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:
- - **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()`.
+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:
 
+- **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.
 
+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]
- - GPU [e.g. 2080 Ti]
+If applicable, add screenshots to help explain your problem.
 
+- OS: [e.g. Ubuntu]
+- 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:
-
-* ✅ **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.
-* ✅ **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 ⚠️.
+In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code
+should be:
 
-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. 
+* ✅ **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.
+* ✅ **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">
@@ -173,33 +184,33 @@ 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!  
+We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!
 
 <p align="center">
   <a href="https://github.com/ultralytics/yolov5/discussions/3213">
   <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>
 <details>
   <summary>YOLOv5-P5 640 Figure (click to expand)</summary>
-  
+
 <p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/114313219-f1d70e00-9af5-11eb-9973-52b1f98d321a.png"></p>
 </details>
 <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. 
-  * EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
-  * **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
-</details>
 
+* 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.
+* EfficientDet data from [google/automl](https://github.com/google/automl) at batch size 8.
+* **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
 
@@ -221,24 +232,30 @@ 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 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`  
-  * 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`  
-  * 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>
 
+* AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results
+  denote val2017 accuracy.
+* 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`
+* 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`
+* 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`
 
-## <div align="center">Contribute</div>
+</details>
 
-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">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.
 
 ## <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 
-[https://ultralytics.com/contact](https://ultralytics.com/contact).
+For issues running YOLOv5 please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business or
+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

@@ -15,47 +15,47 @@ test:  # test images (optional)
 
 # Classes
 nc: 365  # number of classes
-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',
-         'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle',
-         'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird',
-         'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
-         'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning',
-         'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife',
-         'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock',
-         'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish',
-         'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan',
-         'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard',
-         'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign',
-         'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat',
-         'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard',
-         'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry',
-         'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
-         'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors',
-         'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape',
-         'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck',
-         'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette',
-         'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket',
-         'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine',
-         'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine',
-         'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon',
-         'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse',
-         'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball',
-         'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin',
-         'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts',
-         'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
-         'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD',
-         'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder',
-         'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips',
-         'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab',
-         'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal',
-         'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart',
-         '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' ]
+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',
+        'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle',
+        'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird',
+        'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
+        'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning',
+        'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife',
+        'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock',
+        'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish',
+        'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan',
+        'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard',
+        'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign',
+        'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat',
+        'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard',
+        'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry',
+        'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
+        'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors',
+        'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape',
+        'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck',
+        'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette',
+        'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket',
+        'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine',
+        'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine',
+        'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon',
+        'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse',
+        'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball',
+        'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin',
+        'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts',
+        'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
+        'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD',
+        'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder',
+        'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips',
+        'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab',
+        'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal',
+        'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart',
+        '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']
 
 
 # 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',
-         'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' ]  # class names
+names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
+        '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

@@ -15,15 +15,15 @@ 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',
-         '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',
-         'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
-         '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
+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',
+        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+        '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
 
 
 # Download script/URL (optional)

data/coco128.yaml

@@ -15,15 +15,15 @@ test:  # test images (optional)
 
 # Classes
 nc: 80  # number of classes
-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',
-         'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
-         '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
+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',
+        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+        '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
 
 
 # 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

@@ -15,15 +15,15 @@ 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',
-         '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',
-         'Fishing Vessel', 'Ferry', 'Yacht', 'Container Ship', 'Oil Tanker', 'Engineering Vehicle', 'Tower crane',
-         '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
+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',
+        'Fishing Vessel', 'Ferry', 'Yacht', 'Container Ship', 'Oil Tanker', 'Engineering Vehicle', 'Tower crane',
+        '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
 
 
 # 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
-  - [ 30,61, 62,45, 59,119 ]  # P4/16
-  - [ 116,90, 156,198, 373,326 ]  # P5/32
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [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
-  - [ 43,32,  39,70,  86,64 ]  # P4/16
-  - [ 65,131,  134,130,  120,265 ]  # P5/32
-  - [ 282,180,  247,354,  512,387 ]  # P6/64
+  - [9,11,  21,19,  17,41]  # P3/8
+  - [43,32,  39,70,  86,64]  # P4/16
+  - [65,131,  134,130,  120,265]  # P5/32
+  - [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
-  - [ 96,68,  86,152,  180,137 ]  # P4/16
-  - [ 140,301,  303,264,  238,542 ]  # P5/32
-  - [ 436,615,  739,380,  925,792 ]  # P6/64
+  - [19,27,  44,40,  38,94]  # P3/8
+  - [96,68,  86,152,  180,137]  # P4/16
+  - [140,301,  303,264,  238,542]  # P5/32
+  - [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
-  - [ 144,103,  129,227,  270,205 ]  # P4/16
-  - [ 209,452,  455,396,  358,812 ]  # P5/32
-  - [ 653,922,  1109,570,  1387,1187 ]  # P6/64
+  - [28,41,  67,59,  57,141]  # P3/8
+  - [144,103,  129,227,  270,205]  # P4/16
+  - [209,452,  455,396,  358,812]  # P5/32
+  - [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
-  - [ 30,46,  61,38,  39,92 ]  # P4/16
-  - [ 78,80,  146,66,  79,163 ]  # P5/32
-  - [ 149,150,  321,143,  157,303 ]  # P6/64
-  - [ 257,402,  359,290,  524,372 ]  # P7/128
+  - [11,11,  13,30,  29,20]  # P3/8
+  - [30,46,  61,38,  39,92]  # P4/16
+  - [78,80,  146,66,  79,163]  # P5/32
+  - [149,150,  321,143,  157,303]  # P6/64
+  - [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
-  - [ 70,83,  138,71,  75,173 ]  # P4/16
-  - [ 165,159,  148,334,  375,151 ]  # P5/32
-  - [ 334,317,  251,626,  499,474 ]  # P6/64
-  - [ 750,326,  534,814,  1079,818 ]  # P7/128
+  - [19,22,  54,36,  32,77]  # P3/8
+  - [70,83,  138,71,  75,173]  # P4/16
+  - [165,159,  148,334,  375,151]  # P5/32
+  - [334,317,  251,626,  499,474]  # P6/64
+  - [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
-  - [ 105,124,  207,107,  113,259 ]  # P4/16
-  - [ 247,238,  222,500,  563,227 ]  # P5/32
-  - [ 501,476,  376,939,  749,711 ]  # P6/64
-  - [ 1126,489,  801,1222,  1618,1227 ]  # P7/128
+  - [29,34,  81,55,  47,115]  # P3/8
+  - [105,124,  207,107,  113,259]  # P4/16
+  - [247,238,  222,500,  563,227]  # P5/32
+  - [501,476,  376,939,  749,711]  # P6/64
+  - [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
-  - [ 30,61, 62,45, 59,119 ]  # P4/16
-  - [ 116,90, 156,198, 373,326 ]  # P5/32
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [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, [ 64, 3, 2 ] ],  # 1-P1/2
-    [ -1, 1, Bottleneck, [ 64 ] ],
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 3-P2/4
-    [ -1, 2, Bottleneck, [ 128 ] ],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 5-P3/8
-    [ -1, 8, Bottleneck, [ 256 ] ],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 7-P4/16
-    [ -1, 8, Bottleneck, [ 512 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P5/32
-    [ -1, 4, Bottleneck, [ 1024 ] ],  # 10
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
+   [-1, 1, Bottleneck, [64]],
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
+   [-1, 2, Bottleneck, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
+   [-1, 8, Bottleneck, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
+   [-1, 8, Bottleneck, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P5/32
+   [-1, 4, Bottleneck, [1024]],  # 10
   ]
 
 # YOLOv3-SPP head
 head:
-  [ [ -1, 1, Bottleneck, [ 1024, False ] ],
-    [ -1, 1, SPP, [ 512, [ 5, 9, 13 ] ] ],
-    [ -1, 1, Conv, [ 1024, 3, 1 ] ],
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 1 ] ],  # 15 (P5/32-large)
+  [[-1, 1, Bottleneck, [1024, False]],
+   [-1, 1, SPP, [512, [5, 9, 13]]],
+   [-1, 1, Conv, [1024, 3, 1]],
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [1024, 3, 1]],  # 15 (P5/32-large)
 
-    [ -2, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P4
-    [ -1, 1, Bottleneck, [ 512, False ] ],
-    [ -1, 1, Bottleneck, [ 512, False ] ],
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, Conv, [ 512, 3, 1 ] ],  # 22 (P4/16-medium)
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [512, 3, 1]],  # 22 (P4/16-medium)
 
-    [ -2, 1, Conv, [ 128, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P3
-    [ -1, 1, Bottleneck, [ 256, False ] ],
-    [ -1, 2, Bottleneck, [ 256, False ] ],  # 27 (P3/8-small)
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P3
+   [-1, 1, Bottleneck, [256, False]],
+   [-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
-  - [ 81,82, 135,169, 344,319 ]  # P5/32
+  - [10,14, 23,27, 37,58]  # P4/16
+  - [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, nn.MaxPool2d, [ 2, 2, 0 ] ],  # 1-P1/2
-    [ -1, 1, Conv, [ 32, 3, 1 ] ],
-    [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ],  # 3-P2/4
-    [ -1, 1, Conv, [ 64, 3, 1 ] ],
-    [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ],  # 5-P3/8
-    [ -1, 1, Conv, [ 128, 3, 1 ] ],
-    [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ],  # 7-P4/16
-    [ -1, 1, Conv, [ 256, 3, 1 ] ],
-    [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ],  # 9-P5/32
-    [ -1, 1, Conv, [ 512, 3, 1 ] ],
-    [ -1, 1, nn.ZeroPad2d, [ [ 0, 1, 0, 1 ] ] ],  # 11
-    [ -1, 1, nn.MaxPool2d, [ 2, 1, 0 ] ],  # 12
+  [[-1, 1, Conv, [16, 3, 1]],  # 0
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 1-P1/2
+   [-1, 1, Conv, [32, 3, 1]],
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 3-P2/4
+   [-1, 1, Conv, [64, 3, 1]],
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 5-P3/8
+   [-1, 1, Conv, [128, 3, 1]],
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 7-P4/16
+   [-1, 1, Conv, [256, 3, 1]],
+   [-1, 1, nn.MaxPool2d, [2, 2, 0]],  # 9-P5/32
+   [-1, 1, Conv, [512, 3, 1]],
+   [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]],  # 11
+   [-1, 1, nn.MaxPool2d, [2, 1, 0]],  # 12
   ]
 
 # YOLOv3-tiny head
 head:
-  [ [ -1, 1, Conv, [ 1024, 3, 1 ] ],
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, Conv, [ 512, 3, 1 ] ],  # 15 (P5/32-large)
+  [[-1, 1, Conv, [1024, 3, 1]],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [512, 3, 1]],  # 15 (P5/32-large)
 
-    [ -2, 1, Conv, [ 128, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P4
-    [ -1, 1, Conv, [ 256, 3, 1 ] ],  # 19 (P4/16-medium)
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
+   [-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
-  - [ 30,61, 62,45, 59,119 ]  # P4/16
-  - [ 116,90, 156,198, 373,326 ]  # P5/32
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [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, [ 64, 3, 2 ] ],  # 1-P1/2
-    [ -1, 1, Bottleneck, [ 64 ] ],
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 3-P2/4
-    [ -1, 2, Bottleneck, [ 128 ] ],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 5-P3/8
-    [ -1, 8, Bottleneck, [ 256 ] ],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 7-P4/16
-    [ -1, 8, Bottleneck, [ 512 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P5/32
-    [ -1, 4, Bottleneck, [ 1024 ] ],  # 10
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
+   [-1, 1, Bottleneck, [64]],
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
+   [-1, 2, Bottleneck, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
+   [-1, 8, Bottleneck, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
+   [-1, 8, Bottleneck, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P5/32
+   [-1, 4, Bottleneck, [1024]],  # 10
   ]
 
 # YOLOv3 head
 head:
-  [ [ -1, 1, Bottleneck, [ 1024, False ] ],
-    [ -1, 1, Conv, [ 512, [ 1, 1 ] ] ],
-    [ -1, 1, Conv, [ 1024, 3, 1 ] ],
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 1 ] ],  # 15 (P5/32-large)
+  [[-1, 1, Bottleneck, [1024, False]],
+   [-1, 1, Conv, [512, [1, 1]]],
+   [-1, 1, Conv, [1024, 3, 1]],
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [1024, 3, 1]],  # 15 (P5/32-large)
 
-    [ -2, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P4
-    [ -1, 1, Bottleneck, [ 512, False ] ],
-    [ -1, 1, Bottleneck, [ 512, False ] ],
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, Conv, [ 512, 3, 1 ] ],  # 22 (P4/16-medium)
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [512, 3, 1]],  # 22 (P4/16-medium)
 
-    [ -2, 1, Conv, [ 128, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P3
-    [ -1, 1, Bottleneck, [ 256, False ] ],
-    [ -1, 2, Bottleneck, [ 256, False ] ],  # 27 (P3/8-small)
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P3
+   [-1, 1, Bottleneck, [256, False]],
+   [-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
-  - [ 30,61, 62,45, 59,119 ]  # P4/16
-  - [ 116,90, 156,198, 373,326 ]  # P5/32
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [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, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
-    [ -1, 3, Bottleneck, [ 128 ] ],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
-    [ -1, 9, BottleneckCSP, [ 256 ] ],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
-    [ -1, 9, BottleneckCSP, [ 512 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 7-P5/32
-    [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
-    [ -1, 6, BottleneckCSP, [ 1024 ] ],  # 9
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, Bottleneck, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 9, BottleneckCSP, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, BottleneckCSP, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
+   [-1, 1, SPP, [1024, [5, 9, 13]]],
+   [-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, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
-    [ -1, 3, BottleneckCSP, [ 512, False ] ],  # 14 (P4/16-medium)
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 3, BottleneckCSP, [512, False]],  # 14 (P4/16-medium)
 
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 3, BottleneckCSP, [ 256, False ] ],  # 18 (P3/8-small)
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 1, Conv, [256, 1, 1]],
+   [-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, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 7-P5/32
-    [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
-    [ -1, 3, C3, [ 1024, False ] ],  # 9
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 9, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
+   [-1, 1, SPP, [1024, [5, 9, 13]]],
+   [-1, 3, C3, [1024, False]],  # 9
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 512, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 13
-
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 17 (P3/8-small)
-
-    [ -1, 1, Conv, [ 128, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 2 ], 1, Concat, [ 1 ] ],  # cat backbone P2
-    [ -1, 1, C3, [ 128, False ] ],  # 21 (P2/4-xsmall)
-
-    [ -1, 1, Conv, [ 128, 3, 2 ] ],
-    [ [ -1, 18 ], 1, Concat, [ 1 ] ],  # cat head P3
-    [ -1, 3, C3, [ 256, False ] ],  # 24 (P3/8-small)
-
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
-    [ [ -1, 14 ], 1, Concat, [ 1 ] ],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 27 (P4/16-medium)
-
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
-    [ [ -1, 10 ], 1, Concat, [ 1 ] ],  # cat head P5
-    [ -1, 3, C3, [ 1024, False ] ],  # 30 (P5/32-large)
-
-    [ [ 24, 27, 30 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5)
+  [[-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 13
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
+
+   [-1, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 2], 1, Concat, [1]],  # cat backbone P2
+   [-1, 1, C3, [128, False]],  # 21 (P2/4-xsmall)
+
+   [-1, 1, Conv, [128, 3, 2]],
+   [[-1, 18], 1, Concat, [1]],  # cat head P3
+   [-1, 3, C3, [256, False]],  # 24 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 14], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 27 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [1024, False]],  # 30 (P5/32-large)
+
+   [[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, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 7-P5/32
-    [ -1, 3, C3, [ 768 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 9, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
+   [-1, 3, C3, [768]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
+   [-1, 1, SPP, [1024, [3, 5, 7]]],
+   [-1, 3, C3, [1024, False]],  # 11
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P5
-    [ -1, 3, C3, [ 768, False ] ],  # 15
-
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 19
-
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 23 (P3/8-small)
-
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
-    [ [ -1, 20 ], 1, Concat, [ 1 ] ],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 26 (P4/16-medium)
-
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
-    [ [ -1, 16 ], 1, Concat, [ 1 ] ],  # cat head P5
-    [ -1, 3, C3, [ 768, False ] ],  # 29 (P5/32-large)
-
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],
-    [ [ -1, 12 ], 1, Concat, [ 1 ] ],  # cat head P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 32 (P5/64-xlarge)
-
-    [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)
+  [[-1, 1, Conv, [768, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P5
+   [-1, 3, C3, [768, False]],  # 15
+
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 19
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 23 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 20], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 26 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 16], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [768, False]],  # 29 (P5/32-large)
+
+   [-1, 1, Conv, [768, 3, 2]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P6
+   [-1, 3, C3, [1024, False]],  # 32 (P5/64-xlarge)
+
+   [[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, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 7-P5/32
-    [ -1, 3, C3, [ 768 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P6/64
-    [ -1, 3, C3, [ 1024 ] ],
-    [ -1, 1, Conv, [ 1280, 3, 2 ] ],  # 11-P7/128
-    [ -1, 1, SPP, [ 1280, [ 3, 5 ] ] ],
-    [ -1, 3, C3, [ 1280, False ] ],  # 13
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 9, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
+   [-1, 3, C3, [768]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
+   [-1, 3, C3, [1024]],
+   [-1, 1, Conv, [1280, 3, 2]],  # 11-P7/128
+   [-1, 1, SPP, [1280, [3, 5]]],
+   [-1, 3, C3, [1280, False]],  # 13
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 1024, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 10 ], 1, Concat, [ 1 ] ],  # cat backbone P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 17
+  [[-1, 1, Conv, [1024, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 10], 1, Concat, [1]],  # cat backbone P6
+   [-1, 3, C3, [1024, False]],  # 17
 
-    [ -1, 1, Conv, [ 768, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P5
-    [ -1, 3, C3, [ 768, False ] ],  # 21
+   [-1, 1, Conv, [768, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P5
+   [-1, 3, C3, [768, False]],  # 21
 
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 25
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 25
 
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 29 (P3/8-small)
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 29 (P3/8-small)
 
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
-    [ [ -1, 26 ], 1, Concat, [ 1 ] ],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 32 (P4/16-medium)
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 26], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 32 (P4/16-medium)
 
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
-    [ [ -1, 22 ], 1, Concat, [ 1 ] ],  # cat head P5
-    [ -1, 3, C3, [ 768, False ] ],  # 35 (P5/32-large)
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 22], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [768, False]],  # 35 (P5/32-large)
 
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],
-    [ [ -1, 18 ], 1, Concat, [ 1 ] ],  # cat head P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 38 (P6/64-xlarge)
+   [-1, 1, Conv, [768, 3, 2]],
+   [[-1, 18], 1, Concat, [1]],  # cat head P6
+   [-1, 3, C3, [1024, False]],  # 38 (P6/64-xlarge)
 
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],
-    [ [ -1, 14 ], 1, Concat, [ 1 ] ],  # cat head P7
-    [ -1, 3, C3, [ 1280, False ] ],  # 41 (P7/128-xxlarge)
+   [-1, 1, Conv, [1024, 3, 2]],
+   [[-1, 14], 1, Concat, [1]],  # cat head P7
+   [-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
-  - [ 30,61, 62,45, 59,119 ]  # P4/16
-  - [ 116,90, 156,198, 373,326 ]  # P5/32
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [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, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
-    [ -1, 3, BottleneckCSP, [ 128 ] ],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
-    [ -1, 9, BottleneckCSP, [ 256 ] ],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
-    [ -1, 9, BottleneckCSP, [ 512 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 7-P5/32
-    [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
-    [ -1, 3, BottleneckCSP, [ 1024, False ] ],  # 9
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, BottleneckCSP, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 9, BottleneckCSP, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, BottleneckCSP, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
+   [-1, 1, SPP, [1024, [5, 9, 13]]],
+   [-1, 3, BottleneckCSP, [1024, False]],  # 9
   ]
 
 # YOLOv5 PANet head
 head:
-  [ [ -1, 1, Conv, [ 512, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
-    [ -1, 3, BottleneckCSP, [ 512, False ] ],  # 13
+  [[-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, BottleneckCSP, [512, False]],  # 13
 
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
-    [ -1, 3, BottleneckCSP, [ 256, False ] ],  # 17 (P3/8-small)
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, BottleneckCSP, [256, False]],  # 17 (P3/8-small)
 
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
-    [ [ -1, 14 ], 1, Concat, [ 1 ] ],  # cat head P4
-    [ -1, 3, BottleneckCSP, [ 512, False ] ],  # 20 (P4/16-medium)
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 14], 1, Concat, [1]],  # cat head P4
+   [-1, 3, BottleneckCSP, [512, False]],  # 20 (P4/16-medium)
 
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
-    [ [ -1, 10 ], 1, Concat, [ 1 ] ],  # cat head P5
-    [ -1, 3, BottleneckCSP, [ 1024, False ] ],  # 23 (P5/32-large)
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
+   [-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
-  - [ 96,68,  86,152,  180,137 ]  # P4/16
-  - [ 140,301,  303,264,  238,542 ]  # P5/32
-  - [ 436,615,  739,380,  925,792 ]  # P6/64
+  - [19,27,  44,40,  38,94]  # P3/8
+  - [96,68,  86,152,  180,137]  # P4/16
+  - [140,301,  303,264,  238,542]  # P5/32
+  - [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, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 7-P5/32
-    [ -1, 3, C3, [ 768 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 9, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
+   [-1, 3, C3, [768]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
+   [-1, 1, SPP, [1024, [3, 5, 7]]],
+   [-1, 3, C3, [1024, False]],  # 11
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P5
-    [ -1, 3, C3, [ 768, False ] ],  # 15
-
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 19
-
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 23 (P3/8-small)
-
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
-    [ [ -1, 20 ], 1, Concat, [ 1 ] ],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 26 (P4/16-medium)
-
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
-    [ [ -1, 16 ], 1, Concat, [ 1 ] ],  # cat head P5
-    [ -1, 3, C3, [ 768, False ] ],  # 29 (P5/32-large)
-
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],
-    [ [ -1, 12 ], 1, Concat, [ 1 ] ],  # cat head P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 32 (P6/64-xlarge)
-
-    [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)
+  [[-1, 1, Conv, [768, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P5
+   [-1, 3, C3, [768, False]],  # 15
+
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 19
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 23 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 20], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 26 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 16], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [768, False]],  # 29 (P5/32-large)
+
+   [-1, 1, Conv, [768, 3, 2]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P6
+   [-1, 3, C3, [1024, False]],  # 32 (P6/64-xlarge)
+
+   [[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
-  - [ 96,68,  86,152,  180,137 ]  # P4/16
-  - [ 140,301,  303,264,  238,542 ]  # P5/32
-  - [ 436,615,  739,380,  925,792 ]  # P6/64
+  - [19,27,  44,40,  38,94]  # P3/8
+  - [96,68,  86,152,  180,137]  # P4/16
+  - [140,301,  303,264,  238,542]  # P5/32
+  - [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, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 7-P5/32
-    [ -1, 3, C3, [ 768 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 9, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
+   [-1, 3, C3, [768]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
+   [-1, 1, SPP, [1024, [3, 5, 7]]],
+   [-1, 3, C3, [1024, False]],  # 11
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P5
-    [ -1, 3, C3, [ 768, False ] ],  # 15
-
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 19
-
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 23 (P3/8-small)
-
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
-    [ [ -1, 20 ], 1, Concat, [ 1 ] ],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 26 (P4/16-medium)
-
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
-    [ [ -1, 16 ], 1, Concat, [ 1 ] ],  # cat head P5
-    [ -1, 3, C3, [ 768, False ] ],  # 29 (P5/32-large)
-
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],
-    [ [ -1, 12 ], 1, Concat, [ 1 ] ],  # cat head P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 32 (P6/64-xlarge)
-
-    [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)
+  [[-1, 1, Conv, [768, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P5
+   [-1, 3, C3, [768, False]],  # 15
+
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 19
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 23 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 20], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 26 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 16], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [768, False]],  # 29 (P5/32-large)
+
+   [-1, 1, Conv, [768, 3, 2]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P6
+   [-1, 3, C3, [1024, False]],  # 32 (P6/64-xlarge)
+
+   [[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
-  - [ 30,61, 62,45, 59,119 ]  # P4/16
-  - [ 116,90, 156,198, 373,326 ]  # P5/32
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [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, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 7-P5/32
-    [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
-    [ -1, 3, C3TR, [ 1024, False ] ],  # 9  <-------- C3TR() Transformer module
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 9, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
+   [-1, 1, SPP, [1024, [5, 9, 13]]],
+   [-1, 3, C3TR, [1024, False]],  # 9  <-------- C3TR() Transformer module
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 512, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 13
+  [[-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 13
 
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 17 (P3/8-small)
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
 
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
-    [ [ -1, 14 ], 1, Concat, [ 1 ] ],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 20 (P4/16-medium)
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 14], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
 
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
-    [ [ -1, 10 ], 1, Concat, [ 1 ] ],  # cat head P5
-    [ -1, 3, C3, [ 1024, False ] ],  # 23 (P5/32-large)
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
+   [-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
-  - [ 96,68,  86,152,  180,137 ]  # P4/16
-  - [ 140,301,  303,264,  238,542 ]  # P5/32
-  - [ 436,615,  739,380,  925,792 ]  # P6/64
+  - [19,27,  44,40,  38,94]  # P3/8
+  - [96,68,  86,152,  180,137]  # P4/16
+  - [140,301,  303,264,  238,542]  # P5/32
+  - [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, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 7-P5/32
-    [ -1, 3, C3, [ 768 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 9, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
+   [-1, 3, C3, [768]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
+   [-1, 1, SPP, [1024, [3, 5, 7]]],
+   [-1, 3, C3, [1024, False]],  # 11
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P5
-    [ -1, 3, C3, [ 768, False ] ],  # 15
-
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 19
-
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 23 (P3/8-small)
-
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
-    [ [ -1, 20 ], 1, Concat, [ 1 ] ],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 26 (P4/16-medium)
-
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
-    [ [ -1, 16 ], 1, Concat, [ 1 ] ],  # cat head P5
-    [ -1, 3, C3, [ 768, False ] ],  # 29 (P5/32-large)
-
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],
-    [ [ -1, 12 ], 1, Concat, [ 1 ] ],  # cat head P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 32 (P6/64-xlarge)
-
-    [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)
+  [[-1, 1, Conv, [768, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P5
+   [-1, 3, C3, [768, False]],  # 15
+
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 19
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 23 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 20], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 26 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 16], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [768, False]],  # 29 (P5/32-large)
+
+   [-1, 1, Conv, [768, 3, 2]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P6
+   [-1, 3, C3, [1024, False]],  # 32 (P6/64-xlarge)
+
+   [[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
-  - [ 96,68,  86,152,  180,137 ]  # P4/16
-  - [ 140,301,  303,264,  238,542 ]  # P5/32
-  - [ 436,615,  739,380,  925,792 ]  # P6/64
+  - [19,27,  44,40,  38,94]  # P3/8
+  - [96,68,  86,152,  180,137]  # P4/16
+  - [140,301,  303,264,  238,542]  # P5/32
+  - [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, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
-    [ -1, 3, C3, [ 128 ] ],
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
-    [ -1, 9, C3, [ 512 ] ],
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],  # 7-P5/32
-    [ -1, 3, C3, [ 768 ] ],
-    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 9, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
+   [-1, 3, C3, [768]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
+   [-1, 1, SPP, [1024, [3, 5, 7]]],
+   [-1, 3, C3, [1024, False]],  # 11
   ]
 
 # YOLOv5 head
 head:
-  [ [ -1, 1, Conv, [ 768, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 8 ], 1, Concat, [ 1 ] ],  # cat backbone P5
-    [ -1, 3, C3, [ 768, False ] ],  # 15
-
-    [ -1, 1, Conv, [ 512, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
-    [ -1, 3, C3, [ 512, False ] ],  # 19
-
-    [ -1, 1, Conv, [ 256, 1, 1 ] ],
-    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
-    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
-    [ -1, 3, C3, [ 256, False ] ],  # 23 (P3/8-small)
-
-    [ -1, 1, Conv, [ 256, 3, 2 ] ],
-    [ [ -1, 20 ], 1, Concat, [ 1 ] ],  # cat head P4
-    [ -1, 3, C3, [ 512, False ] ],  # 26 (P4/16-medium)
-
-    [ -1, 1, Conv, [ 512, 3, 2 ] ],
-    [ [ -1, 16 ], 1, Concat, [ 1 ] ],  # cat head P5
-    [ -1, 3, C3, [ 768, False ] ],  # 29 (P5/32-large)
-
-    [ -1, 1, Conv, [ 768, 3, 2 ] ],
-    [ [ -1, 12 ], 1, Concat, [ 1 ] ],  # cat head P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 32 (P6/64-xlarge)
-
-    [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5, P6)
+  [[-1, 1, Conv, [768, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P5
+   [-1, 3, C3, [768, False]],  # 15
+
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 19
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 23 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 20], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 26 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 16], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [768, False]],  # 29 (P5/32-large)
+
+   [-1, 1, Conv, [768, 3, 2]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P6
+   [-1, 3, C3, [1024, False]],  # 32 (P6/64-xlarge)
+
+   [[23, 26, 29, 32], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5, P6)
   ]

train.py

@@ -74,7 +74,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
     with open(save_dir / 'opt.yaml', 'w') as f:
         yaml.safe_dump(vars(opt), f, sort_keys=False)
     data_dict = None
-    
+
     # Loggers
     if RANK in [-1, 0]:
         loggers = Loggers(save_dir, weights, opt, hyp, LOGGER).start()  # loggers dict
@@ -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()