PyCharm reformat (#4209)

* PyCharm reformat

* YAML reformat

* Markdown reformat

.github/ISSUE_TEMPLATE/bug-report.md

@@ -7,21 +7,24 @@ assignees: ''
 
 ---
 
-Before submitting a bug report, please be aware that your issue **must be reproducible** with all of the following, otherwise it is non-actionable, and we can not help you:
+Before submitting a bug report, please be aware that your issue **must be reproducible** with all of the following,
- - **Current repo**: run `git fetch && git status -uno` to check and `git pull` to update repo
+otherwise it is non-actionable, and we can not help you:
- - **Common dataset**: coco.yaml or coco128.yaml
- - **Common environment**: Colab, Google Cloud, or Docker image. See https://github.com/ultralytics/yolov5#environments
 
-If this is a custom dataset/training question you **must include** your `train*.jpg`, `val*.jpg` and `results.png` figures, or we can not help you. You can generate these with `utils.plot_results()`.
+- **Current repo**: run `git fetch && git status -uno` to check and `git pull` to update repo
+- **Common dataset**: coco.yaml or coco128.yaml
+- **Common environment**: Colab, Google Cloud, or Docker image. See https://github.com/ultralytics/yolov5#environments
 
+If this is a custom dataset/training question you **must include** your `train*.jpg`, `val*.jpg` and `results.png`
+figures, or we can not help you. You can generate these with `utils.plot_results()`.
 
 ## 🐛 Bug
-A clear and concise description of what the bug is.
 
+A clear and concise description of what the bug is.
 
 ## To Reproduce (REQUIRED)
 
 Input:
+
 ```
 import torch
 
@@ -30,6 +33,7 @@ c = a / 0
 ```
 
 Output:
+
 ```
 Traceback (most recent call last):
   File "/Users/glennjocher/opt/anaconda3/envs/env1/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
@@ -39,17 +43,17 @@ Traceback (most recent call last):
 RuntimeError: ZeroDivisionError
 ```
 
-
 ## Expected behavior
+
 A clear and concise description of what you expected to happen.
 
-
 ## Environment
+
 If applicable, add screenshots to help explain your problem.
 
- - OS: [e.g. Ubuntu]
+- OS: [e.g. Ubuntu]
- - GPU [e.g. 2080 Ti]
+- GPU [e.g. 2080 Ti]
-
 
 ## Additional context
+
 Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature-request.md

@@ -13,7 +13,8 @@ assignees: ''
 
 ## Motivation
 
-<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
+<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? 
+e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
 
 ## Pitch
 

.github/ISSUE_TEMPLATE/question.md

@@ -9,5 +9,4 @@ assignees: ''
 
 ## ❔Question
 
-
 ## Additional context

CONTRIBUTING.md

@@ -8,32 +8,44 @@ We love your input! We want to make contributing to YOLOv5 as easy and transpare
 - Proposing a new feature
 - Becoming a maintainer
 
-YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be helping push the frontiers of what's possible in AI 😃!
+YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be
-
+helping push the frontiers of what's possible in AI 😃!
 
 ## Submitting a Pull Request (PR) 🛠️
+
 Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps:
 
 ### 1. Select File to Update
+
 Select `requirements.txt` to update by clicking on it in GitHub.
 <p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p>
 
 ### 2. Click 'Edit this file'
+
 Button is in top-right corner.
 <p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p>
 
 ### 3. Make Changes
+
 Change `matplotlib` version from `3.2.2` to `3.3`.
 <p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p>
 
 ### 4. Preview Changes and Submit PR
-Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch** for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃!
+
+Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch**
+for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose
+changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃!
 <p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
 
 ### PR recommendations
 
 To allow your work to be integrated as seamlessly as possible, we advise you to:
-- ✅ Verify your PR is **up-to-date with origin/master.** If your PR is behind origin/master an automatic [GitHub actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) rebase may be attempted by including the /rebase command in a comment body, or by running the following code, replacing 'feature' with the name of your local branch:
+
+- ✅ Verify your PR is **up-to-date with origin/master.** If your PR is behind origin/master an
+  automatic [GitHub actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) rebase may
+  be attempted by including the /rebase command in a comment body, or by running the following code, replacing 'feature'
+  with the name of your local branch:
+
 ```bash
 git remote add upstream https://github.com/ultralytics/yolov5.git
 git fetch upstream
@@ -41,30 +53,42 @@ git checkout feature  # <----- replace 'feature' with local branch name
 git merge upstream/master
 git push -u origin -f
 ```
-- ✅ Verify all 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
 
+- ✅ Verify all 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
 
 ## Submitting a Bug Report 🐛
 
 If you spot a problem with YOLOv5 please submit a Bug Report!
 
-For us to start investigating a possibel problem we need to be able to reproduce it ourselves first. We've created a few short guidelines below to help users provide what we need in order to get started.
+For us to start investigating a possibel problem we need to be able to reproduce it ourselves first. We've created a few
+short guidelines below to help users provide what we need in order to get started.
 
-When asking a question, people will be better able to provide help if you provide **code** that they can easily understand and use to **reproduce** the problem. This is referred to by community members as creating a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces the problem should be:
+When asking a question, people will be better able to provide help if you provide **code** that they can easily
+understand and use to **reproduce** the problem. This is referred to by community members as creating
+a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces
+the problem should be:
 
 * ✅ **Minimal** – Use as little code as possible that still produces the same problem
 * ✅ **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://ultralytics.com/) to provide assistance your code
+should be:
 
-* ✅ **Current** – Verify that your code is up-to-date with 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.
+* ✅ **Current** – Verify that your code is up-to-date with current
-* ✅ **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 ⚠️.
+  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.
-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 providing a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better understand and diagnose your problem. 
+* ✅ **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 ⚠️.
 
+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 providing
+a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better
+understand and diagnose your problem.
 
 ## License
 
-By contributing, you agree that your contributions will be licensed under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/)
+By contributing, you agree that your contributions will be licensed under
+the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/)

README.md

@@ -52,31 +52,33 @@ YOLOv5 🚀 is a family of object detection architectures and models pretrained
 
 </div>
 
-
 ## <div align="center">Documentation</div>
 
 See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
 
-
 ## <div align="center">Quick Start Examples</div>
 
-
 <details open>
 <summary>Install</summary>
 
-[**Python>=3.6.0**](https://www.python.org/) is required with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
+[**Python>=3.6.0**](https://www.python.org/) is required with all
+[requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including
+[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
 <!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
+
 ```bash
 $ git clone https://github.com/ultralytics/yolov5
 $ cd yolov5
 $ pip install -r requirements.txt
 ```
+
 </details>
 
 <details open>
 <summary>Inference</summary>
 
-Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases).
+Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download
+from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases).
 
 ```python
 import torch
@@ -85,7 +87,7 @@ import torch
 model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, custom
 
 # Images
-img = 'https://ultralytics.com/images/zidane.jpg'  # or PosixPath, PIL, OpenCV, numpy, list
+img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list
 
 # Inference
 results = model(img)
@@ -101,7 +103,9 @@ results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
 <details>
 <summary>Inference with detect.py</summary>
 
-`detect.py` runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
+`detect.py` runs inference on a variety of sources, downloading models automatically from
+the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
+
 ```bash
 $ python detect.py --source 0  # webcam
                             file.jpg  # image 
@@ -117,13 +121,18 @@ $ python detect.py --source 0  # webcam
 <details>
 <summary>Training</summary>
 
-Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices).
+Run commands below to reproduce results
+on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on
+first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the
+largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices).
+
 ```bash
 $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
                                          yolov5m                                40
                                          yolov5l                                24
                                          yolov5x                                16
 ```
+
 <img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
 
 </details>  
@@ -132,7 +141,8 @@ $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size
 <summary>Tutorials</summary>
 
 * [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)&nbsp; 🚀 RECOMMENDED
-* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)&nbsp; ☘️ RECOMMENDED
+* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)&nbsp; ☘️
+  RECOMMENDED
 * [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)&nbsp; 🌟 NEW
 * [Supervisely Ecosystem](https://github.com/ultralytics/yolov5/issues/2518)&nbsp; 🌟 NEW
 * [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
@@ -147,10 +157,11 @@ $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size
 
 </details>
 
-
 ## <div align="center">Environments and Integrations</div>
 
-Get started in seconds with our verified environments and integrations, including [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) for automatic YOLOv5 experiment logging. Click each icon below for details.
+Get started in seconds with our verified environments and integrations,
+including [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme) for automatic YOLOv5 experiment
+logging. Click each icon below for details.
 
 <div align="center">
     <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
@@ -173,7 +184,6 @@ Get started in seconds with our verified environments and integrations, includin
     </a>
 </div>  
 
-
 ## <div align="center">Compete and Win</div>
 
 We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!
@@ -183,7 +193,6 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
   <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"></a>
 </p>
 
-
 ## <div align="center">Why YOLOv5</div>
 
 <p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313216-f0a5e100-9af5-11eb-8445-c682b60da2e3.png"></p>
@@ -195,11 +204,13 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
 <details>
   <summary>Figure Notes (click to expand)</summary>
 
-  * GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. 
+* GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size
-  * EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
+  32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
-  * **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
+* EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
-</details>
+* **Reproduce** by
+  `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
 
+</details>
 
 ### Pretrained Checkpoints
 
@@ -222,23 +233,29 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
 <details>
   <summary>Table Notes (click to expand)</summary>
 
-  * AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy.  
+* AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results
-  * AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`  
+  denote val2017 accuracy.
-  * Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half`  
+* AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP**
-  * All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). 
+  by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
-  * Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale augmentation. **Reproduce TTA** by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+* Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a
-</details>
+  GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and
+  includes FP16 inference, postprocessing and NMS. **Reproduce speed**
+  by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half`
+* All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
+* Test Time Augmentation ([TTA](https://github.com/ultralytics/yolov5/issues/303)) includes reflection and scale
+  augmentation. **Reproduce TTA** by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
 
+</details>
 
 ## <div align="center">Contribute</div>
 
-We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started. 
+We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see
-
+our [Contributing Guide](CONTRIBUTING.md) to get started.
 
 ## <div align="center">Contact</div>
 
-For issues running YOLOv5 please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business or professional support requests please visit 
+For issues running YOLOv5 please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business or
-[https://ultralytics.com/contact](https://ultralytics.com/contact).
+professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
 
 <br>
 

data/Argoverse.yaml

@@ -15,7 +15,7 @@ test: Argoverse-1.1/images/test/  # test images (optional) https://eval.ai/web/c
 
 # Classes
 nc: 8  # number of classes
-names: [ 'person',  'bicycle',  'car',  'motorcycle',  'bus',  'truck',  'traffic_light',  'stop_sign' ]  # class names
+names: ['person',  'bicycle',  'car',  'motorcycle',  'bus',  'truck',  'traffic_light',  'stop_sign']  # class names
 
 
 # Download script/URL (optional) ---------------------------------------------------------------------------------------

data/GlobalWheat2020.yaml

@@ -27,7 +27,7 @@ test: # test images (optional) 1276 images
 
 # Classes
 nc: 1  # number of classes
-names: [ 'wheat_head' ]  # class names
+names: ['wheat_head']  # class names
 
 
 # Download script/URL (optional) ---------------------------------------------------------------------------------------

data/Objects365.yaml

@@ -15,7 +15,7 @@ test:  # test images (optional)
 
 # Classes
 nc: 365  # number of classes
-names: [ 'Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup',
+names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup',
         'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book',
         'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag',
         'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV',
@@ -55,7 +55,7 @@ names: [ 'Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Gl
         'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French',
         'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell',
         'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil',
-         'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis' ]
+        'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis']
 
 
 # Download script/URL (optional) ---------------------------------------------------------------------------------------

data/SKU-110K.yaml

@@ -15,7 +15,7 @@ test: test.txt  # test images (optional)  2936 images
 
 # Classes
 nc: 1  # number of classes
-names: [ 'object' ]  # class names
+names: ['object']  # class names
 
 
 # Download script/URL (optional) ---------------------------------------------------------------------------------------

data/VOC.yaml

@@ -21,8 +21,8 @@ test: # test images (optional)
 
 # Classes
 nc: 20  # number of classes
-names: [ 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
+names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
-         'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' ]  # class names
+        'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']  # class names
 
 
 # Download script/URL (optional) ---------------------------------------------------------------------------------------

data/VisDrone.yaml

@@ -15,7 +15,7 @@ test: VisDrone2019-DET-test-dev/images  # test images (optional)  1610 images
 
 # Classes
 nc: 10  # number of classes
-names: [ 'pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor' ]
+names: ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']
 
 
 # Download script/URL (optional) ---------------------------------------------------------------------------------------

data/coco.yaml

@@ -15,7 +15,7 @@ test: test-dev2017.txt  # 20288 of 40670 images, submit to https://competitions.
 
 # Classes
 nc: 80  # number of classes
-names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
         'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
         'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
         'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
@@ -23,7 +23,7 @@ names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', '
         'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
         'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
         'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
-         'hair drier', 'toothbrush' ]  # class names
+        'hair drier', 'toothbrush']  # class names
 
 
 # Download script/URL (optional)

data/coco128.yaml

@@ -15,7 +15,7 @@ test:  # test images (optional)
 
 # Classes
 nc: 80  # number of classes
-names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
         'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
         'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
         'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
@@ -23,7 +23,7 @@ names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', '
         'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
         'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
         'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
-         'hair drier', 'toothbrush' ]  # class names
+        'hair drier', 'toothbrush']  # class names
 
 
 # Download script/URL (optional)

data/scripts/get_coco.sh

@@ -12,7 +12,7 @@ d='../datasets' # unzip directory
 url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
 f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
 echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
+curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
 
 # Download/unzip images
 d='../datasets/coco/images' # unzip directory
@@ -22,6 +22,6 @@ f2='val2017.zip'   # 1G, 5k images
 f3='test2017.zip'  # 7G, 41k images (optional)
 for f in $f1 $f2; do
   echo 'Downloading' $url$f '...'
-  curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
+  curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
 done
 wait # finish background tasks

data/scripts/get_coco128.sh

@@ -12,6 +12,6 @@ d='../datasets' # unzip directory
 url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
 f='coco128.zip' # or 'coco2017labels-segments.zip', 68 MB
 echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
+curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
 
 wait # finish background tasks

data/xView.yaml

@@ -15,7 +15,7 @@ val: images/autosplit_val.txt  # train images (relative to 'path') 10% of 847 tr
 
 # Classes
 nc: 60  # number of classes
-names: [ 'Fixed-wing Aircraft', 'Small Aircraft', 'Cargo Plane', 'Helicopter', 'Passenger Vehicle', 'Small Car', 'Bus',
+names: ['Fixed-wing Aircraft', 'Small Aircraft', 'Cargo Plane', 'Helicopter', 'Passenger Vehicle', 'Small Car', 'Bus',
         'Pickup Truck', 'Utility Truck', 'Truck', 'Cargo Truck', 'Truck w/Box', 'Truck Tractor', 'Trailer',
         'Truck w/Flatbed', 'Truck w/Liquid', 'Crane Truck', 'Railway Vehicle', 'Passenger Car', 'Cargo Car',
         'Flat Car', 'Tank car', 'Locomotive', 'Maritime Vessel', 'Motorboat', 'Sailboat', 'Tugboat', 'Barge',
@@ -23,7 +23,7 @@ names: [ 'Fixed-wing Aircraft', 'Small Aircraft', 'Cargo Plane', 'Helicopter', '
         'Container Crane', 'Reach Stacker', 'Straddle Carrier', 'Mobile Crane', 'Dump Truck', 'Haul Truck',
         'Scraper/Tractor', 'Front loader/Bulldozer', 'Excavator', 'Cement Mixer', 'Ground Grader', 'Hut/Tent', 'Shed',
         'Building', 'Aircraft Hangar', 'Damaged Building', 'Facility', 'Construction Site', 'Vehicle Lot', 'Helipad',
-         'Storage Tank', 'Shipping container lot', 'Shipping Container', 'Pylon', 'Tower' ]  # class names
+        'Storage Tank', 'Shipping container lot', 'Shipping Container', 'Pylon', 'Tower']  # class names
 
 
 # Download script/URL (optional) ---------------------------------------------------------------------------------------

models/hub/anchors.yaml

@@ -4,55 +4,55 @@
 # P5 -------------------------------------------------------------------------------------------------------------------
 # P5-640:
 anchors_p5_640:
-  - [ 10,13, 16,30, 33,23 ]  # P3/8
+  - [10,13, 16,30, 33,23]  # P3/8
-  - [ 30,61, 62,45, 59,119 ]  # P4/16
+  - [30,61, 62,45, 59,119]  # P4/16
-  - [ 116,90, 156,198, 373,326 ]  # P5/32
+  - [116,90, 156,198, 373,326]  # P5/32
 
 
 # P6 -------------------------------------------------------------------------------------------------------------------
 # P6-640:  thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11,  21,19,  17,41,  43,32,  39,70,  86,64,  65,131,  134,130,  120,265,  282,180,  247,354,  512,387
 anchors_p6_640:
-  - [ 9,11,  21,19,  17,41 ]  # P3/8
+  - [9,11,  21,19,  17,41]  # P3/8
-  - [ 43,32,  39,70,  86,64 ]  # P4/16
+  - [43,32,  39,70,  86,64]  # P4/16
-  - [ 65,131,  134,130,  120,265 ]  # P5/32
+  - [65,131,  134,130,  120,265]  # P5/32
-  - [ 282,180,  247,354,  512,387 ]  # P6/64
+  - [282,180,  247,354,  512,387]  # P6/64
 
 # P6-1280:  thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27,  44,40,  38,94,  96,68,  86,152,  180,137,  140,301,  303,264,  238,542,  436,615,  739,380,  925,792
 anchors_p6_1280:
-  - [ 19,27,  44,40,  38,94 ]  # P3/8
+  - [19,27,  44,40,  38,94]  # P3/8
-  - [ 96,68,  86,152,  180,137 ]  # P4/16
+  - [96,68,  86,152,  180,137]  # P4/16
-  - [ 140,301,  303,264,  238,542 ]  # P5/32
+  - [140,301,  303,264,  238,542]  # P5/32
-  - [ 436,615,  739,380,  925,792 ]  # P6/64
+  - [436,615,  739,380,  925,792]  # P6/64
 
 # P6-1920:  thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41,  67,59,  57,141,  144,103,  129,227,  270,205,  209,452,  455,396,  358,812,  653,922,  1109,570,  1387,1187
 anchors_p6_1920:
-  - [ 28,41,  67,59,  57,141 ]  # P3/8
+  - [28,41,  67,59,  57,141]  # P3/8
-  - [ 144,103,  129,227,  270,205 ]  # P4/16
+  - [144,103,  129,227,  270,205]  # P4/16
-  - [ 209,452,  455,396,  358,812 ]  # P5/32
+  - [209,452,  455,396,  358,812]  # P5/32
-  - [ 653,922,  1109,570,  1387,1187 ]  # P6/64
+  - [653,922,  1109,570,  1387,1187]  # P6/64
 
 
 # P7 -------------------------------------------------------------------------------------------------------------------
 # P7-640:  thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11,  13,30,  29,20,  30,46,  61,38,  39,92,  78,80,  146,66,  79,163,  149,150,  321,143,  157,303,  257,402,  359,290,  524,372
 anchors_p7_640:
-  - [ 11,11,  13,30,  29,20 ]  # P3/8
+  - [11,11,  13,30,  29,20]  # P3/8
-  - [ 30,46,  61,38,  39,92 ]  # P4/16
+  - [30,46,  61,38,  39,92]  # P4/16
-  - [ 78,80,  146,66,  79,163 ]  # P5/32
+  - [78,80,  146,66,  79,163]  # P5/32
-  - [ 149,150,  321,143,  157,303 ]  # P6/64
+  - [149,150,  321,143,  157,303]  # P6/64
-  - [ 257,402,  359,290,  524,372 ]  # P7/128
+  - [257,402,  359,290,  524,372]  # P7/128
 
 # P7-1280:  thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22,  54,36,  32,77,  70,83,  138,71,  75,173,  165,159,  148,334,  375,151,  334,317,  251,626,  499,474,  750,326,  534,814,  1079,818
 anchors_p7_1280:
-  - [ 19,22,  54,36,  32,77 ]  # P3/8
+  - [19,22,  54,36,  32,77]  # P3/8
-  - [ 70,83,  138,71,  75,173 ]  # P4/16
+  - [70,83,  138,71,  75,173]  # P4/16
-  - [ 165,159,  148,334,  375,151 ]  # P5/32
+  - [165,159,  148,334,  375,151]  # P5/32
-  - [ 334,317,  251,626,  499,474 ]  # P6/64
+  - [334,317,  251,626,  499,474]  # P6/64
-  - [ 750,326,  534,814,  1079,818 ]  # P7/128
+  - [750,326,  534,814,  1079,818]  # P7/128
 
 # P7-1920:  thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34,  81,55,  47,115,  105,124,  207,107,  113,259,  247,238,  222,500,  563,227,  501,476,  376,939,  749,711,  1126,489,  801,1222,  1618,1227
 anchors_p7_1920:
-  - [ 29,34,  81,55,  47,115 ]  # P3/8
+  - [29,34,  81,55,  47,115]  # P3/8
-  - [ 105,124,  207,107,  113,259 ]  # P4/16
+  - [105,124,  207,107,  113,259]  # P4/16
-  - [ 247,238,  222,500,  563,227 ]  # P5/32
+  - [247,238,  222,500,  563,227]  # P5/32
-  - [ 501,476,  376,939,  749,711 ]  # P6/64
+  - [501,476,  376,939,  749,711]  # P6/64
-  - [ 1126,489,  801,1222,  1618,1227 ]  # P7/128
+  - [1126,489,  801,1222,  1618,1227]  # P7/128

models/hub/yolov3-spp.yaml

@@ -3,47 +3,47 @@ nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
 anchors:
-  - [ 10,13, 16,30, 33,23 ]  # P3/8
+  - [10,13, 16,30, 33,23]  # P3/8
-  - [ 30,61, 62,45, 59,119 ]  # P4/16
+  - [30,61, 62,45, 59,119]  # P4/16
-  - [ 116,90, 156,198, 373,326 ]  # P5/32
+  - [116,90, 156,198, 373,326]  # P5/32
 
 # darknet53 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Conv, [ 32, 3, 1 ] ],  # 0
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
-    [ -1, 1, Conv, [ 64, 3, 2 ] ],  # 1-P1/2
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
-    [ -1, 1, Bottleneck, [ 64 ] ],
+   [-1, 1, Bottleneck, [64]],
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 3-P2/4
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
-    [ -1, 2, Bottleneck, [ 128 ] ],
+   [-1, 2, Bottleneck, [128]],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 5-P3/8
+   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
-    [ -1, 8, Bottleneck, [ 256 ] ],
+   [-1, 8, Bottleneck, [256]],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 7-P4/16
+   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
-    [ -1, 8, Bottleneck, [ 512 ] ],
+   [-1, 8, Bottleneck, [512]],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P5/32
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P5/32
-    [ -1, 4, Bottleneck, [ 1024 ] ],  # 10
+   [-1, 4, Bottleneck, [1024]],  # 10
   ]
 
 # YOLOv3-SPP head
 head:
-  [ [ -1, 1, Bottleneck, [ 1024, False ] ],
+  [[-1, 1, Bottleneck, [1024, False]],
-    [ -1, 1, SPP, [ 512, [ 5, 9, 13 ] ] ],
+   [-1, 1, SPP, [512, [5, 9, 13]]],
-    [ -1, 1, Conv, [ 1024, 3, 1 ] ],
+   [-1, 1, Conv, [1024, 3, 1]],
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
+   [-1, 1, Conv, [512, 1, 1]],
-    [ -1, 1, Conv, [ 1024, 3, 1 ] ],  # 15 (P5/32-large)
+   [-1, 1, Conv, [1024, 3, 1]],  # 15 (P5/32-large)
 
-    [ -2, 1, Conv, [ 256, 1, 1 ] ],
+   [-2, 1, Conv, [256, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
-    [ -1, 1, Bottleneck, [ 512, False ] ],
+   [-1, 1, Bottleneck, [512, False]],
-    [ -1, 1, Bottleneck, [ 512, False ] ],
+   [-1, 1, Bottleneck, [512, False]],
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+   [-1, 1, Conv, [256, 1, 1]],
-    [ -1, 1, Conv, [ 512, 3, 1 ] ],  # 22 (P4/16-medium)
+   [-1, 1, Conv, [512, 3, 1]],  # 22 (P4/16-medium)
 
-    [ -2, 1, Conv, [ 128, 1, 1 ] ],
+   [-2, 1, Conv, [128, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P3
-    [ -1, 1, Bottleneck, [ 256, False ] ],
+   [-1, 1, Bottleneck, [256, False]],
-    [ -1, 2, Bottleneck, [ 256, False ] ],  # 27 (P3/8-small)
+   [-1, 2, Bottleneck, [256, False]],  # 27 (P3/8-small)
 
-    [ [ 27, 22, 15 ], 1, Detect, [ nc, anchors ] ],   # Detect(P3, P4, P5)
+   [[27, 22, 15], 1, Detect, [nc, anchors]],   # Detect(P3, P4, P5)
   ]

models/hub/yolov3-tiny.yaml

@@ -3,37 +3,37 @@ nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
 anchors:
-  - [ 10,14, 23,27, 37,58 ]  # P4/16
+  - [10,14, 23,27, 37,58]  # P4/16
-  - [ 81,82, 135,169, 344,319 ]  # P5/32
+  - [81,82, 135,169, 344,319]  # P5/32
 
 # YOLOv3-tiny backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Conv, [ 16, 3, 1 ] ],  # 0
+  [[-1, 1, Conv, [16, 3, 1]],  # 0
-    [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ],  # 1-P1/2
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 1-P1/2
-    [ -1, 1, Conv, [ 32, 3, 1 ] ],
+   [-1, 1, Conv, [32, 3, 1]],
-    [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ],  # 3-P2/4
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 3-P2/4
-    [ -1, 1, Conv, [ 64, 3, 1 ] ],
+   [-1, 1, Conv, [64, 3, 1]],
-    [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ],  # 5-P3/8
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 5-P3/8
-    [ -1, 1, Conv, [ 128, 3, 1 ] ],
+   [-1, 1, Conv, [128, 3, 1]],
-    [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ],  # 7-P4/16
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 7-P4/16
-    [ -1, 1, Conv, [ 256, 3, 1 ] ],
+   [-1, 1, Conv, [256, 3, 1]],
-    [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ],  # 9-P5/32
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 9-P5/32
-    [ -1, 1, Conv, [ 512, 3, 1 ] ],
+   [-1, 1, Conv, [512, 3, 1]],
-    [ -1, 1, nn.ZeroPad2d, [ [ 0, 1, 0, 1 ] ] ],  # 11
+   [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]],  # 11
-    [ -1, 1, nn.MaxPool2d, [ 2, 1, 0 ] ],  # 12
+   [-1, 1, nn.MaxPool2d, [2, 1, 0]],  # 12
   ]
 
 # YOLOv3-tiny head
 head:
-  [ [ -1, 1, Conv, [ 1024, 3, 1 ] ],
+  [[-1, 1, Conv, [1024, 3, 1]],
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+   [-1, 1, Conv, [256, 1, 1]],
-    [ -1, 1, Conv, [ 512, 3, 1 ] ],  # 15 (P5/32-large)
+   [-1, 1, Conv, [512, 3, 1]],  # 15 (P5/32-large)
 
-    [ -2, 1, Conv, [ 128, 1, 1 ] ],
+   [-2, 1, Conv, [128, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
-    [ -1, 1, Conv, [ 256, 3, 1 ] ],  # 19 (P4/16-medium)
+   [-1, 1, Conv, [256, 3, 1]],  # 19 (P4/16-medium)
 
-    [ [ 19, 15 ], 1, Detect, [ nc, anchors ] ],  # Detect(P4, P5)
+   [[19, 15], 1, Detect, [nc, anchors]],  # Detect(P4, P5)
   ]

models/hub/yolov3.yaml

@@ -3,47 +3,47 @@ nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
 anchors:
-  - [ 10,13, 16,30, 33,23 ]  # P3/8
+  - [10,13, 16,30, 33,23]  # P3/8
-  - [ 30,61, 62,45, 59,119 ]  # P4/16
+  - [30,61, 62,45, 59,119]  # P4/16
-  - [ 116,90, 156,198, 373,326 ]  # P5/32
+  - [116,90, 156,198, 373,326]  # P5/32
 
 # darknet53 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Conv, [ 32, 3, 1 ] ],  # 0
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
-    [ -1, 1, Conv, [ 64, 3, 2 ] ],  # 1-P1/2
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
-    [ -1, 1, Bottleneck, [ 64 ] ],
+   [-1, 1, Bottleneck, [64]],
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 3-P2/4
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
-    [ -1, 2, Bottleneck, [ 128 ] ],
+   [-1, 2, Bottleneck, [128]],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 5-P3/8
+   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
-    [ -1, 8, Bottleneck, [ 256 ] ],
+   [-1, 8, Bottleneck, [256]],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 7-P4/16
+   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
-    [ -1, 8, Bottleneck, [ 512 ] ],
+   [-1, 8, Bottleneck, [512]],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P5/32
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P5/32
-    [ -1, 4, Bottleneck, [ 1024 ] ],  # 10
+   [-1, 4, Bottleneck, [1024]],  # 10
   ]
 
 # YOLOv3 head
 head:
-  [ [ -1, 1, Bottleneck, [ 1024, False ] ],
+  [[-1, 1, Bottleneck, [1024, False]],
-    [ -1, 1, Conv, [ 512, [ 1, 1 ] ] ],
+   [-1, 1, Conv, [512, [1, 1]]],
-    [ -1, 1, Conv, [ 1024, 3, 1 ] ],
+   [-1, 1, Conv, [1024, 3, 1]],
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
+   [-1, 1, Conv, [512, 1, 1]],
-    [ -1, 1, Conv, [ 1024, 3, 1 ] ],  # 15 (P5/32-large)
+   [-1, 1, Conv, [1024, 3, 1]],  # 15 (P5/32-large)
 
-    [ -2, 1, Conv, [ 256, 1, 1 ] ],
+   [-2, 1, Conv, [256, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
-    [ -1, 1, Bottleneck, [ 512, False ] ],
+   [-1, 1, Bottleneck, [512, False]],
-    [ -1, 1, Bottleneck, [ 512, False ] ],
+   [-1, 1, Bottleneck, [512, False]],
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+   [-1, 1, Conv, [256, 1, 1]],
-    [ -1, 1, Conv, [ 512, 3, 1 ] ],  # 22 (P4/16-medium)
+   [-1, 1, Conv, [512, 3, 1]],  # 22 (P4/16-medium)
 
-    [ -2, 1, Conv, [ 128, 1, 1 ] ],
+   [-2, 1, Conv, [128, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P3
-    [ -1, 1, Bottleneck, [ 256, False ] ],
+   [-1, 1, Bottleneck, [256, False]],
-    [ -1, 2, Bottleneck, [ 256, False ] ],  # 27 (P3/8-small)
+   [-1, 2, Bottleneck, [256, False]],  # 27 (P3/8-small)
 
-    [ [ 27, 22, 15 ], 1, Detect, [ nc, anchors ] ],   # Detect(P3, P4, P5)
+   [[27, 22, 15], 1, Detect, [nc, anchors]],   # Detect(P3, P4, P5)
   ]

models/hub/yolov5-fpn.yaml

@@ -3,38 +3,38 @@ nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
 anchors:
-  - [ 10,13, 16,30, 33,23 ]  # P3/8
+  - [10,13, 16,30, 33,23]  # P3/8
-  - [ 30,61, 62,45, 59,119 ]  # P4/16
+  - [30,61, 62,45, 59,119]  # P4/16
-  - [ 116,90, 156,198, 373,326 ]  # P5/32
+  - [116,90, 156,198, 373,326]  # P5/32
 
 # YOLOv5 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-    [ -1, 3, Bottleneck, [ 128 ] ],
+   [-1, 3, Bottleneck, [128]],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, BottleneckCSP, [ 256 ] ],
+   [-1, 9, BottleneckCSP, [256]],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-    [ -1, 9, BottleneckCSP, [ 512 ] ],
+   [-1, 9, BottleneckCSP, [512]],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 7-P5/32
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-    [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
+   [-1, 1, SPP, [1024, [5, 9, 13]]],
-    [ -1, 6, BottleneckCSP, [ 1024 ] ],  # 9
+   [-1, 6, BottleneckCSP, [1024]],  # 9
   ]
 
 # YOLOv5 FPN head
 head:
-  [ [ -1, 3, BottleneckCSP, [ 1024, False ] ],  # 10 (P5/32-large)
+  [[-1, 3, BottleneckCSP, [1024, False]],  # 10 (P5/32-large)
 
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
+   [-1, 1, Conv, [512, 1, 1]],
-    [ -1, 3, BottleneckCSP, [ 512, False ] ],  # 14 (P4/16-medium)
+   [-1, 3, BottleneckCSP, [512, False]],  # 14 (P4/16-medium)
 
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+   [-1, 1, Conv, [256, 1, 1]],
-    [ -1, 3, BottleneckCSP, [ 256, False ] ],  # 18 (P3/8-small)
+   [-1, 3, BottleneckCSP, [256, False]],  # 18 (P3/8-small)
 
-    [ [ 18, 14, 10 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5)
+   [[18, 14, 10], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
   ]

models/hub/yolov5-p2.yaml

@@ -7,46 +7,46 @@ anchors: 3
 # YOLOv5 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
+   [-1, 3, C3, [128]],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 9, C3, [256]],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
+   [-1, 9, C3, [512]],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 7-P5/32
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-    [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
+   [-1, 1, SPP, [1024, [5, 9, 13]]],
-    [ -1, 3, C3, [ 1024, False ] ],  # 9
+   [-1, 3, C3, [1024, False]],  # 9
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 512, 1, 1 ] ],
+  [[-1, 1, Conv, [512, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 13
+   [-1, 3, C3, [512, False]],  # 13
 
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+   [-1, 1, Conv, [256, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 17 (P3/8-small)
+   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
 
-    [ -1, 1, Conv, [ 128, 1, 1 ] ],
+   [-1, 1, Conv, [128, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 2 ], 1, Concat, [ 1 ] ],  # cat backbone P2
+   [[-1, 2], 1, Concat, [1]],  # cat backbone P2
-    [ -1, 1, C3, [ 128, False ] ],  # 21 (P2/4-xsmall)
+   [-1, 1, C3, [128, False]],  # 21 (P2/4-xsmall)
 
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],
+   [-1, 1, Conv, [128, 3, 2]],
-    [ [ -1, 18 ], 1, Concat, [ 1 ] ],  # cat head P3
+   [[-1, 18], 1, Concat, [1]],  # cat head P3
-    [ -1, 3, C3, [ 256, False ] ],  # 24 (P3/8-small)
+   [-1, 3, C3, [256, False]],  # 24 (P3/8-small)
 
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
+   [-1, 1, Conv, [256, 3, 2]],
-    [ [ -1, 14 ], 1, Concat, [ 1 ] ],  # cat head P4
+   [[-1, 14], 1, Concat, [1]],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 27 (P4/16-medium)
+   [-1, 3, C3, [512, False]],  # 27 (P4/16-medium)
 
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
+   [-1, 1, Conv, [512, 3, 2]],
-    [ [ -1, 10 ], 1, Concat, [ 1 ] ],  # cat head P5
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
-    [ -1, 3, C3, [ 1024, False ] ],  # 30 (P5/32-large)
+   [-1, 3, C3, [1024, False]],  # 30 (P5/32-large)
 
-    [ [ 24, 27, 30 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5)
+   [[24, 27, 30], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
   ]

models/hub/yolov5-p6.yaml

@@ -7,48 +7,48 @@ anchors: 3
 # YOLOv5 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
+   [-1, 3, C3, [128]],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 9, C3, [256]],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
+   [-1, 9, C3, [512]],
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 7-P5/32
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
-    [ -1, 3, C3, [ 768 ] ],
+   [-1, 3, C3, [768]],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P6/64
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
+   [-1, 1, SPP, [1024, [3, 5, 7]]],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+   [-1, 3, C3, [1024, False]],  # 11
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
+  [[-1, 1, Conv, [768, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P5
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P5
-    [ -1, 3, C3, [ 768, False ] ],  # 15
+   [-1, 3, C3, [768, False]],  # 15
 
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
+   [-1, 1, Conv, [512, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 19
+   [-1, 3, C3, [512, False]],  # 19
 
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+   [-1, 1, Conv, [256, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 23 (P3/8-small)
+   [-1, 3, C3, [256, False]],  # 23 (P3/8-small)
 
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
+   [-1, 1, Conv, [256, 3, 2]],
-    [ [ -1, 20 ], 1, Concat, [ 1 ] ],  # cat head P4
+   [[-1, 20], 1, Concat, [1]],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 26 (P4/16-medium)
+   [-1, 3, C3, [512, False]],  # 26 (P4/16-medium)
 
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
+   [-1, 1, Conv, [512, 3, 2]],
-    [ [ -1, 16 ], 1, Concat, [ 1 ] ],  # cat head P5
+   [[-1, 16], 1, Concat, [1]],  # cat head P5
-    [ -1, 3, C3, [ 768, False ] ],  # 29 (P5/32-large)
+   [-1, 3, C3, [768, False]],  # 29 (P5/32-large)
 
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],
+   [-1, 1, Conv, [768, 3, 2]],
-    [ [ -1, 12 ], 1, Concat, [ 1 ] ],  # cat head P6
+   [[-1, 12], 1, Concat, [1]],  # cat head P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 32 (P5/64-xlarge)
+   [-1, 3, C3, [1024, False]],  # 32 (P5/64-xlarge)
 
-    [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)
+   [[23, 26, 29, 32], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5, P6)
   ]

models/hub/yolov5-p7.yaml

@@ -7,59 +7,59 @@ anchors: 3
 # YOLOv5 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
+   [-1, 3, C3, [128]],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 9, C3, [256]],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
+   [-1, 9, C3, [512]],
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 7-P5/32
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
-    [ -1, 3, C3, [ 768 ] ],
+   [-1, 3, C3, [768]],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P6/64
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
-    [ -1, 3, C3, [ 1024 ] ],
+   [-1, 3, C3, [1024]],
-    [ -1, 1, Conv, [ 1280, 3, 2 ] ],  # 11-P7/128
+   [-1, 1, Conv, [1280, 3, 2]],  # 11-P7/128
-    [ -1, 1, SPP, [ 1280, [ 3, 5 ] ] ],
+   [-1, 1, SPP, [1280, [3, 5]]],
-    [ -1, 3, C3, [ 1280, False ] ],  # 13
+   [-1, 3, C3, [1280, False]],  # 13
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 1024, 1, 1 ] ],
+  [[-1, 1, Conv, [1024, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 10 ], 1, Concat, [ 1 ] ],  # cat backbone P6
+   [[-1, 10], 1, Concat, [1]],  # cat backbone P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 17
+   [-1, 3, C3, [1024, False]],  # 17
 
-    [ -1, 1, Conv, [ 768, 1, 1 ] ],
+   [-1, 1, Conv, [768, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P5
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P5
-    [ -1, 3, C3, [ 768, False ] ],  # 21
+   [-1, 3, C3, [768, False]],  # 21
 
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
+   [-1, 1, Conv, [512, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 25
+   [-1, 3, C3, [512, False]],  # 25
 
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+   [-1, 1, Conv, [256, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 29 (P3/8-small)
+   [-1, 3, C3, [256, False]],  # 29 (P3/8-small)
 
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
+   [-1, 1, Conv, [256, 3, 2]],
-    [ [ -1, 26 ], 1, Concat, [ 1 ] ],  # cat head P4
+   [[-1, 26], 1, Concat, [1]],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 32 (P4/16-medium)
+   [-1, 3, C3, [512, False]],  # 32 (P4/16-medium)
 
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
+   [-1, 1, Conv, [512, 3, 2]],
-    [ [ -1, 22 ], 1, Concat, [ 1 ] ],  # cat head P5
+   [[-1, 22], 1, Concat, [1]],  # cat head P5
-    [ -1, 3, C3, [ 768, False ] ],  # 35 (P5/32-large)
+   [-1, 3, C3, [768, False]],  # 35 (P5/32-large)
 
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],
+   [-1, 1, Conv, [768, 3, 2]],
-    [ [ -1, 18 ], 1, Concat, [ 1 ] ],  # cat head P6
+   [[-1, 18], 1, Concat, [1]],  # cat head P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 38 (P6/64-xlarge)
+   [-1, 3, C3, [1024, False]],  # 38 (P6/64-xlarge)
 
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],
+   [-1, 1, Conv, [1024, 3, 2]],
-    [ [ -1, 14 ], 1, Concat, [ 1 ] ],  # cat head P7
+   [[-1, 14], 1, Concat, [1]],  # cat head P7
-    [ -1, 3, C3, [ 1280, False ] ],  # 41 (P7/128-xxlarge)
+   [-1, 3, C3, [1280, False]],  # 41 (P7/128-xxlarge)
 
-    [ [ 29, 32, 35, 38, 41 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6, P7)
+   [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5, P6, P7)
   ]

models/hub/yolov5-panet.yaml

@@ -3,44 +3,44 @@ nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
 anchors:
-  - [ 10,13, 16,30, 33,23 ]  # P3/8
+  - [10,13, 16,30, 33,23]  # P3/8
-  - [ 30,61, 62,45, 59,119 ]  # P4/16
+  - [30,61, 62,45, 59,119]  # P4/16
-  - [ 116,90, 156,198, 373,326 ]  # P5/32
+  - [116,90, 156,198, 373,326]  # P5/32
 
 # YOLOv5 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-    [ -1, 3, BottleneckCSP, [ 128 ] ],
+   [-1, 3, BottleneckCSP, [128]],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, BottleneckCSP, [ 256 ] ],
+   [-1, 9, BottleneckCSP, [256]],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-    [ -1, 9, BottleneckCSP, [ 512 ] ],
+   [-1, 9, BottleneckCSP, [512]],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 7-P5/32
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-    [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
+   [-1, 1, SPP, [1024, [5, 9, 13]]],
-    [ -1, 3, BottleneckCSP, [ 1024, False ] ],  # 9
+   [-1, 3, BottleneckCSP, [1024, False]],  # 9
   ]
 
 # YOLOv5 PANet head
 head:
-  [ [ -1, 1, Conv, [ 512, 1, 1 ] ],
+  [[-1, 1, Conv, [512, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-    [ -1, 3, BottleneckCSP, [ 512, False ] ],  # 13
+   [-1, 3, BottleneckCSP, [512, False]],  # 13
 
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+   [-1, 1, Conv, [256, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-    [ -1, 3, BottleneckCSP, [ 256, False ] ],  # 17 (P3/8-small)
+   [-1, 3, BottleneckCSP, [256, False]],  # 17 (P3/8-small)
 
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
+   [-1, 1, Conv, [256, 3, 2]],
-    [ [ -1, 14 ], 1, Concat, [ 1 ] ],  # cat head P4
+   [[-1, 14], 1, Concat, [1]],  # cat head P4
-    [ -1, 3, BottleneckCSP, [ 512, False ] ],  # 20 (P4/16-medium)
+   [-1, 3, BottleneckCSP, [512, False]],  # 20 (P4/16-medium)
 
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
+   [-1, 1, Conv, [512, 3, 2]],
-    [ [ -1, 10 ], 1, Concat, [ 1 ] ],  # cat head P5
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
-    [ -1, 3, BottleneckCSP, [ 1024, False ] ],  # 23 (P5/32-large)
+   [-1, 3, BottleneckCSP, [1024, False]],  # 23 (P5/32-large)
 
-    [ [ 17, 20, 23 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5)
+   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
   ]

models/hub/yolov5l6.yaml

@@ -3,56 +3,56 @@ nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
 anchors:
-  - [ 19,27,  44,40,  38,94 ]  # P3/8
+  - [19,27,  44,40,  38,94]  # P3/8
-  - [ 96,68,  86,152,  180,137 ]  # P4/16
+  - [96,68,  86,152,  180,137]  # P4/16
-  - [ 140,301,  303,264,  238,542 ]  # P5/32
+  - [140,301,  303,264,  238,542]  # P5/32
-  - [ 436,615,  739,380,  925,792 ]  # P6/64
+  - [436,615,  739,380,  925,792]  # P6/64
 
 # YOLOv5 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
+   [-1, 3, C3, [128]],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 9, C3, [256]],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
+   [-1, 9, C3, [512]],
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 7-P5/32
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
-    [ -1, 3, C3, [ 768 ] ],
+   [-1, 3, C3, [768]],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P6/64
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
+   [-1, 1, SPP, [1024, [3, 5, 7]]],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+   [-1, 3, C3, [1024, False]],  # 11
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
+  [[-1, 1, Conv, [768, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P5
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P5
-    [ -1, 3, C3, [ 768, False ] ],  # 15
+   [-1, 3, C3, [768, False]],  # 15
 
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
+   [-1, 1, Conv, [512, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 19
+   [-1, 3, C3, [512, False]],  # 19
 
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+   [-1, 1, Conv, [256, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 23 (P3/8-small)
+   [-1, 3, C3, [256, False]],  # 23 (P3/8-small)
 
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
+   [-1, 1, Conv, [256, 3, 2]],
-    [ [ -1, 20 ], 1, Concat, [ 1 ] ],  # cat head P4
+   [[-1, 20], 1, Concat, [1]],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 26 (P4/16-medium)
+   [-1, 3, C3, [512, False]],  # 26 (P4/16-medium)
 
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
+   [-1, 1, Conv, [512, 3, 2]],
-    [ [ -1, 16 ], 1, Concat, [ 1 ] ],  # cat head P5
+   [[-1, 16], 1, Concat, [1]],  # cat head P5
-    [ -1, 3, C3, [ 768, False ] ],  # 29 (P5/32-large)
+   [-1, 3, C3, [768, False]],  # 29 (P5/32-large)
 
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],
+   [-1, 1, Conv, [768, 3, 2]],
-    [ [ -1, 12 ], 1, Concat, [ 1 ] ],  # cat head P6
+   [[-1, 12], 1, Concat, [1]],  # cat head P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 32 (P6/64-xlarge)
+   [-1, 3, C3, [1024, False]],  # 32 (P6/64-xlarge)
 
-    [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)
+   [[23, 26, 29, 32], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5, P6)
   ]

models/hub/yolov5m6.yaml

@@ -3,56 +3,56 @@ nc: 80  # number of classes
 depth_multiple: 0.67  # model depth multiple
 width_multiple: 0.75  # layer channel multiple
 anchors:
-  - [ 19,27,  44,40,  38,94 ]  # P3/8
+  - [19,27,  44,40,  38,94]  # P3/8
-  - [ 96,68,  86,152,  180,137 ]  # P4/16
+  - [96,68,  86,152,  180,137]  # P4/16
-  - [ 140,301,  303,264,  238,542 ]  # P5/32
+  - [140,301,  303,264,  238,542]  # P5/32
-  - [ 436,615,  739,380,  925,792 ]  # P6/64
+  - [436,615,  739,380,  925,792]  # P6/64
 
 # YOLOv5 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
+   [-1, 3, C3, [128]],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 9, C3, [256]],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
+   [-1, 9, C3, [512]],
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 7-P5/32
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
-    [ -1, 3, C3, [ 768 ] ],
+   [-1, 3, C3, [768]],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P6/64
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
+   [-1, 1, SPP, [1024, [3, 5, 7]]],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+   [-1, 3, C3, [1024, False]],  # 11
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
+  [[-1, 1, Conv, [768, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P5
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P5
-    [ -1, 3, C3, [ 768, False ] ],  # 15
+   [-1, 3, C3, [768, False]],  # 15
 
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
+   [-1, 1, Conv, [512, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 19
+   [-1, 3, C3, [512, False]],  # 19
 
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+   [-1, 1, Conv, [256, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 23 (P3/8-small)
+   [-1, 3, C3, [256, False]],  # 23 (P3/8-small)
 
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
+   [-1, 1, Conv, [256, 3, 2]],
-    [ [ -1, 20 ], 1, Concat, [ 1 ] ],  # cat head P4
+   [[-1, 20], 1, Concat, [1]],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 26 (P4/16-medium)
+   [-1, 3, C3, [512, False]],  # 26 (P4/16-medium)
 
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
+   [-1, 1, Conv, [512, 3, 2]],
-    [ [ -1, 16 ], 1, Concat, [ 1 ] ],  # cat head P5
+   [[-1, 16], 1, Concat, [1]],  # cat head P5
-    [ -1, 3, C3, [ 768, False ] ],  # 29 (P5/32-large)
+   [-1, 3, C3, [768, False]],  # 29 (P5/32-large)
 
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],
+   [-1, 1, Conv, [768, 3, 2]],
-    [ [ -1, 12 ], 1, Concat, [ 1 ] ],  # cat head P6
+   [[-1, 12], 1, Concat, [1]],  # cat head P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 32 (P6/64-xlarge)
+   [-1, 3, C3, [1024, False]],  # 32 (P6/64-xlarge)
 
-    [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)
+   [[23, 26, 29, 32], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5, P6)
   ]

models/hub/yolov5s-transformer.yaml

@@ -3,44 +3,44 @@ nc: 80  # number of classes
 depth_multiple: 0.33  # model depth multiple
 width_multiple: 0.50  # layer channel multiple
 anchors:
-  - [ 10,13, 16,30, 33,23 ]  # P3/8
+  - [10,13, 16,30, 33,23]  # P3/8
-  - [ 30,61, 62,45, 59,119 ]  # P4/16
+  - [30,61, 62,45, 59,119]  # P4/16
-  - [ 116,90, 156,198, 373,326 ]  # P5/32
+  - [116,90, 156,198, 373,326]  # P5/32
 
 # YOLOv5 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
+   [-1, 3, C3, [128]],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 9, C3, [256]],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
+   [-1, 9, C3, [512]],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 7-P5/32
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-    [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
+   [-1, 1, SPP, [1024, [5, 9, 13]]],
-    [ -1, 3, C3TR, [ 1024, False ] ],  # 9  <-------- C3TR() Transformer module
+   [-1, 3, C3TR, [1024, False]],  # 9  <-------- C3TR() Transformer module
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 512, 1, 1 ] ],
+  [[-1, 1, Conv, [512, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 13
+   [-1, 3, C3, [512, False]],  # 13
 
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+   [-1, 1, Conv, [256, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 17 (P3/8-small)
+   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
 
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
+   [-1, 1, Conv, [256, 3, 2]],
-    [ [ -1, 14 ], 1, Concat, [ 1 ] ],  # cat head P4
+   [[-1, 14], 1, Concat, [1]],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 20 (P4/16-medium)
+   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
 
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
+   [-1, 1, Conv, [512, 3, 2]],
-    [ [ -1, 10 ], 1, Concat, [ 1 ] ],  # cat head P5
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
-    [ -1, 3, C3, [ 1024, False ] ],  # 23 (P5/32-large)
+   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
 
-    [ [ 17, 20, 23 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5)
+   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
   ]

models/hub/yolov5s6.yaml

@@ -3,56 +3,56 @@ nc: 80  # number of classes
 depth_multiple: 0.33  # model depth multiple
 width_multiple: 0.50  # layer channel multiple
 anchors:
-  - [ 19,27,  44,40,  38,94 ]  # P3/8
+  - [19,27,  44,40,  38,94]  # P3/8
-  - [ 96,68,  86,152,  180,137 ]  # P4/16
+  - [96,68,  86,152,  180,137]  # P4/16
-  - [ 140,301,  303,264,  238,542 ]  # P5/32
+  - [140,301,  303,264,  238,542]  # P5/32
-  - [ 436,615,  739,380,  925,792 ]  # P6/64
+  - [436,615,  739,380,  925,792]  # P6/64
 
 # YOLOv5 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
+   [-1, 3, C3, [128]],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 9, C3, [256]],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
+   [-1, 9, C3, [512]],
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 7-P5/32
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
-    [ -1, 3, C3, [ 768 ] ],
+   [-1, 3, C3, [768]],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P6/64
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
+   [-1, 1, SPP, [1024, [3, 5, 7]]],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+   [-1, 3, C3, [1024, False]],  # 11
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
+  [[-1, 1, Conv, [768, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P5
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P5
-    [ -1, 3, C3, [ 768, False ] ],  # 15
+   [-1, 3, C3, [768, False]],  # 15
 
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
+   [-1, 1, Conv, [512, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 19
+   [-1, 3, C3, [512, False]],  # 19
 
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+   [-1, 1, Conv, [256, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 23 (P3/8-small)
+   [-1, 3, C3, [256, False]],  # 23 (P3/8-small)
 
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
+   [-1, 1, Conv, [256, 3, 2]],
-    [ [ -1, 20 ], 1, Concat, [ 1 ] ],  # cat head P4
+   [[-1, 20], 1, Concat, [1]],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 26 (P4/16-medium)
+   [-1, 3, C3, [512, False]],  # 26 (P4/16-medium)
 
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
+   [-1, 1, Conv, [512, 3, 2]],
-    [ [ -1, 16 ], 1, Concat, [ 1 ] ],  # cat head P5
+   [[-1, 16], 1, Concat, [1]],  # cat head P5
-    [ -1, 3, C3, [ 768, False ] ],  # 29 (P5/32-large)
+   [-1, 3, C3, [768, False]],  # 29 (P5/32-large)
 
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],
+   [-1, 1, Conv, [768, 3, 2]],
-    [ [ -1, 12 ], 1, Concat, [ 1 ] ],  # cat head P6
+   [[-1, 12], 1, Concat, [1]],  # cat head P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 32 (P6/64-xlarge)
+   [-1, 3, C3, [1024, False]],  # 32 (P6/64-xlarge)
 
-    [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)
+   [[23, 26, 29, 32], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5, P6)
   ]

models/hub/yolov5x6.yaml

@@ -3,56 +3,56 @@ nc: 80  # number of classes
 depth_multiple: 1.33  # model depth multiple
 width_multiple: 1.25  # layer channel multiple
 anchors:
-  - [ 19,27,  44,40,  38,94 ]  # P3/8
+  - [19,27,  44,40,  38,94]  # P3/8
-  - [ 96,68,  86,152,  180,137 ]  # P4/16
+  - [96,68,  86,152,  180,137]  # P4/16
-  - [ 140,301,  303,264,  238,542 ]  # P5/32
+  - [140,301,  303,264,  238,542]  # P5/32
-  - [ 436,615,  739,380,  925,792 ]  # P6/64
+  - [436,615,  739,380,  925,792]  # P6/64
 
 # YOLOv5 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
+   [-1, 3, C3, [128]],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 9, C3, [256]],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
+   [-1, 9, C3, [512]],
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 7-P5/32
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
-    [ -1, 3, C3, [ 768 ] ],
+   [-1, 3, C3, [768]],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P6/64
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
+   [-1, 1, SPP, [1024, [3, 5, 7]]],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+   [-1, 3, C3, [1024, False]],  # 11
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
+  [[-1, 1, Conv, [768, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P5
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P5
-    [ -1, 3, C3, [ 768, False ] ],  # 15
+   [-1, 3, C3, [768, False]],  # 15
 
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
+   [-1, 1, Conv, [512, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 19
+   [-1, 3, C3, [512, False]],  # 19
 
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+   [-1, 1, Conv, [256, 1, 1]],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 23 (P3/8-small)
+   [-1, 3, C3, [256, False]],  # 23 (P3/8-small)
 
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
+   [-1, 1, Conv, [256, 3, 2]],
-    [ [ -1, 20 ], 1, Concat, [ 1 ] ],  # cat head P4
+   [[-1, 20], 1, Concat, [1]],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 26 (P4/16-medium)
+   [-1, 3, C3, [512, False]],  # 26 (P4/16-medium)
 
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
+   [-1, 1, Conv, [512, 3, 2]],
-    [ [ -1, 16 ], 1, Concat, [ 1 ] ],  # cat head P5
+   [[-1, 16], 1, Concat, [1]],  # cat head P5
-    [ -1, 3, C3, [ 768, False ] ],  # 29 (P5/32-large)
+   [-1, 3, C3, [768, False]],  # 29 (P5/32-large)
 
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],
+   [-1, 1, Conv, [768, 3, 2]],
-    [ [ -1, 12 ], 1, Concat, [ 1 ] ],  # cat head P6
+   [[-1, 12], 1, Concat, [1]],  # cat head P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 32 (P6/64-xlarge)
+   [-1, 3, C3, [1024, False]],  # 32 (P6/64-xlarge)
 
-    [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)
+   [[23, 26, 29, 32], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5, P6)
   ]

train.py

@@ -83,7 +83,6 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
             if resume:
                 weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp
 
-            
     # Config
     plots = not evolve  # create plots
     cuda = device.type != 'cpu'
@@ -96,7 +95,6 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}'  # check
     is_coco = data.endswith('coco.yaml') and nc == 80  # COCO dataset
 
-
     # Model
     pretrained = weights.endswith('.pt')
     if pretrained:

utils/downloads.py

@@ -115,7 +115,6 @@ def get_token(cookie="./cookie"):
                 return line.split()[-1]
     return ""
 
-
 # Google utils: https://cloud.google.com/storage/docs/reference/libraries ----------------------------------------------
 #
 #

utils/loggers/__init__.py

@@ -1,7 +1,8 @@
 # YOLOv5 experiment logging utils
-import torch
 import warnings
 from threading import Thread
+
+import torch
 from torch.utils.tensorboard import SummaryWriter
 
 from utils.general import colorstr, emojis

utils/loggers/wandb/log_dataset.py

@@ -1,5 +1,4 @@
 import argparse
-import yaml
 
 from wandb_utils import WandbLogger
 

utils/loggers/wandb/sweep.py

@@ -1,7 +1,8 @@
 import sys
-import wandb
 from pathlib import Path
 
+import wandb
+
 FILE = Path(__file__).absolute()
 sys.path.append(FILE.parents[2].as_posix())  # add utils/ to path
 

utils/loggers/wandb/sweep.yaml

@@ -25,9 +25,9 @@ parameters:
   data:
     value: "data/coco128.yaml"
   batch_size:
-    values: [ 64 ]
+    values: [64]
   epochs:
-    values: [ 10 ]
+    values: [10]
 
   lr0:
     distribution: uniform

utils/loggers/wandb/wandb_utils.py

@@ -3,9 +3,10 @@
 import logging
 import os
 import sys
-import yaml
 from contextlib import contextmanager
 from pathlib import Path
+
+import yaml
 from tqdm import tqdm
 
 FILE = Path(__file__).absolute()

val.py

@@ -13,7 +13,6 @@ from threading import Thread
 
 import numpy as np
 import torch
-import yaml
 from tqdm import tqdm
 
 FILE = Path(__file__).absolute()