Update greetings.yml (#11287)

* Update greeting

* Cleanup README

* Created using Colaboratory

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Created using Colaboratory

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.github/workflows/greetings.yml

@@ -23,7 +23,7 @@ jobs:
             - ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_  — Bruce Lee
 
           issue-message: |
-            👋 Hello @${{ github.actor }}, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ [Tutorials](https://github.com/ultralytics/yolov5/wiki#tutorials) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) all the way to advanced concepts like [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607).
+            👋 Hello @${{ github.actor }}, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ [Tutorials](https://docs.ultralytics.com/yolov5/#tutorials) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://docs.ultralytics.com/yolov5/train_custom_data/) all the way to advanced concepts like [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/hyp_evolution/).
 
             If this is a 🐛 Bug Report, please provide a **minimum reproducible example** to help us debug it.
 

README.md

@@ -165,7 +165,7 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  -
 - [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/hyp_evolution)
 - [Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/transfer_learn_frozen)
 - [Architecture Summary](https://docs.ultralytics.com/yolov5/architecture) 🌟 NEW
-- [Roboflow for Datasets, Labeling, and Active Learning](https://docs.ultralytics.com/yolov5/roboflow)
+- [Roboflow for Datasets](https://docs.ultralytics.com/yolov5/roboflow)
 - [ClearML Logging](https://docs.ultralytics.com/yolov5/clearml) 🌟 NEW
 - [YOLOv5 with Neural Magic's Deepsparse](https://docs.ultralytics.com/yolov5/neural_magic) 🌟 NEW
 - [Comet Logging](https://docs.ultralytics.com/yolov5/comet) 🌟 NEW

README.zh-CN.md

@@ -159,7 +159,7 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  -
 - [超参数进化](https://docs.ultralytics.com/yolov5/hyp_evolution)
 - [冻结层的迁移学习](https://docs.ultralytics.com/yolov5/transfer_learn_frozen)
 - [架构概述](https://docs.ultralytics.com/yolov5/architecture) 🌟 新
-- [Roboflow 用于数据集、标签和主动学习](https://docs.ultralytics.com/yolov5/roboflow)
+- [Roboflow](https://docs.ultralytics.com/yolov5/roboflow)
 - [ClearML 日志记录](https://docs.ultralytics.com/yolov5/clearml) 🌟 新
 - [YOLOv5 与 Neural Magic 的 Deepsparse](https://docs.ultralytics.com/yolov5/neural_magic) 🌟 新
 - [Comet 日志记录](https://docs.ultralytics.com/yolov5/comet) 🌟 新

requirements.txt

@@ -46,5 +46,4 @@ setuptools>=65.5.1 # Snyk vulnerability fix
 # mss  # screenshots
 # albumentations>=1.0.3
 # pycocotools>=2.0.6  # COCO mAP
-# roboflow
 # ultralytics  # HUB https://hub.ultralytics.com

tutorial.ipynb

@@ -632,19 +632,13 @@
         "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n",
         "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n",
         "- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n",
-        "<br><br>\n",
+        "<br>\n",
         "\n",
         "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n",
         "\n",
-        "## Train on Custom Data with Roboflow 🌟 NEW\n",
-        "\n",
-        "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n",
-        "\n",
-        "- Custom Training Example: [https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/](https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/?ref=ultralytics)\n",
-        "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/yolov5-custom-training-tutorial/blob/main/yolov5-custom-training.ipynb)\n",
-        "<br>\n",
+        "## Label a dataset on Roboflow (optional)\n",
         "\n",
-        "<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"480\" src=\"https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a275ad4b4ac20cd2e21a_roboflow-annotate.gif\"/></a></p>Label images lightning fast (including with model-assisted labeling)"
+        "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package."
       ]
     },
     {