language":"Python","funding_links":[],"categories":["Object Detection Applications"],"sub_categories":[],"readme":"# YOLOv5 physical adversarial patch generation\n\n[Paper: Towards a Robust Adversarial Patch Attack Against Unmanned Aerial Vehicles Object Detection](https://www.researchgate.net/publication/373548943_Towards_a_Robust_Adversarial_Patch_Attack_Against_Unmanned_Aerial_Vehicles_Object_Detection)\n\n\u003cimg src=\"adv_patch_gen/media/patch_on_cars.png\" width=\"563\" /\u003e\n\nhttps://github.com/SamSamhuns/yolov5_adversarial/assets/13418507/ffb90da3-fb13-472e-89a5-db07d5a571b9\n\nPhysical Adversarial Patch Attack\n\nhttps://github.com/SamSamhuns/yolov5_adversarial/assets/13418507/05c47fd2-44f0-42be-b6d9-ef485ce5e08d\n\nPhysical Adversarial Patch Defense (Example given is not implemented in this paper)\n\nhttps://github.com/SamSamhuns/yolov5_adversarial/assets/13418507/b389449f-98a4-4167-9208-771cb98ce3ce\n\n|                    VisDrone Dataset Patch                    |                    Custom Dataset Patch                    |\n| :----------------------------------------------------------: | :--------------------------------------------------------: |\n| \u003cimg src=\"adv_patch_gen/media/visdrone_p.png\" width=\"256\" /\u003e | \u003cimg src=\"adv_patch_gen/media/custom_p.png\" width=\"256\" /\u003e |\n\n## 1. Setup\n\nNote: Install all required dependencies as mentioned in the main YOLOv5 repository and install additional yolov5 adversarial dependency as follows:\n\n```shell\npip install -r requirements.txt  # yolov5 reqs\npip install -r adv_patch_gen/requirements.txt\n```\n\nDetailed instructions for setup and docker use at [adv_patch_gen/README.md](adv_patch_gen/README.md)\n\n## 2. Download VisDrone Dataset\n\nDownload Task 1 trainset, valset, and testset-dev sets from \u003chttps://github.com/VisDrone/VisDrone-Dataset\u003e. Place data under `data/visdrone_data`.\n\n## 3. Convert VisDrone Dataset to YOLO format\n\nRefer to [adv_patch_gen/conv_visdrone_2_yolo/README.md](adv_patch_gen/conv_visdrone_2_yolo/README.md)\n\n## 4. Download YOLOv5 4 Class (Car,Van,Truck,Bus) detection weights trained on VisDrone-2019\n\n```shell\n# inside a python virtual environment\npip install gdown\ngdown 1LOlHeKz6G8ZEBVBaej_s05nUvnDdlHFc\n# unzip models into runs/train directory\nmkdir -p runs/train\nunzip coco_e300_4Class_Vehicle.zip -d runs/train\n```\n\n## 5. Train an adversarial patch against the detector\n\nDataset paths should be correctly set in the config json file.\n\n```shell\npython train_patch.py --cfg adv_patch_gen/configs/base.json\n```\n\nInstructions in creating the config json file present at [adv_patch_gen/configs/README.md](adv_patch_gen/configs/README.md).\n\n## 6. Test the performance of the adversarial patch\n\n```shell\npython test_patch.py --cfg CONFIG_JSON_FILE -w YOLOV5_TARGET_MODEL_WEIGHTS_PATH -p PATCH_IMG_FILE_PATH --id IMG_DIR_PATH_FOR_TESTING --sd SAVE_DIR_PATH\npython test_patch.py -h  # to get a list of all testing options\n```\n\n## Attack Success Rate of patches tested against the VisDrone-2019 dataset\n\n|            ASR against coco-pretrained \u0026 scratch-trained            |  ASR against increasing detection confidence threshold  |\n| :-----------------------------------------------------------------: | :-----------------------------------------------------: |\n| \u003cimg src=\"adv_patch_gen/media/asr_s_coco_2_s_coco_s_scratch.png\" /\u003e | \u003cimg src=\"adv_patch_gen/media/plot_all_avg_0_76.png\" /\u003e |\n\n\u003cdiv align=\"center\"\u003e\n  \u003cp\u003e\n    \u003ca href=\"https://www.ultralytics.com/events/yolovision\" 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[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)\n\n\u003cdiv\u003e\n    \u003ca href=\"https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml\"\u003e\u003cimg src=\"https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg\" alt=\"YOLOv5 CI\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://zenodo.org/badge/latestdoi/264818686\"\u003e\u003cimg src=\"https://zenodo.org/badge/264818686.svg\" alt=\"YOLOv5 Citation\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://hub.docker.com/r/ultralytics/yolov5\"\u003e\u003cimg src=\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\" alt=\"Docker Pulls\"\u003e\u003c/a\u003e\n    \u003ca href=\"https://discord.com/invite/ultralytics\"\u003e\u003cimg alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord\u0026logoColor=white\u0026label=Discord\u0026color=blue\"\u003e\u003c/a\u003e \u003ca href=\"https://community.ultralytics.com/\"\u003e\u003cimg alt=\"Ultralytics Forums\" src=\"https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com\u0026logo=discourse\u0026label=Forums\u0026color=blue\"\u003e\u003c/a\u003e \u003ca href=\"https://reddit.com/r/ultralytics\"\u003e\u003cimg alt=\"Ultralytics Reddit\" src=\"https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat\u0026logo=reddit\u0026logoColor=white\u0026label=Reddit\u0026color=blue\"\u003e\u003c/a\u003e\n    \u003cbr\u003e\n    \u003ca href=\"https://bit.ly/yolov5-paperspace-notebook\"\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/yolov5/blob/master/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/models/ultralytics/yolov5\"\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\nYOLOv5 🚀 is the world's most loved vision AI, representing \u003ca href=\"https://www.ultralytics.com/\"\u003eUltralytics\u003c/a\u003e open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.\n\nWe hope that the resources here will help you get the most out of YOLOv5. Please browse the YOLOv5 \u003ca href=\"https://docs.ultralytics.com/yolov5/\"\u003eDocs\u003c/a\u003e for details, raise an issue on \u003ca href=\"https://github.com/ultralytics/yolov5/issues/new/choose\"\u003eGitHub\u003c/a\u003e for support, and join our \u003ca href=\"https://discord.com/invite/ultralytics\"\u003eDiscord\u003c/a\u003e community for questions and discussions!\n\nTo request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://github.com/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png\" width=\"2%\" alt=\"Ultralytics GitHub\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\"\u003e\n  \u003ca href=\"https://www.linkedin.com/company/ultralytics/\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png\" width=\"2%\" alt=\"Ultralytics LinkedIn\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\"\u003e\n  \u003ca href=\"https://twitter.com/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png\" width=\"2%\" alt=\"Ultralytics Twitter\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\"\u003e\n  \u003ca href=\"https://youtube.com/ultralytics?sub_confirmation=1\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png\" width=\"2%\" alt=\"Ultralytics YouTube\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\"\u003e\n  \u003ca href=\"https://www.tiktok.com/@ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png\" width=\"2%\" alt=\"Ultralytics TikTok\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\"\u003e\n  \u003ca href=\"https://ultralytics.com/bilibili\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png\" width=\"2%\" alt=\"Ultralytics BiliBili\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"2%\"\u003e\n  \u003ca href=\"https://discord.com/invite/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png\" width=\"2%\" alt=\"Ultralytics Discord\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n\u003c/div\u003e\n\u003cbr\u003e\n\n## \u003cdiv align=\"center\"\u003eYOLO11 🚀 NEW\u003c/div\u003e\n\nWe are excited to unveil the launch of Ultralytics YOLO11 🚀, the latest advancement in our state-of-the-art (SOTA) vision models! Available now at **[GitHub](https://github.com/ultralytics/ultralytics)**, YOLO11 builds on our legacy of speed, precision, and ease of use. Whether you're tackling object detection, image segmentation, or image classification, YOLO11 delivers the performance and versatility needed to excel in diverse applications.\n\nGet started today and unlock the full potential of YOLO11! Visit the [Ultralytics Docs](https://docs.ultralytics.com/) for comprehensive guides and resources:\n\n[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://www.pepy.tech/projects/ultralytics)\n\n```bash\npip install ultralytics\n```\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://www.ultralytics.com/yolo\" target=\"_blank\"\u003e\n  \u003cimg width=\"100%\" src=\"https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n## \u003cdiv align=\"center\"\u003eDocumentation\u003c/div\u003e\n\nSee the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5/) for full documentation on training, testing and deployment. See below for quickstart examples.\n\n\u003cdetails open\u003e\n\u003csummary\u003eInstall\u003c/summary\u003e\n\nClone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python\u003e=3.8.0**](https://www.python.org/) environment, including [**PyTorch\u003e=1.8**](https://pytorch.org/get-started/locally/).\n\n```bash\ngit clone https://github.com/ultralytics/yolov5  # clone\ncd yolov5\npip install -r requirements.txt  # install\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003eInference\u003c/summary\u003e\n\nYOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).\n\n```python\nimport torch\n\n# Model\nmodel = torch.hub.load(\"ultralytics/yolov5\", \"yolov5s\")  # or yolov5n - yolov5x6, custom\n\n# Images\nimg = \"https://ultralytics.com/images/zidane.jpg\"  # or file, Path, PIL, OpenCV, numpy, list\n\n# Inference\nresults = model(img)\n\n# Results\nresults.print()  # or .show(), .save(), .crop(), .pandas(), etc.\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003eInference with detect.py\u003c/summary\u003e\n\n`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.\n\n```bash\npython detect.py --weights yolov5s.pt --source 0                               # webcam\n                                               img.jpg                         # image\n                                               vid.mp4                         # video\n                                               screen                          # screenshot\n                                               path/                           # directory\n                                               list.txt                        # list of images\n                                               list.streams                    # list of streams\n                                               'path/*.jpg'                    # glob\n                                               'https://youtu.be/LNwODJXcvt4'  # YouTube\n                                               'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003eTraining\u003c/summary\u003e\n\nThe commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.\n\n```bash\npython train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  --batch-size 128\n                                                                 yolov5s                    64\n                                                                 yolov5m                    40\n                                                                 yolov5l                    24\n                                                                 yolov5x                    16\n```\n\n\u003cimg width=\"800\" src=\"https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png\"\u003e\n\n\u003c/details\u003e\n\n\u003cdetails open\u003e\n\u003csummary\u003eTutorials\u003c/summary\u003e\n\n- [Train Custom Data](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) 🚀 RECOMMENDED\n- [Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips/) ☘️\n- [Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)\n- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) 🌟 NEW\n- [TFLite, ONNX, CoreML, TensorRT Export](https://docs.ultralytics.com/yolov5/tutorials/model_export/) 🚀\n- [NVIDIA Jetson platform Deployment](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano/) 🌟 NEW\n- [Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)\n- [Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)\n- [Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)\n- [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)\n- [Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)\n- [Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/) 🌟 NEW\n- [Ultralytics HUB to train and deploy YOLO](https://www.ultralytics.com/hub) 🚀 RECOMMENDED\n- [ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)\n- [YOLOv5 with Neural Magic's Deepsparse](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)\n- [Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/) 🌟 NEW\n\n\u003c/details\u003e\n\n## \u003cdiv align=\"center\"\u003eIntegrations\u003c/div\u003e\n\nOur 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 [W\u0026B](https://docs.wandb.ai/guides/integrations/ultralytics/), [Comet](https://bit.ly/yolov8-readme-comet), [Roboflow](https://roboflow.com/?ref=ultralytics) and [OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow.\n\n\u003cbr\u003e\n\u003ca href=\"https://www.ultralytics.com/hub\" target=\"_blank\"\u003e\n\u003cimg width=\"100%\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\" alt=\"Ultralytics active learning integrations\"\u003e\u003c/a\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://www.ultralytics.com/hub\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-ultralytics-hub.png\" width=\"10%\" alt=\"Ultralytics HUB logo\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"15%\" height=\"0\" alt=\"space\"\u003e\n  \u003ca href=\"https://docs.wandb.ai/guides/integrations/ultralytics/\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/partners/logo-wb.png\" width=\"10%\" alt=\"ClearML logo\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"15%\" height=\"0\" alt=\"space\"\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%\" alt=\"Comet ML logo\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"15%\" height=\"0\" alt=\"space\"\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%\" alt=\"NeuralMagic logo\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n|                                                         Ultralytics HUB 🚀                                                         |                                                               W\u0026B                                                               |                                                                       Comet ⭐ NEW                                                                        |                                              Neural Magic                                              |\n| :--------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |\n| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://www.ultralytics.com/hub). Try now! | Track experiments, hyperparameters, and results with [Weights \u0026 Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLOv5 models, resume training, and interactively visualize and debug predictions | Run YOLO11 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://www.ultralytics.com/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://www.ultralytics.com/app-install). Start your journey for **Free** now!\n\n\u003ca align=\"center\" href=\"https://www.ultralytics.com/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\"\u003eWhy YOLOv5\u003c/div\u003e\n\nYOLOv5 has been designed to be super easy to get started and simple to learn. We prioritize real-world results.\n\n\u003cp align=\"left\"\u003e\u003cimg width=\"800\" src=\"https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png\"\u003e\u003c/p\u003e\n\u003cdetails\u003e\n  \u003csummary\u003eYOLOv5-P5 640 Figure\u003c/summary\u003e\n\n\u003cp align=\"left\"\u003e\u003cimg width=\"800\" src=\"https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png\"\u003e\u003c/p\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\n  \u003csummary\u003eFigure Notes\u003c/summary\u003e\n\n- **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.\n- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.\n- **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.\n- **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`\n\n\u003c/details\u003e\n\n### Pretrained Checkpoints\n\n| Model                                                                                           | size\u003cbr\u003e\u003csup\u003e(pixels) | mAP\u003csup\u003eval\u003cbr\u003e50-95 | mAP\u003csup\u003eval\u003cbr\u003e50 | Speed\u003cbr\u003e\u003csup\u003eCPU b1\u003cbr\u003e(ms) | Speed\u003cbr\u003e\u003csup\u003eV100 b1\u003cbr\u003e(ms) | Speed\u003cbr\u003e\u003csup\u003eV100 b32\u003cbr\u003e(ms) | params\u003cbr\u003e\u003csup\u003e(M) | FLOPs\u003cbr\u003e\u003csup\u003e@640 (B) |\n| ----------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |\n| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt)              | 640                   | 28.0                 | 45.7              | **45**                       | **6.3**                       | **0.6**                        | **1.9**            | **4.5**                |\n| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt)              | 640                   | 37.4                 | 56.8              | 98                           | 6.4                           | 0.9                            | 7.2                | 16.5                   |\n| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt)              | 640                   | 45.4                 | 64.1              | 224                          | 8.2                           | 1.7                            | 21.2               | 49.0                   |\n| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt)              | 640                   | 49.0                 | 67.3              | 430                          | 10.1                          | 2.7                            | 46.5               | 109.1                  |\n| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt)              | 640                   | 50.7                 | 68.9              | 766                          | 12.1                          | 4.8                            | 86.7               | 205.7                  |\n|                                                                                                 |                       |                      |                   |                              |                               |                                |                    |                        |\n| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt)            | 1280                  | 36.0                 | 54.4              | 153                          | 8.1                           | 2.1                            | 3.2                | 4.6                    |\n| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt)            | 1280                  | 44.8                 | 63.7              | 385                          | 8.2                           | 3.6                            | 12.6               | 16.8                   |\n| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt)            | 1280                  | 51.3                 | 69.3              | 887                          | 11.1                          | 6.8                            | 35.7               | 50.0                   |\n| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt)            | 1280                  | 53.7                 | 71.3              | 1784                         | 15.8                          | 10.5                           | 76.8               | 111.4                  |\n| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)\u003cbr\u003e+ [TTA] | 1280\u003cbr\u003e1536          | 55.0\u003cbr\u003e**55.8**     | 72.7\u003cbr\u003e**72.7**  | 3136\u003cbr\u003e-                    | 26.2\u003cbr\u003e-                     | 19.4\u003cbr\u003e-                      | 140.7\u003cbr\u003e-         | 209.8\u003cbr\u003e-             |\n\n\u003cdetails\u003e\n  \u003csummary\u003eTable Notes\u003c/summary\u003e\n\n- All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).\n- **mAP\u003csup\u003eval\u003c/sup\u003e** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.\u003cbr\u003eReproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`\n- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.\u003cbr\u003eReproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`\n- **TTA** [Test Time Augmentation](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) includes reflection and scale augmentations.\u003cbr\u003eReproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`\n\n\u003c/details\u003e\n\n## \u003cdiv align=\"center\"\u003eSegmentation\u003c/div\u003e\n\nOur new YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) instance segmentation models are the fastest and most accurate in the world, beating all current [SOTA benchmarks](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco). We've made them super simple to train, validate and deploy. See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and visit our [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart tutorials.\n\n\u003cdetails\u003e\n  \u003csummary\u003eSegmentation Checkpoints\u003c/summary\u003e\n\n\u003cdiv align=\"center\"\u003e\n\u003ca align=\"center\" href=\"https://www.ultralytics.com/yolo\" target=\"_blank\"\u003e\n\u003cimg width=\"800\" src=\"https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\nWe trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) notebooks for easy reproducibility.\n\n| Model                                                                                      | size\u003cbr\u003e\u003csup\u003e(pixels) | mAP\u003csup\u003ebox\u003cbr\u003e50-95 | mAP\u003csup\u003emask\u003cbr\u003e50-95 | Train time\u003cbr\u003e\u003csup\u003e300 epochs\u003cbr\u003eA100 (hours) | Speed\u003cbr\u003e\u003csup\u003eONNX CPU\u003cbr\u003e(ms) | Speed\u003cbr\u003e\u003csup\u003eTRT A100\u003cbr\u003e(ms) | params\u003cbr\u003e\u003csup\u003e(M) | FLOPs\u003cbr\u003e\u003csup\u003e@640 (B) |\n| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | --------------------------------------------- | ------------------------------ | ------------------------------ | ------------------ | ---------------------- |\n| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640                   | 27.6                 | 23.4                  | 80:17                                         | **62.7**                       | **1.2**                        | **2.0**            | **7.1**                |\n| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640                   | 37.6                 | 31.7                  | 88:16                                         | 173.3                          | 1.4                            | 7.6                | 26.4                   |\n| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640                   | 45.0                 | 37.1                  | 108:36                                        | 427.0                          | 2.2                            | 22.0               | 70.8                   |\n| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640                   | 49.0                 | 39.9                  | 66:43 (2x)                                    | 857.4                          | 2.9                            | 47.9               | 147.7                  |\n| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640                   | **50.7**             | **41.4**              | 62:56 (3x)                                    | 1579.2                         | 4.5                            | 88.8               | 265.7                  |\n\n- All checkpoints are trained to 300 epochs with SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at image size 640 and all default settings.\u003cbr\u003eRuns logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official\n- **Accuracy** values are for single-model single-scale on COCO dataset.\u003cbr\u003eReproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`\n- **Speed** averaged over 100 inference images using a [Colab Pro](https://colab.research.google.com/signup) A100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image). \u003cbr\u003eReproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`\n- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. \u003cbr\u003eReproduce by `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n  \u003csummary\u003eSegmentation Usage Examples \u0026nbsp;\u003ca href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e\u003c/summary\u003e\n\n### Train\n\nYOLOv5 segmentation training supports auto-download COCO128-seg segmentation dataset with `--data coco128-seg.yaml` argument and manual download of COCO-segments dataset with `bash data/scripts/get_coco.sh --train --val --segments` and then `python train.py --data coco.yaml`.\n\n```bash\n# Single-GPU\npython segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640\n\n# Multi-GPU DDP\npython -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3\n```\n\n### Val\n\nValidate YOLOv5s-seg mask mAP on COCO dataset:\n\n```bash\nbash data/scripts/get_coco.sh --val --segments  # download COCO val segments split (780MB, 5000 images)\npython segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640  # validate\n```\n\n### Predict\n\nUse pretrained YOLOv5m-seg.pt to predict bus.jpg:\n\n```bash\npython segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg\n```\n\n```python\nmodel = torch.hub.load(\n    \"ultralytics/yolov5\", \"custom\", \"yolov5m-seg.pt\"\n)  # load from PyTorch Hub (WARNING: inference not yet supported)\n```\n\n| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) |\n| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |\n\n### Export\n\nExport YOLOv5s-seg model to ONNX and TensorRT:\n\n```bash\npython export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0\n```\n\n\u003c/details\u003e\n\n## \u003cdiv align=\"center\"\u003eClassification\u003c/div\u003e\n\nYOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation and deployment! See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v6.2) and visit our [YOLOv5 Classification Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) for quickstart tutorials.\n\n\u003cdetails\u003e\n  \u003csummary\u003eClassification Checkpoints\u003c/summary\u003e\n\n\u003cbr\u003e\n\nWe trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) for easy reproducibility.\n\n| Model                                                                                              | size\u003cbr\u003e\u003csup\u003e(pixels) | acc\u003cbr\u003e\u003csup\u003etop1 | acc\u003cbr\u003e\u003csup\u003etop5 | Training\u003cbr\u003e\u003csup\u003e90 epochs\u003cbr\u003e4xA100 (hours) | Speed\u003cbr\u003e\u003csup\u003eONNX CPU\u003cbr\u003e(ms) | Speed\u003cbr\u003e\u003csup\u003eTensorRT V100\u003cbr\u003e(ms) | params\u003cbr\u003e\u003csup\u003e(M) | FLOPs\u003cbr\u003e\u003csup\u003e@224 (B) |\n| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ---------------------- |\n| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt)         | 224                   | 64.6             | 85.4             | 7:59                                         | **3.3**                        | **0.5**                             | **2.5**            | **0.5**                |\n| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt)         | 224                   | 71.5             | 90.2             | 8:09                                         | 6.6                            | 0.6                                 | 5.4                | 1.4                    |\n| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt)         | 224                   | 75.9             | 92.9             | 10:06                                        | 15.5                           | 0.9                                 | 12.9               | 3.9                    |\n| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt)         | 224                   | 78.0             | 94.0             | 11:56                                        | 26.9                           | 1.4                                 | 26.5               | 8.5                    |\n| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt)         | 224                   | **79.0**         | **94.4**         | 15:04                                        | 54.3                           | 1.8                                 | 48.1               | 15.9                   |\n|                                                                                                    |                       |                  |                  |                                              |                                |                                     |                    |                        |\n| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt)               | 224                   | 70.3             | 89.5             | **6:47**                                     | 11.2                           | 0.5                                 | 11.7               | 3.7                    |\n| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt)               | 224                   | 73.9             | 91.8             | 8:33                                         | 20.6                           | 0.9                                 | 21.8               | 7.4                    |\n| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt)               | 224                   | 76.8             | 93.4             | 11:10                                        | 23.4                           | 1.0                                 | 25.6               | 8.5                    |\n| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt)             | 224                   | 78.5             | 94.3             | 17:10                                        | 42.1                           | 1.9                                 | 44.5               | 15.9                   |\n|                                                                                                    |                       |                  |                  |                                              |                                |                                     |                    |                        |\n| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224                   | 75.1             | 92.4             | 13:03                                        | 12.5                           | 1.3                                 | 5.3                | 1.0                    |\n| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224                   | 76.4             | 93.2             | 17:04                                        | 14.9                           | 1.6                                 | 7.8                | 1.5                    |\n| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224                   | 76.6             | 93.4             | 17:10                                        | 15.9                           | 1.6                                 | 9.1                | 1.7                    |\n| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224                   | 77.7             | 94.0             | 19:19                                        | 18.9                           | 1.9                                 | 12.2               | 2.4                    |\n\n\u003cdetails\u003e\n  \u003csummary\u003eTable Notes (click to expand)\u003c/summary\u003e\n\n- All checkpoints are trained to 90 epochs with SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at image size 224 and all default settings.\u003cbr\u003eRuns logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2\n- **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.\u003cbr\u003eReproduce by `python classify/val.py --data ../datasets/imagenet --img 224`\n- **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.\u003cbr\u003eReproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`\n- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. \u003cbr\u003eReproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`\n\n\u003c/details\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n  \u003csummary\u003eClassification Usage Examples \u0026nbsp;\u003ca href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"\u003e\u003c/a\u003e\u003c/summary\u003e\n\n### Train\n\nYOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`.\n\n```bash\n# Single-GPU\npython classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128\n\n# Multi-GPU DDP\npython -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3\n```\n\n### Val\n\nValidate YOLOv5m-cls accuracy on ImageNet-1k dataset:\n\n```bash\nbash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)\npython classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate\n```\n\n### Predict\n\nUse pretrained YOLOv5s-cls.pt to predict bus.jpg:\n\n```bash\npython classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg\n```\n\n```python\nmodel = torch.hub.load(\"ultralytics/yolov5\", \"custom\", \"yolov5s-cls.pt\")  # load from PyTorch Hub\n```\n\n### Export\n\nExport a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT:\n\n```bash\npython export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224\n```\n\n\u003c/details\u003e\n\n## \u003cdiv align=\"center\"\u003eEnvironments\u003c/div\u003e\n\nGet started in seconds with our verified environments. Click each icon below for details.\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://bit.ly/yolov5-paperspace-notebook\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gradient.png\" width=\"10%\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-colab-small.png\" width=\"10%\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://www.kaggle.com/models/ultralytics/yolov5\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-kaggle-small.png\" width=\"10%\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://hub.docker.com/r/ultralytics/yolov5\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-docker-small.png\" width=\"10%\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-aws-small.png\" width=\"10%\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"5%\" alt=\"\" /\u003e\n  \u003ca href=\"https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/\"\u003e\n    \u003cimg src=\"https://github.com/ultralytics/assets/releases/download/v0.0.0/logo-gcp-small.png\" width=\"10%\" /\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n## \u003cdiv align=\"center\"\u003eContribute\u003c/div\u003e\n\nWe love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started, and fill out the [YOLOv5 Survey](https://www.ultralytics.com/survey?utm_source=github\u0026utm_medium=social\u0026utm_campaign=Survey) to send us feedback on your experiences. 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 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\nUltralytics offers two licensing options to accommodate diverse use cases:\n\n- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for more details.\n- **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](https://www.ultralytics.com/license).\n\n## \u003cdiv align=\"center\"\u003eContact\u003c/div\u003e\n\nFor YOLOv5 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues), and join our [Discord](https://discord.com/invite/ultralytics) community for questions and discussions!\n\n\u003cbr\u003e\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://github.com/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png\" width=\"3%\" alt=\"Ultralytics GitHub\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\"\u003e\n  \u003ca href=\"https://www.linkedin.com/company/ultralytics/\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png\" width=\"3%\" alt=\"Ultralytics LinkedIn\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\"\u003e\n  \u003ca href=\"https://twitter.com/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png\" width=\"3%\" alt=\"Ultralytics Twitter\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\"\u003e\n  \u003ca href=\"https://youtube.com/ultralytics?sub_confirmation=1\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png\" width=\"3%\" alt=\"Ultralytics YouTube\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\"\u003e\n  \u003ca href=\"https://www.tiktok.com/@ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png\" width=\"3%\" alt=\"Ultralytics TikTok\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\"\u003e\n  \u003ca href=\"https://ultralytics.com/bilibili\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png\" width=\"3%\" alt=\"Ultralytics BiliBili\"\u003e\u003c/a\u003e\n  \u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png\" width=\"3%\"\u003e\n  \u003ca href=\"https://discord.com/invite/ultralytics\"\u003e\u003cimg src=\"https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png\" width=\"3%\" alt=\"Ultralytics Discord\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSamSamhuns%2Fyolov5_adversarial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FSamSamhuns%2Fyolov5_adversarial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSamSamhuns%2Fyolov5_adversarial/lists"}