Update docs with YOLOv8 banner (#160)

Co-authored-by: Paula Derrenger <107626595+pderrenger@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -0,0 +1,85 @@
+name: 🐛 Bug Report
+# title: " "
+description: Problems with YOLOv8
+labels: [bug, triage]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for submitting a YOLOv8 🐛 Bug Report!
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the [issues](https://github.com/ultralytics/ultralytics/issues) to see if a similar bug report already exists.
+      options:
+        - label: >
+            I have searched the YOLOv8 [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report.
+          required: true
+
+  - type: dropdown
+    attributes:
+      label: YOLOv8 Component
+      description: |
+        Please select the part of YOLOv8 where you found the bug.
+      multiple: true
+      options:
+        - "Training"
+        - "Validation"
+        - "Detection"
+        - "Export"
+        - "PyTorch Hub"
+        - "Multi-GPU"
+        - "Evolution"
+        - "Integrations"
+        - "Other"
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Bug
+      description: Provide console output with error messages and/or screenshots of the bug.
+      placeholder: |
+        💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response.
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Environment
+      description: Please specify the software and hardware you used to produce the bug.
+      placeholder: |
+        - YOLO: YOLOv8 🚀 v6.0-67-g60e42e1 torch 1.9.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)
+        - OS: Ubuntu 20.04
+        - Python: 3.9.0
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Minimal Reproducible Example
+      description: >
+        When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to **reproduce** the problem.
+        This is referred to by community members as creating a [minimal reproducible example](https://stackoverflow.com/help/minimal-reproducible-example).
+      placeholder: |
+        ```
+        # Code to reproduce your issue here
+        ```
+    validations:
+      required: false
+
+  - type: textarea
+    attributes:
+      label: Additional
+      description: Anything else you would like to share?
+
+  - type: checkboxes
+    attributes:
+      label: Are you willing to submit a PR?
+      description: >
+        (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/ultralytics/pulls) (PR) to help improve YOLOv8 for everyone, especially if you have a good understanding of how to implement a fix or feature.
+        See the YOLOv8 [Contributing Guide](https://github.com/ultralytics/ultralytics/blob/master/CONTRIBUTING.md) to get started.
+      options:
+        - label: Yes I'd like to help by submitting a PR!

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,11 @@
+blank_issues_enabled: true
+contact_links:
+  - name: 📄Docs
+    url: https://docs.ultralytics.com/
+    about: Full Ultralytics YOLOv8 Documentation
+  - name: 💬 Forum
+    url: https://community.ultralytics.com/
+    about: Ask on Ultralytics Community Forum
+  - name: Stack Overflow
+    url: https://stackoverflow.com/search?q=YOLOv8
+    about: Ask on Stack Overflow with 'YOLOv8' tag

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -0,0 +1,50 @@
+name: 🚀 Feature Request
+description: Suggest a YOLOv8 idea
+# title: " "
+labels: [enhancement]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for submitting a YOLOv8 🚀 Feature Request!
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the [issues](https://github.com/ultralytics/ultralytics/issues) to see if a similar feature request already exists.
+      options:
+        - label: >
+            I have searched the YOLOv8 [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar feature requests.
+          required: true
+
+  - type: textarea
+    attributes:
+      label: Description
+      description: A short description of your feature.
+      placeholder: |
+        What new feature would you like to see in YOLOv8?
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Use case
+      description: |
+        Describe the use case of your feature request. It will help us understand and prioritize the feature request.
+      placeholder: |
+        How would this feature be used, and who would use it?
+
+  - type: textarea
+    attributes:
+      label: Additional
+      description: Anything else you would like to share?
+
+  - type: checkboxes
+    attributes:
+      label: Are you willing to submit a PR?
+      description: >
+        (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/ultralytics/pulls) (PR) to help improve YOLOv8 for everyone, especially if you have a good understanding of how to implement a fix or feature.
+        See the YOLOv8 [Contributing Guide](https://github.com/ultralytics/ultralytics/blob/master/CONTRIBUTING.md) to get started.
+      options:
+        - label: Yes I'd like to help by submitting a PR!

.github/ISSUE_TEMPLATE/question.yml

@@ -0,0 +1,33 @@
+name: ❓ Question
+description: Ask a YOLOv8 question
+# title: " "
+labels: [question]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for asking a YOLOv8 ❓ Question!
+
+  - type: checkboxes
+    attributes:
+      label: Search before asking
+      description: >
+        Please search the [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/ultralytics/ultralytics/discussions) to see if a similar question already exists.
+      options:
+        - label: >
+            I have searched the YOLOv8 [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/ultralytics/ultralytics/discussions) and found no similar questions.
+          required: true
+
+  - type: textarea
+    attributes:
+      label: Question
+      description: What is your question?
+      placeholder: |
+        💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response.
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Additional
+      description: Anything else you would like to share?

CONTRIBUTING.md

@@ -56,13 +56,13 @@ To allow your work to be integrated as seamlessly as possible, we advise you to:
 
 - ✅ 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
-  
+
 ### Docstrings
 
 Not all functions or classes require docstrings but when they do, we follow [google-stlye docstrings format](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings). Here is an example:
 
 ```python
-'''
+"""
    What the function does - performs nms on given detection predictions
 
     Args:
@@ -74,7 +74,7 @@ Not all functions or classes require docstrings but when they do, we follow [goo
 
     Raises:
         Exception Class: When and why this exception can be raised by the function.
-'''
+"""
 ```
 
 ## Submitting a Bug Report 🐛

docs/app.md

@@ -0,0 +1,23 @@
+# Ultralytics HUB App for YOLOv8
+
+<div align="center">
+  <a href="https://ultralytics.com/app_install" target="_blank">
+    <img width="1024" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-app.png"></a>
+<br>
+</div>
+
+Welcome to the Ultralytics HUB app for demonstrating YOLOv5 and YOLOv8 models! In this app, available on the [Apple App
+Store](https://apps.apple.com/xk/app/ultralytics/id1583935240) and the [Google Play Store](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app), you will be able to see the power and capabilities of YOLOv5, a state-of-the-art object
+detection model developed by Ultralytics. 
+
+**To install simply scan the QR code above**. The App currently features YOLOv5 models, with YOLOv8 models coming soon.
+
+With YOLOv5, you can detect and classify objects in images and videos with high accuracy and speed. The model has been
+trained on a large dataset and is able to detect a wide range of objects, including cars, pedestrians, and traffic
+signs.
+
+In this app, you will be able to try out YOLOv5 on your own images and videos, and see the model in action. You can also
+learn more about how YOLOv5 works and how it can be used in real-world applications.
+
+We hope you enjoy using YOLOv5 and seeing its capabilities firsthand. Thank you for choosing Ultralytics for your object
+detection needs!

docs/engine.md

@@ -10,7 +10,7 @@ More details and source code can be found in [`BaseTrainer` Reference](../refere
 ## DetectionTrainer
 Here's how you can use the YOLOv8 `DetectionTrainer` and customize it.
 ```python
-from Ultrlaytics.yolo.v8 import DetectionTrainer
+from ultralytics.yolo.v8 import DetectionTrainer
 
 trainer = DetectionTrainer(overrides={...})
 trainer.train()
@@ -20,7 +20,7 @@ trained_model = trainer.best # get best model
 ### Customizing the DetectionTrainer
 Let's customize the trainer **to train a custom detection model** that is not supported directly. You can do this by simply overloading the existing the `get_model` functionality:
 ```python
-from Ultrlaytics.yolo.v8 import DetectionTrainer
+from ultralytics.yolo.v8 import DetectionTrainer
 
 class CustomTrainer(DetectionTrainer):
     def get_model(self, cfg, weights):
@@ -36,7 +36,7 @@ You now realize that you need to customize the trainer further to:
 Here's how you can do it:
 
 ```python
-from Ultrlaytics.yolo.v8 import DetectionTrainer
+from ultralytics.yolo.v8 import DetectionTrainer
 
 class CustomTrainer(DetectionTrainer):
     def get_model(self, cfg, weights):

docs/index.md

@@ -1,17 +1,18 @@
 <div align="center">
-  <a href="https://ultralytics.com/yolov5" target="_blank">
-    <img width="1024" src="https://user-images.githubusercontent.com/26833433/210431393-39c997b8-92a7-4957-864f-1f312004eb54.png"></a>
+  <a href="https://github.com/ultralytics/ultralytics" target="_blank">
+    <img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
 <br>
   <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
   <a href="https://colab.research.google.com/github/glenn-jocher/glenn-jocher.github.io/blob/main/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
   <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
 <br>
+<br>
 </div>
 
 # Welcome to Ultralytics YOLOv8
 
-Welcome to the Ultralytics YOLOv8 documentation landing page! Ultralytics YOLOv8 is the latest version of the YOLO (You
-Only Look Once) object detection and image segmentation model developed by Ultralytics. This page serves as the starting
+Welcome to the Ultralytics YOLOv8 documentation landing page! [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of the YOLO (You
+Only Look Once) object detection and image segmentation model developed by [Ultralytics](https://ultralytics.com). This page serves as the starting
 point for exploring the various resources available to help you get started with YOLOv8 and understand its features and
 capabilities.
 
@@ -20,10 +21,9 @@ object detection and image segmentation tasks. It can be trained on large datase
 variety of hardware platforms, from CPUs to GPUs.
 
 Whether you are a seasoned machine learning practitioner or new to the field, we hope that the resources on this page
-will help you get the most out of YOLOv8. Please feel free to browse the documentation and reach out to us with any
-questions or feedback.
+will help you get the most out of YOLOv8. For any bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). For professional support please [Contact Us](https://ultralytics.com/contact).
 
-### A Brief History of YOLO
+## A Brief History of YOLO
 
 YOLO (You Only Look Once) is a popular object detection and image segmentation model developed by Joseph Redmon and Ali
 Farhadi at the University of Washington. The first version of YOLO was released in 2015 and quickly gained popularity
@@ -36,7 +36,7 @@ backbone network, adding a feature pyramid, and making use of focal loss.
 In 2020, YOLOv4 was released which introduced a number of innovations such as the use of Mosaic data augmentation, a new
 anchor-free detection head, and a new loss function.
 
-In 2021, Ultralytics released YOLOv5, which further improved the model's performance and added new features such as
+In 2021, Ultralytics released [YOLOv5](https://github.com/ultralytics/yolov5), which further improved the model's performance and added new features such as
 support for panoptic segmentation and object tracking.
 
 YOLO has been widely used in a variety of applications, including autonomous vehicles, security and surveillance, and
@@ -49,9 +49,9 @@ For more information about the history and development of YOLO, you can refer to
   conference on computer vision and pattern recognition (pp. 779-788).
 - Redmon, J., & Farhadi, A. (2016). YOLO9000: Better, faster, stronger. In Proceedings
 
-### Ultralytics YOLOv8
+## Ultralytics YOLOv8
 
-YOLOv8 is the latest version of the YOLO object detection and image segmentation model developed by
+[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of the YOLO object detection and image segmentation model developed by
 Ultralytics. YOLOv8 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.
 
@@ -66,4 +66,4 @@ detection head, and a new loss function. YOLOv8 is also highly efficient and can
 platforms, from CPUs to GPUs.
 
 Overall, YOLOv8 is a powerful and flexible tool for object detection and image segmentation that offers the best of both
-worlds: the latest SOTA technology and the ability to use and compare all previous YOLO versions.
+worlds: the latest SOTA technology and the ability to use and compare all previous YOLO versions.

docs/quickstart.md

@@ -1,9 +1,12 @@
 ## Installation
-!!! note "Latest Stable Release"
+
+Install YOLOv8 via the `ultralytics` pip package for the latest stable release or by cloning the [https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) repository for the most up-to-date version.
+
+!!! note "pip install (recommended)"
     ```
     pip install ultralytics
     ```
-??? tip "Development and Contributing"
+!!! note "git clone"
     ```
     git clone https://github.com/ultralytics/ultralytics
     cd ultralytics
@@ -13,38 +16,41 @@
 
 
 ## CLI
-The command line YOLO interface let's you simply train, validate or infer models on various tasks and versions.
-CLI requires no customization or code. You can simply run all tasks from the terminal
-!!! tip
+The command line YOLO interface lets you simply train, validate or infer models on various tasks and versions.
+CLI requires no customization or code. You can simply run all tasks from the terminal with the `yolo` command.
+!!! note
     === "Syntax"
         ```bash
-        yolo task=detect    mode=train  model=s.yaml    epochs=1 ...
-                   ...             ...          ...
-                 segment          infer       s-cls.pt
-                 classify         val         s-seg.pt
+        yolo task=detect    mode=train    model=yolov8n.yaml      args...
+                  classify       predict        yolov8n-cls.yaml  args...
+                  segment        val            yolov8n-seg.yaml  args...
+                                 export         yolov8n.pt        format=onnx  args...
         ```
 
     === "Example training"
         ```bash
-        yolo task=detect mode=train model=s.yaml 
+        yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml device=0
         ```
-        TODO:  add terminal screen/gif
-    === "Example training DDP"
+    === "Example Multi-GPU training"
         ```bash
-        yolo task=detect mode=train model=s.yaml device=\'0,1,2,3\'
+        yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml device=\'0,1,2,3\'
         ```
 [CLI Guide](cli.md){ .md-button .md-button--primary}
 
 ## Python API
-Ultralytics YOLO comes with pythonic Model and Trainer interface. 
-!!! tip
+The Python API allows users to easily use YOLOv8 in their Python projects. It provides functions for loading and running the model, as well as for processing the model's output. The interface is designed to be easy to use, so that users can quickly implement object detection in their projects.
+
+Overall, the Python interface is a useful tool for anyone looking to incorporate object detection, segmentation or classification into their Python projects using YOLOv8.
+
+!!! note
     ```python
-    import ultralytics
     from ultralytics import YOLO
 
-    model = YOLO("yolov8n-seg.yaml") # automatically detects task type
-    model = YOLO("yolov8n.pt") # load checkpoint
-    model.train(data="coco128-seg.yaml", epochs=1, lr0=0.01, ...)
-    model.train(data="coco128-seg.yaml", epochs=1, lr0=0.01, device="0,1,2,3") # DDP mode
+    model = YOLO('yolov8n.yaml')                # build a new model from scratch
+    model = YOLO('yolov8n.pt')                  # load a pretrained model (recommended for best training results)
+    results = model.train(data='coco128.yaml')  # train the model
+    results = model.val()                       # evaluate model performance on the validation set
+    results = model.predict(source='bus.jpg')   # predict on an image
+    success = model.export(format='onnx')       # export the model to ONNX format
     ```
 [API Guide](sdk.md){ .md-button .md-button--primary}

mkdocs.yml

@@ -4,7 +4,7 @@ repo_name: Ultralytics
 
 theme:
   name: "material"
-  logo: assets/logo.png
+  logo: https://github.com/ultralytics/assets/raw/main/logo/Ultralytics-logomark-white.png
   icon:
     repo: fontawesome/brands/github
     admonition:
@@ -82,6 +82,7 @@ nav:
   - Python Interface: sdk.md
   - Configuration: config.md
   - Customization Guide: engine.md
+  - iOS and Android App: app.md
   - Reference:
       - Python Model interface: reference/model.md
       - Engine:

ultralytics/hub/utils.py

@@ -72,7 +72,7 @@ def split_key(key=''):
     return api_key, model_id
 
 
-def smart_request(*args, retry=3, timeout=30, thread=True, code=-1, method="post", **kwargs):
+def smart_request(*args, retry=3, timeout=30, thread=True, code=-1, method="post", verbose=True, **kwargs):
     """
     Makes an HTTP request using the 'requests' library, with exponential backoff retries up to a specified timeout.
 
@@ -83,6 +83,7 @@ def smart_request(*args, retry=3, timeout=30, thread=True, code=-1, method="post
         thread (bool, optional): Whether to execute the request in a separate daemon thread. Default is True.
         code (int, optional): An identifier for the request, used for logging purposes. Default is -1.
         method (str, optional): The HTTP method to use for the request. Choices are 'post' and 'get'. Default is 'post'.
+        verbose (bool, optional): A flag to determine whether to print out to console or not. Default is True.
         **kwargs: Keyword arguments to be passed to the requests function specified in method.
 
     Returns:
@@ -111,7 +112,8 @@ def smart_request(*args, retry=3, timeout=30, thread=True, code=-1, method="post
                     h = r.headers  # response headers
                     m = f"Rate limit reached ({h['X-RateLimit-Remaining']}/{h['X-RateLimit-Limit']}). " \
                         f"Please retry after {h['Retry-After']}s."
-                LOGGER.warning(f"{PREFIX}{m} {HELP_MSG} ({r.status_code} #{code})")
+                if verbose:
+                    LOGGER.warning(f"{PREFIX}{m} {HELP_MSG} ({r.status_code} #{code})")
                 if r.status_code not in retry_codes:
                     return r
             time.sleep(2 ** i)  # exponential standoff
@@ -139,4 +141,4 @@ def sync_analytics(cfg, all_keys=False, enabled=False):
         cfg['uuid'] = SETTINGS['uuid']  # add the device UUID to the configuration data
 
         # Send a request to the HUB API to sync the analytics data
-        smart_request(f'{HUB_API_ROOT}/v1/usage/anonymous', data=cfg, headers=None, code=3, retry=0)
+        smart_request(f'{HUB_API_ROOT}/v1/usage/anonymous', data=cfg, headers=None, code=3, retry=0, verbose=False)