Update header line in Python files (#13072)

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* Auto-format by https://ultralytics.com/actions

* Update ImageNet1000.yaml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Auto-format by https://ultralytics.com/actions

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Ultralytics AssistantandGlenn Jocher authored and GitHub committed 2024-06-08 22:29:29 +02:00
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
name: 🐛 Bug Report
# title: " "
description: Problems with YOLOv5
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
blank_issues_enabled: true
contact_links:
- name: 📄 Docs
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
name: 🚀 Feature Request
description: Suggest a YOLOv5 idea
# title: " "
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
name: ❓ Question
description: Ask a YOLOv5 question
# title: " "
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# Ultralytics YOLO 🚀, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Dependabot for package version updates
# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# YOLOv5 Continuous Integration (CI) GitHub Actions tests
name: YOLOv5 CI
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# Ultralytics YOLO 🚀, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Ultralytics Contributor License Agreement (CLA) action https://docs.ultralytics.com/help/CLA
# This workflow automatically requests Pull Requests (PR) authors to sign the Ultralytics CLA before PRs can be merged
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# This action runs GitHub's industry-leading static analysis engine, CodeQL, against a repository's source code to find security vulnerabilities.
# https://github.com/github/codeql-action
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Builds ultralytics/yolov5:latest images on DockerHub https://hub.docker.com/r/ultralytics/yolov5
name: Publish Docker Images
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# Ultralytics 🚀 - AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Ultralytics Actions https://github.com/ultralytics/actions
# This workflow automatically formats code and documentation in PRs to official Ultralytics standards
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
name: Greetings
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# Ultralytics YOLO 🚀, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Continuous Integration (CI) GitHub Actions tests broken link checker using https://github.com/lycheeverse/lychee
# Ignores the following status codes to reduce false positives:
# - 403(OpenVINO, 'forbidden')
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# Ultralytics YOLO 🚀, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Automatically merges repository 'main' branch into all open PRs to keep them up-to-date
# Action runs on updates to main branch so when one PR merges to main all others update
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
name: Close stale issues
on:
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
Run YOLOv5 benchmarks on all supported export formats.
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
Train a YOLOv5 classifier model on a classification dataset.
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
Validate a trained YOLOv5 classification model on a classification dataset.
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
# Example usage: python train.py --data Argoverse.yaml
# parent
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
# Example usage: python train.py --data GlobalWheat2020.yaml
# parent
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
# Example usage: python classify/train.py --data imagenet
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
# Example usage: python classify/train.py --data imagenet
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
# Example usage: python classify/train.py --data imagenet
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
# Example usage: python classify/train.py --data imagenet
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Objects365 dataset https://www.objects365.org/ by Megvii
# Example usage: python train.py --data Objects365.yaml
# parent
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
# Example usage: python train.py --data SKU-110K.yaml
# parent
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
# Example usage: python train.py --data VOC.yaml
# parent
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
# Example usage: python train.py --data VisDrone.yaml
# parent
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# COCO 2017 dataset http://cocodataset.org by Microsoft
# Example usage: python train.py --data coco.yaml
# parent
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Example usage: python train.py --data coco128.yaml
# parent
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Example usage: python train.py --data coco128.yaml
# parent
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Hyperparameters for Objects365 training
# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Hyperparameters for VOC training
# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve
# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Hyperparameters when using Albumentations frameworks
# python train.py --hyp hyp.no-augmentation.yaml
# See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Hyperparameters for high-augmentation COCO training from scratch
# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Hyperparameters for low-augmentation COCO training from scratch
# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Hyperparameters for medium-augmentation COCO training from scratch
# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
# Example usage: python train.py --data xView.yaml
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Common modules."""
import ast
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Experimental modules."""
import math
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Default anchors for COCO data
# P5 -------------------------------------------------------------------------------------------------------------------
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
TensorFlow, Keras and TFLite versions of YOLOv5
Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
YOLO-specific modules.
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Parameters
nc: 80 # number of classes
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# Ultralyticsv5 YOLO 🚀, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Overview:
# This pyproject.toml file manages the build, packaging, and distribution of the Ultralytics library.
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
Run YOLOv5 segmentation inference on images, videos, directories, streams, etc.
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
Train a YOLOv5 segment model on a segment dataset Models and datasets download automatically from the latest YOLOv5
release.
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
Validate a trained YOLOv5 segment model on a segment dataset.
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""
Train a YOLOv5 model on a custom dataset. Models and datasets download automatically from the latest YOLOv5 release.
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""utils/initialization."""
import contextlib
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Activation functions."""
import torch
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Image augmentation functions."""
import math
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""AutoAnchor utils."""
import random
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Auto-batch utils."""
from copy import deepcopy
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# Resume all interrupted trainings in yolov5/ dir including DDP trainings
# Usage: $ python utils/aws/resume.py
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Callback utils."""
import threading
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Dataloaders and dataset utils."""
import contextlib
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Download utils."""
import logging
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Perform test request."""
import pprint
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Run a Flask REST API exposing one or more YOLOv5s models."""
import argparse
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""General utils."""
import contextlib
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
runtime: custom
env: flex
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Logging utils."""
import json
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Main Logger class for ClearML experiment tracking."""
import glob
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
from clearml import Task
# Connecting ClearML with the current process,
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
import glob
import json
import logging
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
import logging
import os
from urllib.parse import urlparse
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
import argparse
import json
import logging
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
# WARNING ⚠️ wandb is deprecated and will be removed in future release.
# See supported integrations at https://github.com/ultralytics/yolov5#integrations
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
"""Loss functions."""
import torch
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