+ - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)
+ - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)
+ - **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) 
+
+ ## Status
+
+ 
+
+ If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml
deleted file mode 100644
index 212211d24..000000000
--- a/.github/workflows/greetings.yml
+++ /dev/null
@@ -1,65 +0,0 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
-
-name: Greetings
-
-on:
- pull_request_target:
- types: [opened]
- issues:
- types: [opened]
-
-jobs:
- greeting:
- runs-on: ubuntu-latest
- steps:
- - uses: actions/first-interaction@v1
- with:
- repo-token: ${{ secrets.GITHUB_TOKEN }}
- pr-message: |
- 👋 Hello @${{ github.actor }}, thank you for submitting a YOLOv5 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
-
- - ✅ Verify your PR is **up-to-date** with `ultralytics/yolov5` `master` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally.
- - ✅ Verify all YOLOv5 Continuous Integration (CI) **checks are passing**.
- - ✅ 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://docs.ultralytics.com/yolov5/) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) all the way to advanced concepts like [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/).
-
- If this is a 🐛 Bug Report, please provide a **minimum reproducible example** to help us debug it.
-
- If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our [Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips//).
-
- ## Requirements
-
- [**Python>=3.8.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). To get started:
- ```bash
- git clone https://github.com/ultralytics/yolov5 # clone
- cd yolov5
- pip install -r requirements.txt # install
- ```
-
- ## Environments
-
- YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
-
- - **Notebooks** with free GPU:
- - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)
- - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)
- - **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/)
-
- ## Status
-
-
-
- If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
-
- ## Introducing YOLOv8 🚀
-
- We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - [YOLOv8](https://github.com/ultralytics/ultralytics) 🚀!
-
- Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.
-
- Check out our [YOLOv8 Docs](https://docs.ultralytics.com/) for details and get started with:
- ```bash
- pip install ultralytics
- ```
diff --git a/.github/workflows/links.yml b/.github/workflows/links.yml
index 82c1f3f23..2da3c066f 100644
--- a/.github/workflows/links.yml
+++ b/.github/workflows/links.yml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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')
@@ -23,17 +24,15 @@ jobs:
- name: Download and install lychee
run: |
LYCHEE_URL=$(curl -s https://api.github.com/repos/lycheeverse/lychee/releases/latest | grep "browser_download_url" | grep "x86_64-unknown-linux-gnu.tar.gz" | cut -d '"' -f 4)
- curl -L $LYCHEE_URL -o lychee.tar.gz
- tar xzf lychee.tar.gz
- sudo mv lychee /usr/local/bin
+ curl -L $LYCHEE_URL | tar xz -C /usr/local/bin
- name: Test Markdown and HTML links with retry
- uses: nick-invision/retry@v3
+ uses: ultralytics/actions/retry@main
with:
timeout_minutes: 5
- retry_wait_seconds: 60
- max_attempts: 3
- command: |
+ retry_delay_seconds: 60
+ retries: 2
+ run: |
lychee \
--scheme 'https' \
--timeout 60 \
@@ -45,16 +44,16 @@ jobs:
--github-token ${{ secrets.GITHUB_TOKEN }} \
--header "User-Agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.6478.183 Safari/537.36" \
'./**/*.md' \
- './**/*.html'
+ './**/*.html' | tee -a $GITHUB_STEP_SUMMARY
- name: Test Markdown, HTML, YAML, Python and Notebook links with retry
if: github.event_name == 'workflow_dispatch'
- uses: nick-invision/retry@v3
+ uses: ultralytics/actions/retry@main
with:
timeout_minutes: 5
- retry_wait_seconds: 60
- max_attempts: 3
- command: |
+ retry_delay_seconds: 60
+ retries: 2
+ run: |
lychee \
--scheme 'https' \
--timeout 60 \
@@ -70,4 +69,4 @@ jobs:
'./**/*.yml' \
'./**/*.yaml' \
'./**/*.py' \
- './**/*.ipynb'
+ './**/*.ipynb' | tee -a $GITHUB_STEP_SUMMARY
diff --git a/.github/workflows/merge-main-into-prs.yml b/.github/workflows/merge-main-into-prs.yml
index 2cd4b028c..034c6c143 100644
--- a/.github/workflows/merge-main-into-prs.yml
+++ b/.github/workflows/merge-main-into-prs.yml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
@@ -6,10 +7,9 @@ name: Merge main into PRs
on:
workflow_dispatch:
- push:
- branches:
- - main
- - master
+ # push:
+ # branches:
+ # - ${{ github.event.repository.default_branch }}
jobs:
Merge:
@@ -22,35 +22,51 @@ jobs:
fetch-depth: 0
- uses: actions/setup-python@v5
with:
- python-version: "3.11"
- cache: "pip" # caching pip dependencies
+ python-version: "3.x"
+ cache: "pip"
- name: Install requirements
run: |
pip install pygithub
- - name: Merge main into PRs
+ - name: Merge default branch into PRs
shell: python
run: |
from github import Github
import os
- # Authenticate with the GitHub Token
g = Github(os.getenv('GITHUB_TOKEN'))
-
- # Get the repository dynamically
repo = g.get_repo(os.getenv('GITHUB_REPOSITORY'))
- # List all open pull requests
- open_pulls = repo.get_pulls(state='open', sort='created')
+ # Fetch the default branch name
+ default_branch_name = repo.default_branch
+ default_branch = repo.get_branch(default_branch_name)
- for pr in open_pulls:
- # Compare PR head with main to see if it's behind
+ for pr in repo.get_pulls(state='open', sort='created'):
try:
- # Merge main into the PR branch
- success = pr.update_branch()
- assert success, "Branch update failed"
- print(f"Merged 'master' into PR #{pr.number} ({pr.head.ref}) successfully.")
+ # Get full names for repositories and branches
+ base_repo_name = repo.full_name
+ head_repo_name = pr.head.repo.full_name
+ base_branch_name = pr.base.ref
+ head_branch_name = pr.head.ref
+
+ # Check if PR is behind the default branch
+ comparison = repo.compare(default_branch.commit.sha, pr.head.sha)
+
+ if comparison.behind_by > 0:
+ print(f"⚠️ PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}) is behind {default_branch_name} by {comparison.behind_by} commit(s).")
+
+ # Attempt to update the branch
+ try:
+ success = pr.update_branch()
+ assert success, "Branch update failed"
+ print(f"✅ Successfully merged '{default_branch_name}' into PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}).")
+ except Exception as update_error:
+ print(f"❌ Could not update PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}): {update_error}")
+ print(" This might be due to branch protection rules or insufficient permissions.")
+ else:
+ print(f"✅ PR #{pr.number} ({head_repo_name}:{head_branch_name} -> {base_repo_name}:{base_branch_name}) is up to date with {default_branch_name}.")
except Exception as e:
- print(f"Could not merge 'master' into PR #{pr.number} ({pr.head.ref}): {e}")
+ print(f"❌ Could not process PR #{pr.number}: {e}")
+
env:
- GITHUB_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
+ GITHUB_TOKEN: ${{ secrets._GITHUB_TOKEN }}
GITHUB_REPOSITORY: ${{ github.repository }}
diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml
index 374bc01ab..fda092a44 100644
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
name: Close stale issues
on:
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 556c554a2..7b9c1cd64 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -64,10 +64,10 @@ When asking a question, people will be better able to provide help if you provid
- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
-In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code should be:
+In addition to the above requirements, for [Ultralytics](https://www.ultralytics.com/) to provide assistance your code should be:
- ✅ **Current** – Verify that your code is up-to-date with the current GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new copy to ensure your problem has not already been resolved by previous commits.
-- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️.
+- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this repository. [Ultralytics](https://www.ultralytics.com/) does not provide support for custom code ⚠️.
If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 **Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) to help us better understand and diagnose your problem.
diff --git a/README.md b/README.md
index ac8b63af8..46b2a833e 100644
--- a/README.md
+++ b/README.md
@@ -1,28 +1,28 @@
-
+
- 
+ 
-
+
@@ -37,32 +37,32 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
- 
+
+
+
## 
-
+
@@ -430,7 +432,7 @@ Get started in seconds with our verified environments. Click each icon below for
##
-
+
-
+
+
-
+
+
+
##
-
+
@@ -429,7 +432,7 @@ python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --inclu
##
-
+
![\"Run](\"https://assets.paperspace.io/img/gradient-badge.svg\"){kind=icon}
\n",
" ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
- " ![\"Open](\"https://kaggle.com/static/images/open-in-kaggle.svg\")
\n",
+ " \n",
" \n",
+ "- **Notebooks** with free GPU: \n",
"- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
"- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
"- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) ![\"Docker](\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\")
\n"
diff --git a/classify/val.py b/classify/val.py
index 8ce48f064..72bd0e14e 100644
--- a/classify/val.py
+++ b/classify/val.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
Validate a trained YOLOv5 classification model on a classification dataset.
diff --git a/data/Argoverse.yaml b/data/Argoverse.yaml
index 366552ea4..651b6431b 100644
--- a/data/Argoverse.yaml
+++ b/data/Argoverse.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/data/GlobalWheat2020.yaml b/data/GlobalWheat2020.yaml
index acb88290f..eb25871c6 100644
--- a/data/GlobalWheat2020.yaml
+++ b/data/GlobalWheat2020.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
# Example usage: python train.py --data GlobalWheat2020.yaml
# parent
diff --git a/data/ImageNet.yaml b/data/ImageNet.yaml
index 979a0e4de..a3cf694bc 100644
--- a/data/ImageNet.yaml
+++ b/data/ImageNet.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/data/ImageNet10.yaml b/data/ImageNet10.yaml
index 2189def7d..e50e58888 100644
--- a/data/ImageNet10.yaml
+++ b/data/ImageNet10.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/data/ImageNet100.yaml b/data/ImageNet100.yaml
index 560cdecdb..e3891bcb4 100644
--- a/data/ImageNet100.yaml
+++ b/data/ImageNet100.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/data/ImageNet1000.yaml b/data/ImageNet1000.yaml
index aa17e9e05..8943d3312 100644
--- a/data/ImageNet1000.yaml
+++ b/data/ImageNet1000.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/data/Objects365.yaml b/data/Objects365.yaml
index f1f0a1ae4..248b6c775 100644
--- a/data/Objects365.yaml
+++ b/data/Objects365.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
# Objects365 dataset https://www.objects365.org/ by Megvii
# Example usage: python train.py --data Objects365.yaml
# parent
diff --git a/data/SKU-110K.yaml b/data/SKU-110K.yaml
index b012bec31..695b89cd4 100644
--- a/data/SKU-110K.yaml
+++ b/data/SKU-110K.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/data/VOC.yaml b/data/VOC.yaml
index 227d91d76..9dad47777 100644
--- a/data/VOC.yaml
+++ b/data/VOC.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/data/VisDrone.yaml b/data/VisDrone.yaml
index 20ff1d39c..637433b50 100644
--- a/data/VisDrone.yaml
+++ b/data/VisDrone.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
# Example usage: python train.py --data VisDrone.yaml
# parent
diff --git a/data/coco.yaml b/data/coco.yaml
index 816efa5cf..7f872e8ca 100644
--- a/data/coco.yaml
+++ b/data/coco.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
# COCO 2017 dataset http://cocodataset.org by Microsoft
# Example usage: python train.py --data coco.yaml
# parent
diff --git a/data/coco128-seg.yaml b/data/coco128-seg.yaml
index aea711c98..fa618d87e 100644
--- a/data/coco128-seg.yaml
+++ b/data/coco128-seg.yaml
@@ -1,5 +1,6 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
-# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Example usage: python train.py --data coco128.yaml
# parent
# ├── yolov5
diff --git a/data/coco128.yaml b/data/coco128.yaml
index 2ed35c06e..e81fb1ff4 100644
--- a/data/coco128.yaml
+++ b/data/coco128.yaml
@@ -1,5 +1,6 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
-# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Example usage: python train.py --data coco128.yaml
# parent
# ├── yolov5
diff --git a/data/hyps/hyp.Objects365.yaml b/data/hyps/hyp.Objects365.yaml
index 7a6c507c7..7b26a053b 100644
--- a/data/hyps/hyp.Objects365.yaml
+++ b/data/hyps/hyp.Objects365.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/data/hyps/hyp.VOC.yaml b/data/hyps/hyp.VOC.yaml
index c04c63e21..378bc4030 100644
--- a/data/hyps/hyp.VOC.yaml
+++ b/data/hyps/hyp.VOC.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/data/hyps/hyp.no-augmentation.yaml b/data/hyps/hyp.no-augmentation.yaml
index adc360bb8..08378961f 100644
--- a/data/hyps/hyp.no-augmentation.yaml
+++ b/data/hyps/hyp.no-augmentation.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/data/hyps/hyp.scratch-high.yaml b/data/hyps/hyp.scratch-high.yaml
index 3e913e36d..74536c297 100644
--- a/data/hyps/hyp.scratch-high.yaml
+++ b/data/hyps/hyp.scratch-high.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/data/hyps/hyp.scratch-low.yaml b/data/hyps/hyp.scratch-low.yaml
index ff0d1e7ff..e89b3ba4e 100644
--- a/data/hyps/hyp.scratch-low.yaml
+++ b/data/hyps/hyp.scratch-low.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/data/hyps/hyp.scratch-med.yaml b/data/hyps/hyp.scratch-med.yaml
index c2fba1fc2..7dfd2f306 100644
--- a/data/hyps/hyp.scratch-med.yaml
+++ b/data/hyps/hyp.scratch-med.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/data/xView.yaml b/data/xView.yaml
index 407159831..6bea7637e 100644
--- a/data/xView.yaml
+++ b/data/xView.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/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
diff --git a/detect.py b/detect.py
index 57d778740..24724a35f 100644
--- a/detect.py
+++ b/detect.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
@@ -219,9 +219,10 @@ def run(
def write_to_csv(image_name, prediction, confidence):
"""Writes prediction data for an image to a CSV file, appending if the file exists."""
data = {"Image Name": image_name, "Prediction": prediction, "Confidence": confidence}
+ file_exists = os.path.isfile(csv_path)
with open(csv_path, mode="a", newline="") as f:
writer = csv.DictWriter(f, fieldnames=data.keys())
- if not csv_path.is_file():
+ if not file_exists:
writer.writeheader()
writer.writerow(data)
@@ -237,7 +238,7 @@ def run(
p = Path(p) # to Path
save_path = str(save_dir / p.name) # im.jpg
txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt
- s += "%gx%g " % im.shape[2:] # print string
+ s += "{:g}x{:g} ".format(*im.shape[2:]) # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
@@ -308,7 +309,7 @@ def run(
vid_writer[i].write(im0)
# Print time (inference-only)
- LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
+ LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1e3:.1f}ms")
# Print results
t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
diff --git a/export.py b/export.py
index f3216a564..e6d18eaa0 100644
--- a/export.py
+++ b/export.py
@@ -1,6 +1,6 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
-Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
+Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit.
Format | `export.py --include` | Model
--- | --- | ---
@@ -91,6 +91,8 @@ MACOS = platform.system() == "Darwin" # macOS environment
class iOSModel(torch.nn.Module):
+ """An iOS-compatible wrapper for YOLOv5 models that normalizes input images based on their dimensions."""
+
def __init__(self, model, im):
"""
Initializes an iOS compatible model with normalization based on image dimensions.
@@ -141,7 +143,7 @@ class iOSModel(torch.nn.Module):
def export_formats():
- """
+ r"""
Returns a DataFrame of supported YOLOv5 model export formats and their properties.
Returns:
@@ -450,8 +452,9 @@ def export_openvino(file, metadata, half, int8, data, prefix=colorstr("OpenVINO:
Extracts and preprocess input data from dataloader item for quantization.
- Parameters:
+
data_item: Tuple with data item produced by DataLoader during iteration
+
Returns:
input_tensor: Input data for quantization
"""
@@ -563,11 +566,7 @@ def export_coreml(model, im, file, int8, half, nms, mlmodel, prefix=colorstr("Co
else:
f = file.with_suffix(".mlpackage")
convert_to = "mlprogram"
- if half:
- precision = ct.precision.FLOAT16
- else:
- precision = ct.precision.FLOAT32
-
+ precision = ct.precision.FLOAT16 if half else ct.precision.FLOAT32
if nms:
model = iOSModel(model, im)
ts = torch.jit.trace(model, im, strict=False) # TorchScript model
@@ -594,7 +593,9 @@ def export_coreml(model, im, file, int8, half, nms, mlmodel, prefix=colorstr("Co
@try_export
-def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr("TensorRT:")):
+def export_engine(
+ model, im, file, half, dynamic, simplify, workspace=4, verbose=False, cache="", prefix=colorstr("TensorRT:")
+):
"""
Export a YOLOv5 model to TensorRT engine format, requiring GPU and TensorRT>=7.0.0.
@@ -607,6 +608,7 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose
simplify (bool): Set to True to simplify the model during export.
workspace (int): Workspace size in GB (default is 4).
verbose (bool): Set to True for verbose logging output.
+ cache (str): Path to save the TensorRT timing cache.
prefix (str): Log message prefix.
Returns:
@@ -661,6 +663,11 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30)
else: # TensorRT versions 7, 8
config.max_workspace_size = workspace * 1 << 30
+ if cache: # enable timing cache
+ Path(cache).parent.mkdir(parents=True, exist_ok=True)
+ buf = Path(cache).read_bytes() if Path(cache).exists() else b""
+ timing_cache = config.create_timing_cache(buf)
+ config.set_timing_cache(timing_cache, ignore_mismatch=True)
flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
network = builder.create_network(flag)
parser = trt.OnnxParser(network, logger)
@@ -689,6 +696,9 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose
build = builder.build_serialized_network if is_trt10 else builder.build_engine
with build(network, config) as engine, open(f, "wb") as t:
t.write(engine if is_trt10 else engine.serialize())
+ if cache: # save timing cache
+ with open(cache, "wb") as c:
+ c.write(config.get_timing_cache().serialize())
return f, None
@@ -1135,11 +1145,7 @@ def pipeline_coreml(model, im, file, names, y, mlmodel, prefix=colorstr("CoreML
import coremltools as ct
from PIL import Image
- if mlmodel:
- f = file.with_suffix(".mlmodel") # filename
- else:
- f = file.with_suffix(".mlpackage") # filename
-
+ f = file.with_suffix(".mlmodel") if mlmodel else file.with_suffix(".mlpackage")
print(f"{prefix} starting pipeline with coremltools {ct.__version__}...")
batch_size, ch, h, w = list(im.shape) # BCHW
t = time.time()
@@ -1183,10 +1189,7 @@ def pipeline_coreml(model, im, file, names, y, mlmodel, prefix=colorstr("CoreML
# Model from spec
weights_dir = None
- if mlmodel:
- weights_dir = None
- else:
- weights_dir = str(f / "Data/com.apple.CoreML/weights")
+ weights_dir = None if mlmodel else str(f / "Data/com.apple.CoreML/weights")
model = ct.models.MLModel(spec, weights_dir=weights_dir)
# 3. Create NMS protobuf
@@ -1285,6 +1288,7 @@ def run(
int8=False, # CoreML/TF INT8 quantization
per_tensor=False, # TF per tensor quantization
dynamic=False, # ONNX/TF/TensorRT: dynamic axes
+ cache="", # TensorRT: timing cache path
simplify=False, # ONNX: simplify model
mlmodel=False, # CoreML: Export in *.mlmodel format
opset=12, # ONNX: opset version
@@ -1314,6 +1318,7 @@ def run(
int8 (bool): Apply INT8 quantization for CoreML or TensorFlow models. Default is False.
per_tensor (bool): Apply per tensor quantization for TensorFlow models. Default is False.
dynamic (bool): Enable dynamic axes for ONNX, TensorFlow, or TensorRT exports. Default is False.
+ cache (str): TensorRT timing cache path. Default is an empty string.
simplify (bool): Simplify the ONNX model during export. Default is False.
opset (int): ONNX opset version. Default is 12.
verbose (bool): Enable verbose logging for TensorRT export. Default is False.
@@ -1349,6 +1354,7 @@ def run(
int8=False,
per_tensor=False,
dynamic=False,
+ cache="",
simplify=False,
opset=12,
verbose=False,
@@ -1386,7 +1392,8 @@ def run(
# Input
gs = int(max(model.stride)) # grid size (max stride)
imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples
- im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection
+ ch = next(model.parameters()).size(1) # require input image channels
+ im = torch.zeros(batch_size, ch, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection
# Update model
model.eval()
@@ -1410,7 +1417,7 @@ def run(
if jit: # TorchScript
f[0], _ = export_torchscript(model, im, file, optimize)
if engine: # TensorRT required before ONNX
- f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose)
+ f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose, cache)
if onnx or xml: # OpenVINO requires ONNX
f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
if xml: # OpenVINO
@@ -1464,12 +1471,12 @@ def run(
else ""
)
LOGGER.info(
- f'\nExport complete ({time.time() - t:.1f}s)'
+ f"\nExport complete ({time.time() - t:.1f}s)"
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
f"\nDetect: python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}"
f"\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}"
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}"
- f'\nVisualize: https://netron.app'
+ f"\nVisualize: https://netron.app"
)
return f # return list of exported files/dirs
@@ -1505,6 +1512,7 @@ def parse_opt(known=False):
parser.add_argument("--int8", action="store_true", help="CoreML/TF/OpenVINO INT8 quantization")
parser.add_argument("--per-tensor", action="store_true", help="TF per-tensor quantization")
parser.add_argument("--dynamic", action="store_true", help="ONNX/TF/TensorRT: dynamic axes")
+ parser.add_argument("--cache", type=str, default="", help="TensorRT: timing cache file path")
parser.add_argument("--simplify", action="store_true", help="ONNX: simplify model")
parser.add_argument("--mlmodel", action="store_true", help="CoreML: Export in *.mlmodel format")
parser.add_argument("--opset", type=int, default=17, help="ONNX: opset version")
diff --git a/hubconf.py b/hubconf.py
index 98e399421..2eeac4edd 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -1,6 +1,6 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
-PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5
+PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5.
Usage:
import torch
diff --git a/models/__init__.py b/models/__init__.py
index e69de29bb..77a19dcf0 100644
--- a/models/__init__.py
+++ b/models/__init__.py
@@ -0,0 +1 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
diff --git a/models/common.py b/models/common.py
index a497627a7..29e4413e3 100644
--- a/models/common.py
+++ b/models/common.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Common modules."""
import ast
@@ -71,7 +71,8 @@ def autopad(k, p=None, d=1):
class Conv(nn.Module):
- # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
+ """Applies a convolution, batch normalization, and activation function to an input tensor in a neural network."""
+
default_act = nn.SiLU() # default activation
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
@@ -91,7 +92,8 @@ class Conv(nn.Module):
class DWConv(Conv):
- # Depth-wise convolution
+ """Implements a depth-wise convolution layer with optional activation for efficient spatial filtering."""
+
def __init__(self, c1, c2, k=1, s=1, d=1, act=True):
"""Initializes a depth-wise convolution layer with optional activation; args: input channels (c1), output
channels (c2), kernel size (k), stride (s), dilation (d), and activation flag (act).
@@ -100,7 +102,8 @@ class DWConv(Conv):
class DWConvTranspose2d(nn.ConvTranspose2d):
- # Depth-wise transpose convolution
+ """A depth-wise transpose convolutional layer for upsampling in neural networks, particularly in YOLOv5 models."""
+
def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):
"""Initializes a depth-wise transpose convolutional layer for YOLOv5; args: input channels (c1), output channels
(c2), kernel size (k), stride (s), input padding (p1), output padding (p2).
@@ -109,7 +112,8 @@ class DWConvTranspose2d(nn.ConvTranspose2d):
class TransformerLayer(nn.Module):
- # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
+ """Transformer layer with multihead attention and linear layers, optimized by removing LayerNorm."""
+
def __init__(self, c, num_heads):
"""
Initializes a transformer layer, sans LayerNorm for performance, with multihead attention and linear layers.
@@ -132,7 +136,8 @@ class TransformerLayer(nn.Module):
class TransformerBlock(nn.Module):
- # Vision Transformer https://arxiv.org/abs/2010.11929
+ """A Transformer block for vision tasks with convolution, position embeddings, and Transformer layers."""
+
def __init__(self, c1, c2, num_heads, num_layers):
"""Initializes a Transformer block for vision tasks, adapting dimensions if necessary and stacking specified
layers.
@@ -157,7 +162,8 @@ class TransformerBlock(nn.Module):
class Bottleneck(nn.Module):
- # Standard bottleneck
+ """A bottleneck layer with optional shortcut and group convolution for efficient feature extraction."""
+
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):
"""Initializes a standard bottleneck layer with optional shortcut and group convolution, supporting channel
expansion.
@@ -176,7 +182,8 @@ class Bottleneck(nn.Module):
class BottleneckCSP(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+ """CSP bottleneck layer for feature extraction with cross-stage partial connections and optional shortcuts."""
+
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
"""Initializes CSP bottleneck with optional shortcuts; args: ch_in, ch_out, number of repeats, shortcut bool,
groups, expansion.
@@ -201,7 +208,8 @@ class BottleneckCSP(nn.Module):
class CrossConv(nn.Module):
- # Cross Convolution Downsample
+ """Implements a cross convolution layer with downsampling, expansion, and optional shortcut."""
+
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
"""
Initializes CrossConv with downsampling, expanding, and optionally shortcutting; `c1` input, `c2` output
@@ -221,7 +229,8 @@ class CrossConv(nn.Module):
class C3(nn.Module):
- # CSP Bottleneck with 3 convolutions
+ """Implements a CSP Bottleneck module with three convolutions for enhanced feature extraction in neural networks."""
+
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
"""Initializes C3 module with options for channel count, bottleneck repetition, shortcut usage, group
convolutions, and expansion.
@@ -239,7 +248,8 @@ class C3(nn.Module):
class C3x(C3):
- # C3 module with cross-convolutions
+ """Extends the C3 module with cross-convolutions for enhanced feature extraction in neural networks."""
+
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
"""Initializes C3x module with cross-convolutions, extending C3 with customizable channel dimensions, groups,
and expansion.
@@ -250,7 +260,8 @@ class C3x(C3):
class C3TR(C3):
- # C3 module with TransformerBlock()
+ """C3 module with TransformerBlock for enhanced feature extraction in object detection models."""
+
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
"""Initializes C3 module with TransformerBlock for enhanced feature extraction, accepts channel sizes, shortcut
config, group, and expansion.
@@ -261,7 +272,8 @@ class C3TR(C3):
class C3SPP(C3):
- # C3 module with SPP()
+ """Extends the C3 module with an SPP layer for enhanced spatial feature extraction and customizable channels."""
+
def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
"""Initializes a C3 module with SPP layer for advanced spatial feature extraction, given channel sizes, kernel
sizes, shortcut, group, and expansion ratio.
@@ -272,7 +284,8 @@ class C3SPP(C3):
class C3Ghost(C3):
- # C3 module with GhostBottleneck()
+ """Implements a C3 module with Ghost Bottlenecks for efficient feature extraction in YOLOv5."""
+
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
"""Initializes YOLOv5's C3 module with Ghost Bottlenecks for efficient feature extraction."""
super().__init__(c1, c2, n, shortcut, g, e)
@@ -281,7 +294,8 @@ class C3Ghost(C3):
class SPP(nn.Module):
- # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
+ """Implements Spatial Pyramid Pooling (SPP) for feature extraction, ref: https://arxiv.org/abs/1406.4729."""
+
def __init__(self, c1, c2, k=(5, 9, 13)):
"""Initializes SPP layer with Spatial Pyramid Pooling, ref: https://arxiv.org/abs/1406.4729, args: c1 (input channels), c2 (output channels), k (kernel sizes)."""
super().__init__()
@@ -301,7 +315,8 @@ class SPP(nn.Module):
class SPPF(nn.Module):
- # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
+ """Implements a fast Spatial Pyramid Pooling (SPPF) layer for efficient feature extraction in YOLOv5 models."""
+
def __init__(self, c1, c2, k=5):
"""
Initializes YOLOv5 SPPF layer with given channels and kernel size for YOLOv5 model, combining convolution and
@@ -326,7 +341,8 @@ class SPPF(nn.Module):
class Focus(nn.Module):
- # Focus wh information into c-space
+ """Focuses spatial information into channel space using slicing and convolution for efficient feature extraction."""
+
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
"""Initializes Focus module to concentrate width-height info into channel space with configurable convolution
parameters.
@@ -342,7 +358,8 @@ class Focus(nn.Module):
class GhostConv(nn.Module):
- # Ghost Convolution https://github.com/huawei-noah/ghostnet
+ """Implements Ghost Convolution for efficient feature extraction, see https://github.com/huawei-noah/ghostnet."""
+
def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
"""Initializes GhostConv with in/out channels, kernel size, stride, groups, and activation; halves out channels
for efficiency.
@@ -359,7 +376,8 @@ class GhostConv(nn.Module):
class GhostBottleneck(nn.Module):
- # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
+ """Efficient bottleneck layer using Ghost Convolutions, see https://github.com/huawei-noah/ghostnet."""
+
def __init__(self, c1, c2, k=3, s=1):
"""Initializes GhostBottleneck with ch_in `c1`, ch_out `c2`, kernel size `k`, stride `s`; see https://github.com/huawei-noah/ghostnet."""
super().__init__()
@@ -379,7 +397,8 @@ class GhostBottleneck(nn.Module):
class Contract(nn.Module):
- # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
+ """Contracts spatial dimensions into channel dimensions for efficient processing in neural networks."""
+
def __init__(self, gain=2):
"""Initializes a layer to contract spatial dimensions (width-height) into channels, e.g., input shape
(1,64,80,80) to (1,256,40,40).
@@ -399,7 +418,8 @@ class Contract(nn.Module):
class Expand(nn.Module):
- # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
+ """Expands spatial dimensions by redistributing channels, e.g., from (1,64,80,80) to (1,16,160,160)."""
+
def __init__(self, gain=2):
"""
Initializes the Expand module to increase spatial dimensions by redistributing channels, with an optional gain
@@ -422,7 +442,8 @@ class Expand(nn.Module):
class Concat(nn.Module):
- # Concatenate a list of tensors along dimension
+ """Concatenates tensors along a specified dimension for efficient tensor manipulation in neural networks."""
+
def __init__(self, dimension=1):
"""Initializes a Concat module to concatenate tensors along a specified dimension."""
super().__init__()
@@ -436,7 +457,8 @@ class Concat(nn.Module):
class DetectMultiBackend(nn.Module):
- # YOLOv5 MultiBackend class for python inference on various backends
+ """YOLOv5 MultiBackend class for inference on various backends including PyTorch, ONNX, TensorRT, and more."""
+
def __init__(self, weights="yolov5s.pt", device=torch.device("cpu"), dnn=False, data=None, fp16=False, fuse=True):
"""Initializes DetectMultiBackend with support for various inference backends, including PyTorch and ONNX."""
# PyTorch: weights = *.pt
@@ -728,6 +750,8 @@ class DetectMultiBackend(nn.Module):
scale, zero_point = output["quantization"]
x = (x.astype(np.float32) - zero_point) * scale # re-scale
y.append(x)
+ if len(y) == 2 and len(y[1].shape) != 4:
+ y = list(reversed(y))
y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels
@@ -778,7 +802,8 @@ class DetectMultiBackend(nn.Module):
class AutoShape(nn.Module):
- # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+ """AutoShape class for robust YOLOv5 inference with preprocessing, NMS, and support for various input formats."""
+
conf = 0.25 # NMS confidence threshold
iou = 0.45 # NMS IoU threshold
agnostic = False # NMS class-agnostic
@@ -889,7 +914,8 @@ class AutoShape(nn.Module):
class Detections:
- # YOLOv5 detections class for inference results
+ """Manages YOLOv5 detection results with methods for visualization, saving, cropping, and exporting detections."""
+
def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
"""Initializes the YOLOv5 Detections class with image info, predictions, filenames, timing and normalization."""
super().__init__()
@@ -1047,7 +1073,8 @@ class Detections:
class Proto(nn.Module):
- # YOLOv5 mask Proto module for segmentation models
+ """YOLOv5 mask Proto module for segmentation models, performing convolutions and upsampling on input tensors."""
+
def __init__(self, c1, c_=256, c2=32):
"""Initializes YOLOv5 Proto module for segmentation with input, proto, and mask channels configuration."""
super().__init__()
@@ -1062,7 +1089,8 @@ class Proto(nn.Module):
class Classify(nn.Module):
- # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2)
+ """YOLOv5 classification head with convolution, pooling, and dropout layers for channel transformation."""
+
def __init__(
self, c1, c2, k=1, s=1, p=None, g=1, dropout_p=0.0
): # ch_in, ch_out, kernel, stride, padding, groups, dropout probability
diff --git a/models/experimental.py b/models/experimental.py
index ab9b0ed23..63d9c461a 100644
--- a/models/experimental.py
+++ b/models/experimental.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Experimental modules."""
import math
diff --git a/models/hub/anchors.yaml b/models/hub/anchors.yaml
index c8089311b..0f3e288e1 100644
--- a/models/hub/anchors.yaml
+++ b/models/hub/anchors.yaml
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
# Default anchors for COCO data
# P5 -------------------------------------------------------------------------------------------------------------------
diff --git a/models/hub/yolov3-spp.yaml b/models/hub/yolov3-spp.yaml
index 0e073667b..34c2d517c 100644
--- a/models/hub/yolov3-spp.yaml
+++ b/models/hub/yolov3-spp.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov3-tiny.yaml b/models/hub/yolov3-tiny.yaml
index 0a74fff71..f1861012e 100644
--- a/models/hub/yolov3-tiny.yaml
+++ b/models/hub/yolov3-tiny.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov3.yaml b/models/hub/yolov3.yaml
index ce4a980c8..15cb68a83 100644
--- a/models/hub/yolov3.yaml
+++ b/models/hub/yolov3.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5-bifpn.yaml b/models/hub/yolov5-bifpn.yaml
index bf05e434c..fba3fe5f7 100644
--- a/models/hub/yolov5-bifpn.yaml
+++ b/models/hub/yolov5-bifpn.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5-fpn.yaml b/models/hub/yolov5-fpn.yaml
index dcfdd14a7..4411d1cc0 100644
--- a/models/hub/yolov5-fpn.yaml
+++ b/models/hub/yolov5-fpn.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5-p2.yaml b/models/hub/yolov5-p2.yaml
index 2626e7348..e47d39e4e 100644
--- a/models/hub/yolov5-p2.yaml
+++ b/models/hub/yolov5-p2.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5-p34.yaml b/models/hub/yolov5-p34.yaml
index fba35ec10..17e46f7bd 100644
--- a/models/hub/yolov5-p34.yaml
+++ b/models/hub/yolov5-p34.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5-p6.yaml b/models/hub/yolov5-p6.yaml
index c997df2db..dbc1ae4d0 100644
--- a/models/hub/yolov5-p6.yaml
+++ b/models/hub/yolov5-p6.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5-p7.yaml b/models/hub/yolov5-p7.yaml
index 14e6ce05d..2c1706992 100644
--- a/models/hub/yolov5-p7.yaml
+++ b/models/hub/yolov5-p7.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5-panet.yaml b/models/hub/yolov5-panet.yaml
index f0857f92d..68a717566 100644
--- a/models/hub/yolov5-panet.yaml
+++ b/models/hub/yolov5-panet.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5l6.yaml b/models/hub/yolov5l6.yaml
index 05501a9d1..223f681bf 100644
--- a/models/hub/yolov5l6.yaml
+++ b/models/hub/yolov5l6.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5m6.yaml b/models/hub/yolov5m6.yaml
index 1512e2b6e..6878d8996 100644
--- a/models/hub/yolov5m6.yaml
+++ b/models/hub/yolov5m6.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5n6.yaml b/models/hub/yolov5n6.yaml
index 11350413e..0d454c9ca 100644
--- a/models/hub/yolov5n6.yaml
+++ b/models/hub/yolov5n6.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5s-LeakyReLU.yaml b/models/hub/yolov5s-LeakyReLU.yaml
index 6e9d4a882..61d6d3317 100644
--- a/models/hub/yolov5s-LeakyReLU.yaml
+++ b/models/hub/yolov5s-LeakyReLU.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5s-ghost.yaml b/models/hub/yolov5s-ghost.yaml
index cc4336948..53695ae48 100644
--- a/models/hub/yolov5s-ghost.yaml
+++ b/models/hub/yolov5s-ghost.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5s-transformer.yaml b/models/hub/yolov5s-transformer.yaml
index 1b2d62c5a..213e4dac1 100644
--- a/models/hub/yolov5s-transformer.yaml
+++ b/models/hub/yolov5s-transformer.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5s6.yaml b/models/hub/yolov5s6.yaml
index 2a4c11625..6e69964a9 100644
--- a/models/hub/yolov5s6.yaml
+++ b/models/hub/yolov5s6.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/hub/yolov5x6.yaml b/models/hub/yolov5x6.yaml
index 0c8f29e60..33a8525f1 100644
--- a/models/hub/yolov5x6.yaml
+++ b/models/hub/yolov5x6.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/segment/yolov5l-seg.yaml b/models/segment/yolov5l-seg.yaml
index de430f4fb..824e8aec2 100644
--- a/models/segment/yolov5l-seg.yaml
+++ b/models/segment/yolov5l-seg.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/segment/yolov5m-seg.yaml b/models/segment/yolov5m-seg.yaml
index 288577778..c3c1e668a 100644
--- a/models/segment/yolov5m-seg.yaml
+++ b/models/segment/yolov5m-seg.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/segment/yolov5n-seg.yaml b/models/segment/yolov5n-seg.yaml
index faf5228fd..2461e4160 100644
--- a/models/segment/yolov5n-seg.yaml
+++ b/models/segment/yolov5n-seg.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/segment/yolov5s-seg.yaml b/models/segment/yolov5s-seg.yaml
index a199f1d82..fac7664a3 100644
--- a/models/segment/yolov5s-seg.yaml
+++ b/models/segment/yolov5s-seg.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/segment/yolov5x-seg.yaml b/models/segment/yolov5x-seg.yaml
index 75f426386..d3c457a6d 100644
--- a/models/segment/yolov5x-seg.yaml
+++ b/models/segment/yolov5x-seg.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/tf.py b/models/tf.py
index 9884ec3db..c2cad393e 100644
--- a/models/tf.py
+++ b/models/tf.py
@@ -1,7 +1,7 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
TensorFlow, Keras and TFLite versions of YOLOv5
-Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127
+Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127.
Usage:
$ python models/tf.py --weights yolov5s.pt
@@ -49,7 +49,8 @@ from utils.general import LOGGER, make_divisible, print_args
class TFBN(keras.layers.Layer):
- # TensorFlow BatchNormalization wrapper
+ """TensorFlow BatchNormalization wrapper for initializing with optional pretrained weights."""
+
def __init__(self, w=None):
"""Initializes a TensorFlow BatchNormalization layer with optional pretrained weights."""
super().__init__()
@@ -67,7 +68,8 @@ class TFBN(keras.layers.Layer):
class TFPad(keras.layers.Layer):
- # Pad inputs in spatial dimensions 1 and 2
+ """Pads input tensors in spatial dimensions 1 and 2 with specified integer or tuple padding values."""
+
def __init__(self, pad):
"""
Initializes a padding layer for spatial dimensions 1 and 2 with specified padding, supporting both int and tuple
@@ -87,7 +89,8 @@ class TFPad(keras.layers.Layer):
class TFConv(keras.layers.Layer):
- # Standard convolution
+ """Implements a standard convolutional layer with optional batch normalization and activation for TensorFlow."""
+
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
"""
Initializes a standard convolution layer with optional batch normalization and activation; supports only
@@ -118,7 +121,8 @@ class TFConv(keras.layers.Layer):
class TFDWConv(keras.layers.Layer):
- # Depthwise convolution
+ """Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow."""
+
def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):
"""
Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow
@@ -147,7 +151,8 @@ class TFDWConv(keras.layers.Layer):
class TFDWConvTranspose2d(keras.layers.Layer):
- # Depthwise ConvTranspose2d
+ """Implements a depthwise ConvTranspose2D layer for TensorFlow with specific settings."""
+
def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None):
"""
Initializes depthwise ConvTranspose2D layer with specific channel, kernel, stride, and padding settings.
@@ -179,7 +184,8 @@ class TFDWConvTranspose2d(keras.layers.Layer):
class TFFocus(keras.layers.Layer):
- # Focus wh information into c-space
+ """Focuses spatial information into channel space using pixel shuffling and convolution for TensorFlow models."""
+
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
"""
Initializes TFFocus layer to focus width and height information into channel space with custom convolution
@@ -201,7 +207,8 @@ class TFFocus(keras.layers.Layer):
class TFBottleneck(keras.layers.Layer):
- # Standard bottleneck
+ """Implements a TensorFlow bottleneck layer with optional shortcut connections for efficient feature extraction."""
+
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None):
"""
Initializes a standard bottleneck layer for TensorFlow models, expanding and contracting channels with optional
@@ -223,7 +230,8 @@ class TFBottleneck(keras.layers.Layer):
class TFCrossConv(keras.layers.Layer):
- # Cross Convolution
+ """Implements a cross convolutional layer with optional expansion, grouping, and shortcut for TensorFlow."""
+
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None):
"""Initializes cross convolution layer with optional expansion, grouping, and shortcut addition capabilities."""
super().__init__()
@@ -238,7 +246,8 @@ class TFCrossConv(keras.layers.Layer):
class TFConv2d(keras.layers.Layer):
- # Substitution for PyTorch nn.Conv2D
+ """Implements a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D for specified filters and stride."""
+
def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
"""Initializes a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D functionality for given filter
sizes and stride.
@@ -261,7 +270,8 @@ class TFConv2d(keras.layers.Layer):
class TFBottleneckCSP(keras.layers.Layer):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+ """Implements a CSP bottleneck layer for TensorFlow models to enhance gradient flow and efficiency."""
+
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
"""
Initializes CSP bottleneck layer with specified channel sizes, count, shortcut option, groups, and expansion
@@ -289,7 +299,8 @@ class TFBottleneckCSP(keras.layers.Layer):
class TFC3(keras.layers.Layer):
- # CSP Bottleneck with 3 convolutions
+ """CSP bottleneck layer with 3 convolutions for TensorFlow, supporting optional shortcuts and group convolutions."""
+
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
"""
Initializes CSP Bottleneck with 3 convolutions, supporting optional shortcuts and group convolutions.
@@ -313,7 +324,8 @@ class TFC3(keras.layers.Layer):
class TFC3x(keras.layers.Layer):
- # 3 module with cross-convolutions
+ """A TensorFlow layer for enhanced feature extraction using cross-convolutions in object detection models."""
+
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
"""
Initializes layer with cross-convolutions for enhanced feature extraction in object detection models.
@@ -335,7 +347,8 @@ class TFC3x(keras.layers.Layer):
class TFSPP(keras.layers.Layer):
- # Spatial pyramid pooling layer used in YOLOv3-SPP
+ """Implements spatial pyramid pooling for YOLOv3-SPP with specific channels and kernel sizes."""
+
def __init__(self, c1, c2, k=(5, 9, 13), w=None):
"""Initializes a YOLOv3-SPP layer with specific input/output channels and kernel sizes for pooling."""
super().__init__()
@@ -351,7 +364,8 @@ class TFSPP(keras.layers.Layer):
class TFSPPF(keras.layers.Layer):
- # Spatial pyramid pooling-Fast layer
+ """Implements a fast spatial pyramid pooling layer for TensorFlow with optimized feature extraction."""
+
def __init__(self, c1, c2, k=5, w=None):
"""Initializes a fast spatial pyramid pooling layer with customizable in/out channels, kernel size, and
weights.
@@ -373,7 +387,8 @@ class TFSPPF(keras.layers.Layer):
class TFDetect(keras.layers.Layer):
- # TF YOLOv5 Detect layer
+ """Implements YOLOv5 object detection layer in TensorFlow for predicting bounding boxes and class probabilities."""
+
def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None):
"""Initializes YOLOv5 detection layer for TensorFlow with configurable classes, anchors, channels, and image
size.
@@ -427,7 +442,8 @@ class TFDetect(keras.layers.Layer):
class TFSegment(TFDetect):
- # YOLOv5 Segment head for segmentation models
+ """YOLOv5 segmentation head for TensorFlow, combining detection and segmentation."""
+
def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None):
"""Initializes YOLOv5 Segment head with specified channel depths, anchors, and input size for segmentation
models.
@@ -450,6 +466,8 @@ class TFSegment(TFDetect):
class TFProto(keras.layers.Layer):
+ """Implements convolutional and upsampling layers for feature extraction in YOLOv5 segmentation."""
+
def __init__(self, c1, c_=256, c2=32, w=None):
"""Initializes TFProto layer with convolutional and upsampling layers for feature extraction and
transformation.
@@ -466,7 +484,8 @@ class TFProto(keras.layers.Layer):
class TFUpsample(keras.layers.Layer):
- # TF version of torch.nn.Upsample()
+ """Implements a TensorFlow upsampling layer with specified size, scale factor, and interpolation mode."""
+
def __init__(self, size, scale_factor, mode, w=None):
"""
Initializes a TensorFlow upsampling layer with specified size, scale_factor, and mode, ensuring scale_factor is
@@ -488,7 +507,8 @@ class TFUpsample(keras.layers.Layer):
class TFConcat(keras.layers.Layer):
- # TF version of torch.concat()
+ """Implements TensorFlow's version of torch.concat() for concatenating tensors along the last dimension."""
+
def __init__(self, dimension=1, w=None):
"""Initializes a TensorFlow layer for NCHW to NHWC concatenation, requiring dimension=1."""
super().__init__()
@@ -581,7 +601,8 @@ def parse_model(d, ch, model, imgsz):
class TFModel:
- # TF YOLOv5 model
+ """Implements YOLOv5 model in TensorFlow, supporting TensorFlow, Keras, and TFLite formats for object detection."""
+
def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, model=None, imgsz=(640, 640)):
"""Initializes TF YOLOv5 model with specified configuration, channels, classes, model instance, and input
size.
@@ -653,7 +674,8 @@ class TFModel:
class AgnosticNMS(keras.layers.Layer):
- # TF Agnostic NMS
+ """Performs agnostic non-maximum suppression (NMS) on detected objects using IoU and confidence thresholds."""
+
def call(self, input, topk_all, iou_thres, conf_thres):
"""Performs agnostic NMS on input tensors using given thresholds and top-K selection."""
return tf.map_fn(
diff --git a/models/yolo.py b/models/yolo.py
index d89c5da01..13498aced 100644
--- a/models/yolo.py
+++ b/models/yolo.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
YOLO-specific modules.
@@ -70,7 +70,8 @@ except ImportError:
class Detect(nn.Module):
- # YOLOv5 Detect head for detection models
+ """YOLOv5 Detect head for processing input tensors and generating detection outputs in object detection models."""
+
stride = None # strides computed during build
dynamic = False # force grid reconstruction
export = False # export mode
@@ -127,7 +128,8 @@ class Detect(nn.Module):
class Segment(Detect):
- # YOLOv5 Segment head for segmentation models
+ """YOLOv5 Segment head for segmentation models, extending Detect with mask and prototype layers."""
+
def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True):
"""Initializes YOLOv5 Segment head with options for mask count, protos, and channel adjustments."""
super().__init__(nc, anchors, ch, inplace)
@@ -214,7 +216,8 @@ class BaseModel(nn.Module):
class DetectionModel(BaseModel):
- # YOLOv5 detection model
+ """YOLOv5 detection model class for object detection tasks, supporting custom configurations and anchors."""
+
def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None):
"""Initializes YOLOv5 model with configuration file, input channels, number of classes, and custom anchors."""
super().__init__()
@@ -332,14 +335,16 @@ Model = DetectionModel # retain YOLOv5 'Model' class for backwards compatibilit
class SegmentationModel(DetectionModel):
- # YOLOv5 segmentation model
+ """YOLOv5 segmentation model for object detection and segmentation tasks with configurable parameters."""
+
def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None):
"""Initializes a YOLOv5 segmentation model with configurable params: cfg (str) for configuration, ch (int) for channels, nc (int) for num classes, anchors (list)."""
super().__init__(cfg, ch, nc, anchors)
class ClassificationModel(BaseModel):
- # YOLOv5 classification model
+ """YOLOv5 classification model for image classification tasks, initialized with a config file or detection model."""
+
def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):
"""Initializes YOLOv5 model with config file `cfg`, input channels `ch`, number of classes `nc`, and `cuttoff`
index.
diff --git a/models/yolov5l.yaml b/models/yolov5l.yaml
index 7cac7ead2..c6c878a10 100644
--- a/models/yolov5l.yaml
+++ b/models/yolov5l.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/yolov5m.yaml b/models/yolov5m.yaml
index 820e60704..41d9c223a 100644
--- a/models/yolov5m.yaml
+++ b/models/yolov5m.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/yolov5n.yaml b/models/yolov5n.yaml
index d3b84ace2..588674923 100644
--- a/models/yolov5n.yaml
+++ b/models/yolov5n.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/yolov5s.yaml b/models/yolov5s.yaml
index 090cb67c2..11ff79001 100644
--- a/models/yolov5s.yaml
+++ b/models/yolov5s.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/models/yolov5x.yaml b/models/yolov5x.yaml
index 8c1a6be1b..817b4f911 100644
--- a/models/yolov5x.yaml
+++ b/models/yolov5x.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Parameters
nc: 80 # number of classes
diff --git a/pyproject.toml b/pyproject.toml
index 2bcf65929..9680857af 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Overview:
# This pyproject.toml file manages the build, packaging, and distribution of the Ultralytics library.
diff --git a/requirements.txt b/requirements.txt
index e10fd0cfd..dcd23bf53 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,12 +9,12 @@ opencv-python>=4.1.1
pillow>=10.3.0
psutil # system resources
PyYAML>=5.3.1
-requests>=2.32.0
+requests>=2.32.2
scipy>=1.4.1
thop>=0.1.1 # FLOPs computation
torch>=1.8.0 # see https://pytorch.org/get-started/locally (recommended)
torchvision>=0.9.0
-tqdm>=4.64.0
+tqdm>=4.66.3
ultralytics>=8.2.34 # https://ultralytics.com
# protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012
diff --git a/segment/predict.py b/segment/predict.py
index 0bccaaaae..e0e4336c1 100644
--- a/segment/predict.py
+++ b/segment/predict.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
Run YOLOv5 segmentation inference on images, videos, directories, streams, etc.
@@ -164,7 +164,7 @@ def run(
p = Path(p) # to Path
save_path = str(save_dir / p.name) # im.jpg
txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt
- s += "%gx%g " % im.shape[2:] # print string
+ s += "{:g}x{:g} ".format(*im.shape[2:]) # print string
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
@@ -245,7 +245,7 @@ def run(
vid_writer[i].write(im0)
# Print time (inference-only)
- LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
+ LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1e3:.1f}ms")
# Print results
t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
diff --git a/segment/train.py b/segment/train.py
index 379fed0b2..815c97ce1 100644
--- a/segment/train.py
+++ b/segment/train.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
Train a YOLOv5 segment model on a segment dataset Models and datasets download automatically from the latest YOLOv5
release.
@@ -325,10 +325,10 @@ def train(hyp, opt, device, callbacks):
compute_loss = ComputeLoss(model, overlap=overlap) # init loss class
# callbacks.run('on_train_start')
LOGGER.info(
- f'Image sizes {imgsz} train, {imgsz} val\n'
- f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
+ f"Image sizes {imgsz} train, {imgsz} val\n"
+ f"Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n"
f"Logging results to {colorstr('bold', save_dir)}\n"
- f'Starting training for {epochs} epochs...'
+ f"Starting training for {epochs} epochs..."
)
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
# callbacks.run('on_train_epoch_start')
@@ -405,7 +405,7 @@ def train(hyp, opt, device, callbacks):
# Log
if RANK in {-1, 0}:
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
- mem = f"{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G" # (GB)
+ mem = f"{torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0:.3g}G" # (GB)
pbar.set_description(
("%11s" * 2 + "%11.4g" * 6)
% (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1])
@@ -740,9 +740,9 @@ def main(opt, callbacks=Callbacks()):
# Plot results
plot_evolve(evolve_csv)
LOGGER.info(
- f'Hyperparameter evolution finished {opt.evolve} generations\n'
+ f"Hyperparameter evolution finished {opt.evolve} generations\n"
f"Results saved to {colorstr('bold', save_dir)}\n"
- f'Usage example: $ python train.py --hyp {evolve_yaml}'
+ f"Usage example: $ python train.py --hyp {evolve_yaml}"
)
diff --git a/segment/tutorial.ipynb b/segment/tutorial.ipynb
index 56ea50500..bb5c1f996 100644
--- a/segment/tutorial.ipynb
+++ b/segment/tutorial.ipynb
@@ -15,7 +15,7 @@
"\n",
" \n",
- " \n",
+ " \n",
" \n",
+ "- **Notebooks** with free GPU: \n",
"- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
"- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
"- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \n"
diff --git a/segment/val.py b/segment/val.py
index ab8a66a90..edd6a08fa 100644
--- a/segment/val.py
+++ b/segment/val.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
Validate a trained YOLOv5 segment model on a segment dataset.
@@ -121,7 +121,7 @@ def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, over
detections (array[N, 6]), x1, y1, x2, y2, conf, class
labels (array[M, 5]), class, x1, y1, x2, y2
Returns:
- correct (array[N, 10]), for 10 IoU levels
+ correct (array[N, 10]), for 10 IoU levels.
"""
if masks:
if overlap:
diff --git a/train.py b/train.py
index b4395d7e8..1401ccb96 100644
--- a/train.py
+++ b/train.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
Train a YOLOv5 model on a custom dataset. Models and datasets download automatically from the latest YOLOv5 release.
@@ -357,10 +357,10 @@ def train(hyp, opt, device, callbacks):
compute_loss = ComputeLoss(model) # init loss class
callbacks.run("on_train_start")
LOGGER.info(
- f'Image sizes {imgsz} train, {imgsz} val\n'
- f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
+ f"Image sizes {imgsz} train, {imgsz} val\n"
+ f"Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n"
f"Logging results to {colorstr('bold', save_dir)}\n"
- f'Starting training for {epochs} epochs...'
+ f"Starting training for {epochs} epochs..."
)
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
callbacks.run("on_train_epoch_start")
@@ -434,7 +434,7 @@ def train(hyp, opt, device, callbacks):
# Log
if RANK in {-1, 0}:
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
- mem = f"{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G" # (GB)
+ mem = f"{torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0:.3g}G" # (GB)
pbar.set_description(
("%11s" * 2 + "%11.4g" * 5)
% (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1])
@@ -717,10 +717,10 @@ def main(opt, callbacks=Callbacks()):
"perspective": (True, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
"flipud": (True, 0.0, 1.0), # image flip up-down (probability)
"fliplr": (True, 0.0, 1.0), # image flip left-right (probability)
- "mosaic": (True, 0.0, 1.0), # image mixup (probability)
+ "mosaic": (True, 0.0, 1.0), # image mosaic (probability)
"mixup": (True, 0.0, 1.0), # image mixup (probability)
- "copy_paste": (True, 0.0, 1.0),
- } # segment copy-paste (probability)
+ "copy_paste": (True, 0.0, 1.0), # segment copy-paste (probability)
+ }
# GA configs
pop_size = 50
@@ -880,9 +880,9 @@ def main(opt, callbacks=Callbacks()):
# Plot results
plot_evolve(evolve_csv)
LOGGER.info(
- f'Hyperparameter evolution finished {opt.evolve} generations\n'
+ f"Hyperparameter evolution finished {opt.evolve} generations\n"
f"Results saved to {colorstr('bold', save_dir)}\n"
- f'Usage example: $ python train.py --hyp {evolve_yaml}'
+ f"Usage example: $ python train.py --hyp {evolve_yaml}"
)
diff --git a/tutorial.ipynb b/tutorial.ipynb
index ebc6c0b22..b383deb7e 100644
--- a/tutorial.ipynb
+++ b/tutorial.ipynb
@@ -28,7 +28,7 @@
"\n",
" \n",
" \n",
- " \n",
+ " \n",
"\n",
"This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure. \n",
+ "- **Notebooks** with free GPU: \n",
"- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
"- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
"- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \n"
diff --git a/utils/__init__.py b/utils/__init__.py
index c7ece49fa..3c43c9b68 100644
--- a/utils/__init__.py
+++ b/utils/__init__.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""utils/initialization."""
import contextlib
@@ -12,7 +12,8 @@ def emojis(str=""):
class TryExcept(contextlib.ContextDecorator):
- # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
+ """A context manager and decorator for error handling that prints an optional message with emojis on exception."""
+
def __init__(self, msg=""):
"""Initializes TryExcept with an optional message, used as a decorator or context manager for error handling."""
self.msg = msg
diff --git a/utils/activations.py b/utils/activations.py
index 47f0a9980..4652540db 100644
--- a/utils/activations.py
+++ b/utils/activations.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Activation functions."""
import torch
@@ -7,6 +7,8 @@ import torch.nn.functional as F
class SiLU(nn.Module):
+ """Applies the Sigmoid-weighted Linear Unit (SiLU) activation function, also known as Swish."""
+
@staticmethod
def forward(x):
"""
@@ -18,6 +20,8 @@ class SiLU(nn.Module):
class Hardswish(nn.Module):
+ """Applies the Hardswish activation function, which is efficient for mobile and embedded devices."""
+
@staticmethod
def forward(x):
"""
@@ -38,7 +42,11 @@ class Mish(nn.Module):
class MemoryEfficientMish(nn.Module):
+ """Efficiently applies the Mish activation function using custom autograd for reduced memory usage."""
+
class F(torch.autograd.Function):
+ """Implements a custom autograd function for memory-efficient Mish activation."""
+
@staticmethod
def forward(ctx, x):
"""Applies the Mish activation function, a smooth ReLU alternative, to the input tensor `x`."""
diff --git a/utils/augmentations.py b/utils/augmentations.py
index bdbe07712..79e7afc8d 100644
--- a/utils/augmentations.py
+++ b/utils/augmentations.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Image augmentation functions."""
import math
@@ -18,7 +18,8 @@ IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation
class Albumentations:
- # YOLOv5 Albumentations class (optional, only used if package is installed)
+ """Provides optional data augmentation for YOLOv5 using Albumentations library if installed."""
+
def __init__(self, size=640):
"""Initializes Albumentations class for optional data augmentation in YOLOv5 with specified input size."""
self.transform = None
@@ -196,15 +197,7 @@ def random_perspective(
else: # affine
im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
- # Visualize
- # import matplotlib.pyplot as plt
- # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
- # ax[0].imshow(im[:, :, ::-1]) # base
- # ax[1].imshow(im2[:, :, ::-1]) # warped
-
- # Transform label coordinates
- n = len(targets)
- if n:
+ if n := len(targets):
use_segments = any(x.any() for x in segments) and len(segments) == n
new = np.zeros((n, 4))
if use_segments: # warp segments
@@ -378,7 +371,8 @@ def classify_transforms(size=224):
class LetterBox:
- # YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
+ """Resizes and pads images to specified dimensions while maintaining aspect ratio for YOLOv5 preprocessing."""
+
def __init__(self, size=(640, 640), auto=False, stride=32):
"""Initializes a LetterBox object for YOLOv5 image preprocessing with optional auto sizing and stride
adjustment.
@@ -405,7 +399,8 @@ class LetterBox:
class CenterCrop:
- # YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])
+ """Applies center crop to an image, resizing it to the specified size while maintaining aspect ratio."""
+
def __init__(self, size=640):
"""Initializes CenterCrop for image preprocessing, accepting single int or tuple for size, defaults to 640."""
super().__init__()
@@ -424,7 +419,8 @@ class CenterCrop:
class ToTensor:
- # YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
+ """Converts BGR np.array image from HWC to RGB CHW format, normalizes to [0, 1], and supports FP16 if half=True."""
+
def __init__(self, half=False):
"""Initializes ToTensor for YOLOv5 image preprocessing, with optional half precision (half=True for FP16)."""
super().__init__()
diff --git a/utils/autoanchor.py b/utils/autoanchor.py
index 00eee2eb7..1fc420759 100644
--- a/utils/autoanchor.py
+++ b/utils/autoanchor.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""AutoAnchor utils."""
import random
diff --git a/utils/autobatch.py b/utils/autobatch.py
index 08a0de841..9d5ea0a94 100644
--- a/utils/autobatch.py
+++ b/utils/autobatch.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Auto-batch utils."""
from copy import deepcopy
diff --git a/utils/aws/__init__.py b/utils/aws/__init__.py
index e69de29bb..77a19dcf0 100644
--- a/utils/aws/__init__.py
+++ b/utils/aws/__init__.py
@@ -0,0 +1 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
diff --git a/utils/aws/resume.py b/utils/aws/resume.py
index ea432a161..5b80fd41f 100644
--- a/utils/aws/resume.py
+++ b/utils/aws/resume.py
@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
# Resume all interrupted trainings in yolov5/ dir including DDP trainings
# Usage: $ python utils/aws/resume.py
diff --git a/utils/callbacks.py b/utils/callbacks.py
index 21c587bd7..1a6092840 100644
--- a/utils/callbacks.py
+++ b/utils/callbacks.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Callback utils."""
import threading
diff --git a/utils/dataloaders.py b/utils/dataloaders.py
index bdeffec46..d86c9afda 100644
--- a/utils/dataloaders.py
+++ b/utils/dataloaders.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Dataloaders and dataset utils."""
import contextlib
@@ -93,7 +93,7 @@ def exif_size(img):
def exif_transpose(image):
"""
Transpose a PIL image accordingly if it has an EXIF Orientation tag.
- Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose()
+ Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose().
:param image: The image to transpose.
:return: An image.
@@ -131,6 +131,8 @@ def seed_worker(worker_id):
# Inherit from DistributedSampler and override iterator
# https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py
class SmartDistributedSampler(distributed.DistributedSampler):
+ """A distributed sampler ensuring deterministic shuffling and balanced data distribution across GPUs."""
+
def __iter__(self):
"""Yields indices for distributed data sampling, shuffled deterministically based on epoch and seed."""
g = torch.Generator()
@@ -208,6 +210,7 @@ def create_dataloader(
shuffle=shuffle and sampler is None,
num_workers=nw,
sampler=sampler,
+ drop_last=quad,
pin_memory=PIN_MEMORY,
collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
worker_init_fn=seed_worker,
@@ -259,7 +262,8 @@ class _RepeatSampler:
class LoadScreenshots:
- # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"`
+ """Loads and processes screenshots for YOLOv5 detection from specified screen regions using mss."""
+
def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
"""
Initializes a screenshot dataloader for YOLOv5 with specified source region, image size, stride, auto, and
@@ -316,7 +320,7 @@ class LoadScreenshots:
class LoadImages:
- """YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`"""
+ """YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`."""
def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
"""Initializes YOLOv5 loader for images/videos, supporting glob patterns, directories, and lists of paths."""
@@ -352,8 +356,7 @@ class LoadImages:
else:
self.cap = None
assert self.nf > 0, (
- f"No images or videos found in {p}. "
- f"Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}"
+ f"No images or videos found in {p}. Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}"
)
def __iter__(self):
@@ -428,7 +431,8 @@ class LoadImages:
class LoadStreams:
- # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams`
+ """Loads and processes video streams for YOLOv5, supporting various sources including YouTube and IP cameras."""
+
def __init__(self, sources="file.streams", img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
"""Initializes a stream loader for processing video streams with YOLOv5, supporting various sources including
YouTube.
@@ -531,7 +535,8 @@ def img2label_paths(img_paths):
class LoadImagesAndLabels(Dataset):
- # YOLOv5 train_loader/val_loader, loads images and labels for training and validation
+ """Loads images and their corresponding labels for training and validation in YOLOv5."""
+
cache_version = 0.6 # dataset labels *.cache version
rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
@@ -683,16 +688,17 @@ class LoadImagesAndLabels(Dataset):
b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes
self.im_hw0, self.im_hw = [None] * n, [None] * n
fcn = self.cache_images_to_disk if cache_images == "disk" else self.load_image
- results = ThreadPool(NUM_THREADS).imap(lambda i: (i, fcn(i)), self.indices)
- pbar = tqdm(results, total=len(self.indices), bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)
- for i, x in pbar:
- if cache_images == "disk":
- b += self.npy_files[i].stat().st_size
- else: # 'ram'
- self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i)
- b += self.ims[i].nbytes * WORLD_SIZE
- pbar.desc = f"{prefix}Caching images ({b / gb:.1f}GB {cache_images})"
- pbar.close()
+ with ThreadPool(NUM_THREADS) as pool:
+ results = pool.imap(lambda i: (i, fcn(i)), self.indices)
+ pbar = tqdm(results, total=len(self.indices), bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)
+ for i, x in pbar:
+ if cache_images == "disk":
+ b += self.npy_files[i].stat().st_size
+ else: # 'ram'
+ self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i)
+ b += self.ims[i].nbytes * WORLD_SIZE
+ pbar.desc = f"{prefix}Caching images ({b / gb:.1f}GB {cache_images})"
+ pbar.close()
def check_cache_ram(self, safety_margin=0.1, prefix=""):
"""Checks if available RAM is sufficient for caching images, adjusting for a safety margin."""
@@ -707,8 +713,8 @@ class LoadImagesAndLabels(Dataset):
cache = mem_required * (1 + safety_margin) < mem.available # to cache or not to cache, that is the question
if not cache:
LOGGER.info(
- f'{prefix}{mem_required / gb:.1f}GB RAM required, '
- f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, '
+ f"{prefix}{mem_required / gb:.1f}GB RAM required, "
+ f"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, "
f"{'caching images ✅' if cache else 'not caching images ⚠️'}"
)
return cache
@@ -768,8 +774,7 @@ class LoadImagesAndLabels(Dataset):
index = self.indices[index] # linear, shuffled, or image_weights
hyp = self.hyp
- mosaic = self.mosaic and random.random() < hyp["mosaic"]
- if mosaic:
+ if mosaic := self.mosaic and random.random() < hyp["mosaic"]:
# Load mosaic
img, labels = self.load_mosaic(index)
shapes = None
@@ -1103,7 +1108,6 @@ def extract_boxes(path=DATASETS_DIR / "coco128"):
def autosplit(path=DATASETS_DIR / "coco128/images", weights=(0.9, 0.1, 0.0), annotated_only=False):
"""Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
- Usage: from utils.dataloaders import *; autosplit()
Arguments:
path: Path to images directory
@@ -1156,8 +1160,7 @@ def verify_image_label(args):
segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...)
lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)
lb = np.array(lb, dtype=np.float32)
- nl = len(lb)
- if nl:
+ if nl := len(lb):
assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected"
assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}"
assert (lb[:, 1:] <= 1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"
diff --git a/utils/downloads.py b/utils/downloads.py
index c7e2273c7..f51d67aa9 100644
--- a/utils/downloads.py
+++ b/utils/downloads.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Download utils."""
import logging
diff --git a/utils/flask_rest_api/README.md b/utils/flask_rest_api/README.md
index b18a3011c..d3ffaa206 100644
--- a/utils/flask_rest_api/README.md
+++ b/utils/flask_rest_api/README.md
@@ -4,7 +4,7 @@
## Requirements
-[Flask](https://palletsprojects.com/p/flask/) is required. Install with:
+[Flask](https://palletsprojects.com/projects/flask/) is required. Install with:
```shell
$ pip install Flask
diff --git a/utils/flask_rest_api/example_request.py b/utils/flask_rest_api/example_request.py
index 104249002..db88e8040 100644
--- a/utils/flask_rest_api/example_request.py
+++ b/utils/flask_rest_api/example_request.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Perform test request."""
import pprint
diff --git a/utils/flask_rest_api/restapi.py b/utils/flask_rest_api/restapi.py
index 7e03d3a66..410ae26c5 100644
--- a/utils/flask_rest_api/restapi.py
+++ b/utils/flask_rest_api/restapi.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Run a Flask REST API exposing one or more YOLOv5s models."""
import argparse
diff --git a/utils/general.py b/utils/general.py
index 57db68a7a..89bbc61f4 100644
--- a/utils/general.py
+++ b/utils/general.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""General utils."""
import contextlib
@@ -173,8 +173,7 @@ def user_config_dir(dir="Ultralytics", env_var="YOLOV5_CONFIG_DIR"):
"""Returns user configuration directory path, preferring environment variable `YOLOV5_CONFIG_DIR` if set, else OS-
specific.
"""
- env = os.getenv(env_var)
- if env:
+ if env := os.getenv(env_var):
path = Path(env) # use environment variable
else:
cfg = {"Windows": "AppData/Roaming", "Linux": ".config", "Darwin": "Library/Application Support"} # 3 OS dirs
@@ -188,7 +187,8 @@ CONFIG_DIR = user_config_dir() # Ultralytics settings dir
class Profile(contextlib.ContextDecorator):
- # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager
+ """Context manager and decorator for profiling code execution time, with optional CUDA synchronization."""
+
def __init__(self, t=0.0, device: torch.device = None):
"""Initializes a profiling context for YOLOv5 with optional timing threshold and device specification."""
self.t = t
@@ -213,7 +213,8 @@ class Profile(contextlib.ContextDecorator):
class Timeout(contextlib.ContextDecorator):
- # YOLOv5 Timeout class. Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager
+ """Enforces a timeout on code execution, raising TimeoutError if the specified duration is exceeded."""
+
def __init__(self, seconds, *, timeout_msg="", suppress_timeout_errors=True):
"""Initializes a timeout context/decorator with defined seconds, optional message, and error suppression."""
self.seconds = int(seconds)
@@ -239,7 +240,8 @@ class Timeout(contextlib.ContextDecorator):
class WorkingDirectory(contextlib.ContextDecorator):
- # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager
+ """Context manager/decorator to temporarily change the working directory within a 'with' statement or decorator."""
+
def __init__(self, new_dir):
"""Initializes a context manager/decorator to temporarily change the working directory."""
self.dir = new_dir # new dir
@@ -493,9 +495,9 @@ def check_file(file, suffix=""):
assert Path(file).exists() and Path(file).stat().st_size > 0, f"File download failed: {url}" # check
return file
elif file.startswith("clearml://"): # ClearML Dataset ID
- assert (
- "clearml" in sys.modules
- ), "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'."
+ assert "clearml" in sys.modules, (
+ "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'."
+ )
return file
else: # search
files = []
diff --git a/utils/google_app_engine/app.yaml b/utils/google_app_engine/app.yaml
index 4c1751f55..6fb9d5f9d 100644
--- a/utils/google_app_engine/app.yaml
+++ b/utils/google_app_engine/app.yaml
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
runtime: custom
env: flex
diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py
index 7051e8da0..be9ccfbb1 100644
--- a/utils/loggers/__init__.py
+++ b/utils/loggers/__init__.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Logging utils."""
import json
@@ -76,7 +76,8 @@ def _json_default(value):
class Loggers:
- # YOLOv5 Loggers class
+ """Initializes and manages various logging utilities for tracking YOLOv5 training and validation metrics."""
+
def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
"""Initializes loggers for YOLOv5 training and validation metrics, paths, and options."""
self.save_dir = save_dir
@@ -349,7 +350,7 @@ class Loggers:
class GenericLogger:
"""
YOLOv5 General purpose logger for non-task specific logging
- Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...)
+
Arguments:
opt: Run arguments
diff --git a/utils/loggers/clearml/README.md b/utils/loggers/clearml/README.md
index a61e30254..374765dfb 100644
--- a/utils/loggers/clearml/README.md
+++ b/utils/loggers/clearml/README.md
@@ -4,7 +4,7 @@
## About ClearML
-[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️.
+[ClearML](https://clear.ml/) is an [open-source](https://github.com/clearml/clearml) toolbox designed to save you time ⏱️.
🔨 Track every YOLOv5 training run in the experiment manager
@@ -18,13 +18,13 @@
And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline!
-
+
## 🦾 Setting Things Up
To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one:
-Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go!
+Either sign up for free to the [ClearML Hosted Service](https://clear.ml/) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go!
1. Install the `clearml` python package:
@@ -85,7 +85,7 @@ There even more we can do with all of this information, like hyperparameter opti
Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment!
-
+
### Prepare Your Dataset
@@ -163,13 +163,13 @@ pip install optuna
python utils/loggers/clearml/hpo.py
```
-
+
## 🤯 Remote Execution (advanced)
Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs. This is where the ClearML Agent comes into play. Check out what the agent can do here:
-- [YouTube video](https://youtu.be/MX3BrXnaULs)
+- [YouTube video](https://www.youtube.com/watch?v=MX3BrXnaULs&feature=youtu.be)
- [Documentation](https://clear.ml/docs/latest/docs/clearml_agent)
In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager.
@@ -190,7 +190,7 @@ With our agent running, we can give it some work. Remember from the HPO section
⏳ Enqueue the task to any of the queues by right-clicking it
-
+
### Executing A Task Remotely
diff --git a/utils/loggers/clearml/__init__.py b/utils/loggers/clearml/__init__.py
index e69de29bb..77a19dcf0 100644
--- a/utils/loggers/clearml/__init__.py
+++ b/utils/loggers/clearml/__init__.py
@@ -0,0 +1 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
diff --git a/utils/loggers/clearml/clearml_utils.py b/utils/loggers/clearml/clearml_utils.py
index de4129e08..67553bdb4 100644
--- a/utils/loggers/clearml/clearml_utils.py
+++ b/utils/loggers/clearml/clearml_utils.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Main Logger class for ClearML experiment tracking."""
import glob
@@ -41,11 +41,9 @@ def construct_dataset(clearml_info_string):
with open(yaml_filenames[0]) as f:
dataset_definition = yaml.safe_load(f)
- assert set(
- dataset_definition.keys()
- ).issuperset(
- {"train", "test", "val", "nc", "names"}
- ), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')"
+ assert set(dataset_definition.keys()).issuperset({"train", "test", "val", "nc", "names"}), (
+ "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')"
+ )
data_dict = {
"train": (
@@ -78,7 +76,7 @@ class ClearmlLogger:
def __init__(self, opt, hyp):
"""
- Initialize ClearML Task, this object will capture the experiment
- - Upload dataset version to ClearML Data if opt.upload_dataset is True
+ - Upload dataset version to ClearML Data if opt.upload_dataset is True.
Arguments:
opt (namespace) -- Commandline arguments for this run
diff --git a/utils/loggers/clearml/hpo.py b/utils/loggers/clearml/hpo.py
index 5a9be757a..099a87fca 100644
--- a/utils/loggers/clearml/hpo.py
+++ b/utils/loggers/clearml/hpo.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from clearml import Task
diff --git a/utils/loggers/comet/__init__.py b/utils/loggers/comet/__init__.py
index 846dcb42a..1ad44b9a3 100644
--- a/utils/loggers/comet/__init__.py
+++ b/utils/loggers/comet/__init__.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import glob
import json
@@ -88,14 +88,12 @@ class CometLogger:
self.upload_dataset = self.opt.upload_dataset or COMET_UPLOAD_DATASET
self.resume = self.opt.resume
- # Default parameters to pass to Experiment objects
self.default_experiment_kwargs = {
"log_code": False,
"log_env_gpu": True,
"log_env_cpu": True,
"project_name": COMET_PROJECT_NAME,
- }
- self.default_experiment_kwargs.update(experiment_kwargs)
+ } | experiment_kwargs
self.experiment = self._get_experiment(self.comet_mode, run_id)
self.experiment.set_name(self.opt.name)
diff --git a/utils/loggers/comet/comet_utils.py b/utils/loggers/comet/comet_utils.py
index cf936ab48..1dc572cca 100644
--- a/utils/loggers/comet/comet_utils.py
+++ b/utils/loggers/comet/comet_utils.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import logging
import os
diff --git a/utils/loggers/comet/hpo.py b/utils/loggers/comet/hpo.py
index c225ebbd0..dc171e261 100644
--- a/utils/loggers/comet/hpo.py
+++ b/utils/loggers/comet/hpo.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import argparse
import json
diff --git a/utils/loggers/wandb/__init__.py b/utils/loggers/wandb/__init__.py
index e69de29bb..77a19dcf0 100644
--- a/utils/loggers/wandb/__init__.py
+++ b/utils/loggers/wandb/__init__.py
@@ -0,0 +1 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
diff --git a/utils/loggers/wandb/wandb_utils.py b/utils/loggers/wandb/wandb_utils.py
index 6a32c8cc7..83c5ee130 100644
--- a/utils/loggers/wandb/wandb_utils.py
+++ b/utils/loggers/wandb/wandb_utils.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# WARNING ⚠️ wandb is deprecated and will be removed in future release.
# See supported integrations at https://github.com/ultralytics/yolov5#integrations
@@ -18,7 +18,7 @@ if str(ROOT) not in sys.path:
RANK = int(os.getenv("RANK", -1))
DEPRECATION_WARNING = (
f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. "
- f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.'
+ f"See supported integrations at https://github.com/ultralytics/yolov5#integrations."
)
try:
@@ -47,7 +47,7 @@ class WandbLogger:
"""
- Initialize WandbLogger instance
- Upload dataset if opt.upload_dataset is True
- - Setup training processes if job_type is 'Training'
+ - Setup training processes if job_type is 'Training'.
Arguments:
opt (namespace) -- Commandline arguments for this run
@@ -88,7 +88,7 @@ class WandbLogger:
Setup the necessary processes for training YOLO models:
- Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
- Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
- - Setup log_dict, initialize bbox_interval
+ - Setup log_dict, initialize bbox_interval.
Arguments:
opt (namespace) -- commandline arguments for this run
diff --git a/utils/loss.py b/utils/loss.py
index e8f148e77..35ce6d792 100644
--- a/utils/loss.py
+++ b/utils/loss.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Loss functions."""
import torch
@@ -9,12 +9,13 @@ from utils.torch_utils import de_parallel
def smooth_BCE(eps=0.1):
- """Returns label smoothing BCE targets for reducing overfitting; pos: `1.0 - 0.5*eps`, neg: `0.5*eps`. For details see https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441"""
+ """Returns label smoothing BCE targets for reducing overfitting; pos: `1.0 - 0.5*eps`, neg: `0.5*eps`. For details see https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441."""
return 1.0 - 0.5 * eps, 0.5 * eps
class BCEBlurWithLogitsLoss(nn.Module):
- # BCEwithLogitLoss() with reduced missing label effects.
+ """Modified BCEWithLogitsLoss to reduce missing label effects in YOLOv5 training with optional alpha smoothing."""
+
def __init__(self, alpha=0.05):
"""Initializes a modified BCEWithLogitsLoss with reduced missing label effects, taking optional alpha smoothing
parameter.
@@ -37,7 +38,8 @@ class BCEBlurWithLogitsLoss(nn.Module):
class FocalLoss(nn.Module):
- # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
+ """Applies focal loss to address class imbalance by modifying BCEWithLogitsLoss with gamma and alpha parameters."""
+
def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
"""Initializes FocalLoss with specified loss function, gamma, and alpha values; modifies loss reduction to
'none'.
@@ -71,7 +73,8 @@ class FocalLoss(nn.Module):
class QFocalLoss(nn.Module):
- # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
+ """Implements Quality Focal Loss to address class imbalance by modulating loss based on prediction confidence."""
+
def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
"""Initializes Quality Focal Loss with given loss function, gamma, alpha; modifies reduction to 'none'."""
super().__init__()
@@ -101,6 +104,8 @@ class QFocalLoss(nn.Module):
class ComputeLoss:
+ """Computes the total loss for YOLOv5 model predictions, including classification, box, and objectness losses."""
+
sort_obj_iou = False
# Compute losses
@@ -143,8 +148,7 @@ class ComputeLoss:
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
- n = b.shape[0] # number of targets
- if n:
+ if n := b.shape[0]:
# pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0
pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions
@@ -170,10 +174,6 @@ class ComputeLoss:
t[range(n), tcls[i]] = self.cp
lcls += self.BCEcls(pcls, t) # BCE
- # Append targets to text file
- # with open('targets.txt', 'a') as file:
- # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
obji = self.BCEobj(pi[..., 4], tobj)
lobj += obji * self.balance[i] # obj loss
if self.autobalance:
diff --git a/utils/metrics.py b/utils/metrics.py
index 9acc38591..03013f4e3 100644
--- a/utils/metrics.py
+++ b/utils/metrics.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Model validation metrics."""
import math
@@ -100,7 +100,7 @@ def compute_ap(recall, precision):
recall: The recall curve (list)
precision: The precision curve (list)
# Returns
- Average precision, precision curve, recall curve
+ Average precision, precision curve, recall curve.
"""
# Append sentinel values to beginning and end
mrec = np.concatenate(([0.0], recall, [1.0]))
@@ -122,7 +122,8 @@ def compute_ap(recall, precision):
class ConfusionMatrix:
- # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
+ """Generates and visualizes a confusion matrix for evaluating object detection classification performance."""
+
def __init__(self, nc, conf=0.25, iou_thres=0.45):
"""Initializes ConfusionMatrix with given number of classes, confidence, and IoU threshold."""
self.matrix = np.zeros((nc + 1, nc + 1))
@@ -346,7 +347,7 @@ def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=()):
else:
ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision)
- ax.plot(px, py.mean(1), linewidth=3, color="blue", label="all classes %.3f mAP@0.5" % ap[:, 0].mean())
+ ax.plot(px, py.mean(1), linewidth=3, color="blue", label=f"all classes {ap[:, 0].mean():.3f} mAP@0.5")
ax.set_xlabel("Recall")
ax.set_ylabel("Precision")
ax.set_xlim(0, 1)
diff --git a/utils/plots.py b/utils/plots.py
index 9bec34a15..f70775f26 100644
--- a/utils/plots.py
+++ b/utils/plots.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Plotting utils."""
import contextlib
@@ -29,7 +29,8 @@ matplotlib.use("Agg") # for writing to files only
class Colors:
- # Ultralytics color palette https://ultralytics.com/
+ """Provides an RGB color palette derived from Ultralytics color scheme for visualization tasks."""
+
def __init__(self):
"""
Initializes the Colors class with a palette derived from Ultralytics color scheme, converting hex codes to RGB.
@@ -81,7 +82,7 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path("runs/detec
module_type: Module type
stage: Module stage within model
n: Maximum number of feature maps to plot
- save_dir: Directory to save results
+ save_dir: Directory to save results.
"""
if ("Detect" not in module_type) and (
"Segment" not in module_type
@@ -182,7 +183,7 @@ def plot_images(images, targets, paths=None, fname="images.jpg", names=None):
# Annotate
fs = int((h + w) * ns * 0.01) # font size
annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
- for i in range(i + 1):
+ for i in range(bs):
x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
if paths:
diff --git a/utils/segment/__init__.py b/utils/segment/__init__.py
index e69de29bb..77a19dcf0 100644
--- a/utils/segment/__init__.py
+++ b/utils/segment/__init__.py
@@ -0,0 +1 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
diff --git a/utils/segment/augmentations.py b/utils/segment/augmentations.py
index 2e1dca119..14a81cf7d 100644
--- a/utils/segment/augmentations.py
+++ b/utils/segment/augmentations.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Image augmentation functions."""
import math
@@ -69,16 +69,8 @@ def random_perspective(
else: # affine
im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
- # Visualize
- # import matplotlib.pyplot as plt
- # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
- # ax[0].imshow(im[:, :, ::-1]) # base
- # ax[1].imshow(im2[:, :, ::-1]) # warped
-
- # Transform label coordinates
- n = len(targets)
new_segments = []
- if n:
+ if n := len(targets):
new = np.zeros((n, 4))
segments = resample_segments(segments) # upsample
for i, segment in enumerate(segments):
diff --git a/utils/segment/dataloaders.py b/utils/segment/dataloaders.py
index c2be5f0df..2363d7265 100644
--- a/utils/segment/dataloaders.py
+++ b/utils/segment/dataloaders.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Dataloaders."""
import os
@@ -75,6 +75,7 @@ def create_dataloader(
shuffle=shuffle and sampler is None,
num_workers=nw,
sampler=sampler,
+ drop_last=quad,
pin_memory=True,
collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn,
worker_init_fn=seed_worker,
@@ -83,6 +84,8 @@ def create_dataloader(
class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing
+ """Loads images, labels, and segmentation masks for training and testing YOLO models with augmentation support."""
+
def __init__(
self,
path,
@@ -129,9 +132,7 @@ class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing
index = self.indices[index] # linear, shuffled, or image_weights
hyp = self.hyp
- mosaic = self.mosaic and random.random() < hyp["mosaic"]
- masks = []
- if mosaic:
+ if mosaic := self.mosaic and random.random() < hyp["mosaic"]:
# Load mosaic
img, labels, segments = self.load_mosaic(index)
shapes = None
@@ -177,6 +178,7 @@ class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing
)
nl = len(labels) # number of labels
+ masks = []
if nl:
labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3)
if self.overlap:
diff --git a/utils/segment/general.py b/utils/segment/general.py
index 0793470a9..6a71c2551 100644
--- a/utils/segment/general.py
+++ b/utils/segment/general.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import cv2
import numpy as np
@@ -28,7 +28,7 @@ def process_mask_upsample(protos, masks_in, bboxes, shape):
protos: [mask_dim, mask_h, mask_w]
masks_in: [n, mask_dim], n is number of masks after nms
bboxes: [n, 4], n is number of masks after nms
- shape: input_image_size, (h, w)
+ shape: input_image_size, (h, w).
return: h, w, n
"""
@@ -45,7 +45,7 @@ def process_mask(protos, masks_in, bboxes, shape, upsample=False):
proto_out: [mask_dim, mask_h, mask_w]
out_masks: [n, mask_dim], n is number of masks after nms
bboxes: [n, 4], n is number of masks after nms
- shape:input_image_size, (h, w)
+ shape:input_image_size, (h, w).
return: h, w, n
"""
@@ -71,7 +71,7 @@ def process_mask_native(protos, masks_in, bboxes, shape):
protos: [mask_dim, mask_h, mask_w]
masks_in: [n, mask_dim], n is number of masks after nms
bboxes: [n, 4], n is number of masks after nms
- shape: input_image_size, (h, w)
+ shape: input_image_size, (h, w).
return: h, w, n
"""
@@ -92,7 +92,7 @@ def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
"""
img1_shape: model input shape, [h, w]
img0_shape: origin pic shape, [h, w, 3]
- masks: [h, w, num]
+ masks: [h, w, num].
"""
# Rescale coordinates (xyxy) from im1_shape to im0_shape
if ratio_pad is None: # calculate from im0_shape
@@ -120,7 +120,7 @@ def mask_iou(mask1, mask2, eps=1e-7):
"""
mask1: [N, n] m1 means number of predicted objects
mask2: [M, n] m2 means number of gt objects
- Note: n means image_w x image_h
+ Note: n means image_w x image_h.
return: masks iou, [N, M]
"""
@@ -133,7 +133,7 @@ def masks_iou(mask1, mask2, eps=1e-7):
"""
mask1: [N, n] m1 means number of predicted objects
mask2: [N, n] m2 means number of gt objects
- Note: n means image_w x image_h
+ Note: n means image_w x image_h.
return: masks iou, (N, )
"""
diff --git a/utils/segment/loss.py b/utils/segment/loss.py
index d4bc9d3ae..6ef590620 100644
--- a/utils/segment/loss.py
+++ b/utils/segment/loss.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import torch
import torch.nn as nn
@@ -12,7 +12,8 @@ from .general import crop_mask
class ComputeLoss:
- # Compute losses
+ """Computes the YOLOv5 model's loss components including classification, objectness, box, and mask losses."""
+
def __init__(self, model, autobalance=False, overlap=False):
"""Initializes the compute loss function for YOLOv5 models with options for autobalancing and overlap
handling.
@@ -60,8 +61,7 @@ class ComputeLoss:
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
- n = b.shape[0] # number of targets
- if n:
+ if n := b.shape[0]:
pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1) # subset of predictions
# Box regression
diff --git a/utils/segment/metrics.py b/utils/segment/metrics.py
index 6f57dec13..3bb7aeec4 100644
--- a/utils/segment/metrics.py
+++ b/utils/segment/metrics.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Model validation metrics."""
import numpy as np
@@ -54,6 +54,8 @@ def ap_per_class_box_and_mask(
class Metric:
+ """Computes performance metrics like precision, recall, F1 score, and average precision for model evaluation."""
+
def __init__(self) -> None:
"""Initializes performance metric attributes for precision, recall, F1 score, average precision, and class
indices.
@@ -127,7 +129,7 @@ class Metric:
return (self.mp, self.mr, self.map50, self.map)
def class_result(self, i):
- """Class-aware result, return p[i], r[i], ap50[i], ap[i]"""
+ """Class-aware result, return p[i], r[i], ap50[i], ap[i]."""
return (self.p[i], self.r[i], self.ap50[i], self.ap[i])
def get_maps(self, nc):
@@ -140,7 +142,7 @@ class Metric:
def update(self, results):
"""
Args:
- results: tuple(p, r, ap, f1, ap_class)
+ results: tuple(p, r, ap, f1, ap_class).
"""
p, r, all_ap, f1, ap_class_index = results
self.p = p
@@ -163,7 +165,7 @@ class Metrics:
def update(self, results):
"""
Args:
- results: Dict{'boxes': Dict{}, 'masks': Dict{}}
+ results: Dict{'boxes': Dict{}, 'masks': Dict{}}.
"""
self.metric_box.update(list(results["boxes"].values()))
self.metric_mask.update(list(results["masks"].values()))
diff --git a/utils/segment/plots.py b/utils/segment/plots.py
index f5b81711c..5619e9de9 100644
--- a/utils/segment/plots.py
+++ b/utils/segment/plots.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import contextlib
import math
diff --git a/utils/torch_utils.py b/utils/torch_utils.py
index d15f1f73f..8b3c43b5b 100644
--- a/utils/torch_utils.py
+++ b/utils/torch_utils.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""PyTorch utils."""
import math
@@ -121,9 +121,9 @@ def select_device(device="", batch_size=0, newline=True):
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # force torch.cuda.is_available() = False
elif device: # non-cpu device requested
os.environ["CUDA_VISIBLE_DEVICES"] = device # set environment variable - must be before assert is_available()
- assert torch.cuda.is_available() and torch.cuda.device_count() >= len(
- device.replace(",", "")
- ), f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
+ assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(",", "")), (
+ f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
+ )
if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available
devices = device.split(",") if device else "0" # range(torch.cuda.device_count()) # i.e. 0,1,6,7
@@ -161,7 +161,7 @@ def profile(input, ops, n=10, device=None):
input = torch.randn(16, 3, 640, 640)
m1 = lambda x: x * torch.sigmoid(x)
m2 = nn.SiLU()
- profile(input, [m1, m2], n=100) # profile over 100 iterations
+ profile(input, [m1, m2], n=100) # profile over 100 iterations.
"""
results = []
if not isinstance(device, torch.device):
@@ -380,7 +380,7 @@ def smart_optimizer(model, name="Adam", lr=0.001, momentum=0.9, decay=1e-5):
optimizer.add_param_group({"params": g[1], "weight_decay": 0.0}) # add g1 (BatchNorm2d weights)
LOGGER.info(
f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
- f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias'
+ f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias"
)
return optimizer
@@ -420,7 +420,8 @@ def smart_resume(ckpt, optimizer, ema=None, weights="yolov5s.pt", epochs=300, re
class EarlyStopping:
- # YOLOv5 simple early stopper
+ """Implements early stopping to halt training when no improvement is observed for a specified number of epochs."""
+
def __init__(self, patience=30):
"""Initializes simple early stopping mechanism for YOLOv5, with adjustable patience for non-improving epochs."""
self.best_fitness = 0.0 # i.e. mAP
@@ -449,7 +450,7 @@ class EarlyStopping:
class ModelEMA:
"""Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
Keeps a moving average of everything in the model state_dict (parameters and buffers)
- For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+ For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage.
"""
def __init__(self, model, decay=0.9999, tau=2000, updates=0):
diff --git a/utils/triton.py b/utils/triton.py
index 2fee42815..07531ea7e 100644
--- a/utils/triton.py
+++ b/utils/triton.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Utils to interact with the Triton Inference Server."""
import typing
@@ -18,7 +18,6 @@ class TritonRemoteModel:
def __init__(self, url: str):
"""
Keyword Arguments:
- url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000
"""
parsed_url = urlparse(url)
if parsed_url.scheme == "grpc":
diff --git a/val.py b/val.py
index b8db6122f..600353b1c 100644
--- a/val.py
+++ b/val.py
@@ -1,4 +1,4 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""
Validate a trained YOLOv5 detection model on a detection dataset.
