YAML reformat (#12648)

* YAML reformat

* Auto-format by Ultralytics actions

---------

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

.github/workflows/ci-testing.yml

@@ -9,7 +9,7 @@ on:
   pull_request:
     branches: [master]
   schedule:
-    - cron: '0 0 * * *'  # runs at 00:00 UTC every day
+    - cron: "0 0 * * *" # runs at 00:00 UTC every day
 
 jobs:
   Benchmarks:
@@ -18,14 +18,14 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest]
-        python-version: [ '3.11' ]  # requires python<=3.10
+        python-version: ["3.11"] # requires python<=3.10
         model: [yolov5n]
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip' # caching pip dependencies
+          cache: "pip" # caching pip dependencies
       - name: Install requirements
         run: |
           python -m pip install --upgrade pip wheel
@@ -52,25 +52,25 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-latest] # macos-latest bug https://github.com/ultralytics/yolov5/pull/9049
-        python-version: [ '3.11' ]
+        python-version: ["3.11"]
         model: [yolov5n]
         include:
           - os: ubuntu-latest
-            python-version: '3.8'  # '3.6.8' min
+            python-version: "3.8" # '3.6.8' min
             model: yolov5n
           - os: ubuntu-latest
-            python-version: '3.9'
+            python-version: "3.9"
             model: yolov5n
           - os: ubuntu-latest
-            python-version: '3.8'  # torch 1.8.0 requires python >=3.6, <=3.8
+            python-version: "3.8" # torch 1.8.0 requires python >=3.6, <=3.8
             model: yolov5n
-            torch: '1.8.0'  # min torch version CI https://pypi.org/project/torchvision/
+            torch: "1.8.0" # min torch version CI https://pypi.org/project/torchvision/
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip' # caching pip dependencies
+          cache: "pip" # caching pip dependencies
       - name: Install requirements
         run: |
           python -m pip install --upgrade pip wheel

.github/workflows/codeql-analysis.yml

@@ -5,7 +5,7 @@ name: "CodeQL"
 
 on:
   schedule:
-    - cron: '0 0 1 * *'  # Runs at 00:00 UTC on the 1st of every month
+    - cron: "0 0 1 * *" # Runs at 00:00 UTC on the 1st of every month
   workflow_dispatch:
 
 jobs:
@@ -16,7 +16,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        language: ['python']
+        language: ["python"]
         # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ]
         # Learn more:
         # https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed

.github/workflows/links.yml

@@ -12,7 +12,7 @@ name: Check Broken links
 on:
   workflow_dispatch:
   schedule:
-    - cron: '0 0 * * *'  # runs at 00:00 UTC every day
+    - cron: "0 0 * * *" # runs at 00:00 UTC every day
 
 jobs:
   Links:

.github/workflows/stale.yml

@@ -3,7 +3,7 @@
 name: Close stale issues
 on:
   schedule:
-    - cron: '0 0 * * *'  # Runs at 00:00 UTC every day
+    - cron: "0 0 * * *" # Runs at 00:00 UTC every day
 
 jobs:
   stale:
@@ -43,5 +43,5 @@ jobs:
           days-before-issue-close: 10
           days-before-pr-stale: 90
           days-before-pr-close: 30
-          exempt-issue-labels: 'documentation,tutorial,TODO'
+          exempt-issue-labels: "documentation,tutorial,TODO"
           operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting.

README.zh-CN.md

@@ -184,7 +184,7 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  -
 </div>
 
 |                                                  Roboflow                                                  |                                         ClearML ⭐ 新                                          |                                                   Comet ⭐ 新                                                    |                                           Neural Magic ⭐ 新                                           |
-| :--------------------------------------------------------------------------------: | :-------------------------------------------------------------------------: | :--------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: |
+| :--------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
 | 将您的自定义数据集进行标注并直接导出到 YOLOv5 以进行训练 [Roboflow](https://roboflow.com/?ref=ultralytics) | 自动跟踪、可视化甚至远程训练 YOLOv5 [ClearML](https://cutt.ly/yolov5-readme-clearml)（开源！） | 永远免费，[Comet](https://bit.ly/yolov5-readme-comet2)可让您保存 YOLOv5 模型、恢复训练以及交互式可视化和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic)，运行 YOLOv5 推理的速度最高可提高6倍 |
 
 ## <div align="center">Ultralytics HUB</div>
@@ -217,7 +217,7 @@ YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结
 ### 预训练模型
 
 | 模型                                                                                           | 尺寸<br><sup>（像素） | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | 推理速度<br><sup>CPU b1<br>（ms） | 推理速度<br><sup>V100 b1<br>（ms） | 速度<br><sup>V100 b32<br>（ms） | 参数量<br><sup>(M) | FLOPs<br><sup>@640 (B) |
-| ---------------------------------------------------------------------------------------------- | --------------- | -------------------- | ----------------- | --------------------------- | ---------------------------- | --------------------------- | --------------- | ---------------------- |
+| ---------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | --------------------------------- | ---------------------------------- | ------------------------------- | ------------------ | ---------------------- |
 | [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt)             | 640                   | 28.0                 | 45.7              | **45**                            | **6.3**                            | **0.6**                         | **1.9**            | **4.5**                |
 | [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt)             | 640                   | 37.4                 | 56.8              | 98                                | 6.4                                | 0.9                             | 7.2                | 16.5                   |
 | [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt)             | 640                   | 45.4                 | 64.1              | 224                               | 8.2                                | 1.7                             | 21.2               | 49.0                   |
@@ -257,7 +257,7 @@ YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结
 我们使用 A100 GPU 在 COCO 上以 640 图像大小训练了 300 epochs 得到 YOLOv5 分割模型。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于再现，我们在 Google [Colab Pro](https://colab.research.google.com/signup) 上进行了所有速度测试。
 
 | 模型                                                                                       | 尺寸<br><sup>（像素） | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 训练时长<br><sup>300 epochs<br>A100 GPU（小时） | 推理速度<br><sup>ONNX CPU<br>（ms） | 推理速度<br><sup>TRT A100<br>（ms） | 参数量<br><sup>(M) | FLOPs<br><sup>@640 (B) |
-| ------------------------------------------------------------------------------------------ | --------------- | -------------------- | --------------------- | --------------------------------------- | ----------------------------- | ----------------------------- | --------------- | ---------------------- |
+| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | ----------------------------------------------- | ----------------------------------- | ----------------------------------- | ------------------ | ---------------------- |
 | [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640                   | 27.6                 | 23.4                  | 80:17                                           | **62.7**                            | **1.2**                             | **2.0**            | **7.1**                |
 | [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640                   | 37.6                 | 31.7                  | 88:16                                           | 173.3                               | 1.4                                 | 7.6                | 26.4                   |
 | [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640                   | 45.0                 | 37.1                  | 108:36                                          | 427.0                               | 2.2                                 | 22.0               | 70.8                   |
@@ -334,7 +334,7 @@ YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) 带来对
 我们使用 4xA100 实例在 ImageNet 上训练了 90 个 epochs 得到 YOLOv5-cls 分类模型，我们训练了 ResNet 和 EfficientNet 模型以及相同的默认训练设置以进行比较。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于重现，我们在 Google 上进行了所有速度测试 [Colab Pro](https://colab.research.google.com/signup) 。
 
 | 模型                                                                                               | 尺寸<br><sup>（像素） | acc<br><sup>top1 | acc<br><sup>top5 | 训练时长<br><sup>90 epochs<br>4xA100（小时） | 推理速度<br><sup>ONNX CPU<br>（ms） | 推理速度<br><sup>TensorRT V100<br>（ms） | 参数<br><sup>(M) | FLOPs<br><sup>@640 (B) |
-| -------------------------------------------------------------------------------------------------- | --------------- | ---------------- | ---------------- | ------------------------------------ | ----------------------------- | ---------------------------------- | -------------- | ---------------------- |
+| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ----------------------------------- | ---------------------------------------- | ---------------- | ---------------------- |
 | [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt)         | 224                   | 64.6             | 85.4             | 7:59                                         | **3.3**                             | **0.5**                                  | **2.5**          | **0.5**                |
 | [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt)         | 224                   | 71.5             | 90.2             | 8:09                                         | 6.6                                 | 0.6                                      | 5.4              | 1.4                    |
 | [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt)         | 224                   | 75.9             | 92.9             | 10:06                                        | 15.5                                | 0.9                                      | 12.9             | 3.9                    |

data/Argoverse.yaml

@@ -6,7 +6,6 @@
 # └── datasets
 #     └── Argoverse  ← downloads here (31.3 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/Argoverse # dataset root dir
 train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
@@ -24,7 +23,6 @@ names:
   6: traffic_light
   7: stop_sign
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   import json

data/GlobalWheat2020.yaml

@@ -6,7 +6,6 @@
 # └── datasets
 #     └── GlobalWheat2020  ← downloads here (7.0 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/GlobalWheat2020 # dataset root dir
 train: # train images (relative to 'path') 3422 images
@@ -29,7 +28,6 @@ test: # test images (optional) 1276 images
 names:
   0: wheat_head
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   from utils.general import download, Path

data/ImageNet.yaml

@@ -7,7 +7,6 @@
 # └── datasets
 #     └── imagenet  ← downloads here (144 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/imagenet # dataset root dir
 train: train # train images (relative to 'path') 1281167 images
@@ -1017,6 +1016,5 @@ names:
   998: ear
   999: toilet paper
 
-
 # Download script/URL (optional)
 download: data/scripts/get_imagenet.sh

data/ImageNet10.yaml

@@ -7,7 +7,6 @@
 # └── datasets
 #     └── imagenet10  ← downloads here
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/imagenet10 # dataset root dir
 train: train # train images (relative to 'path') 1281167 images
@@ -27,6 +26,5 @@ names:
   8: hen
   9: ostrich
 
-
 # Download script/URL (optional)
 download: data/scripts/get_imagenet10.sh

data/ImageNet100.yaml

@@ -7,7 +7,6 @@
 # └── datasets
 #     └── imagenet100  ← downloads here
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/imagenet100 # dataset root dir
 train: train # train images (relative to 'path') 1281167 images

data/ImageNet1000.yaml

@@ -7,7 +7,6 @@
 # └── datasets
 #     └── imagenet100  ← downloads here
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/imagenet1000 # dataset root dir
 train: train # train images (relative to 'path') 1281167 images
@@ -1017,6 +1016,5 @@ names:
   998: ear
   999: toilet paper
 
-
 # Download script/URL (optional)
 download: data/scripts/get_imagenet1000.sh

data/Objects365.yaml

@@ -6,7 +6,6 @@
 # └── datasets
 #     └── Objects365  ← downloads here (712 GB = 367G data + 345G zips)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/Objects365 # dataset root dir
 train: images/train # train images (relative to 'path') 1742289 images
@@ -381,7 +380,6 @@ names:
   363: Curling
   364: Table Tennis
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   from tqdm import tqdm

data/SKU-110K.yaml

@@ -6,7 +6,6 @@
 # └── datasets
 #     └── SKU-110K  ← downloads here (13.6 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/SKU-110K # dataset root dir
 train: train.txt # train images (relative to 'path')  8219 images
@@ -17,7 +16,6 @@ test: test.txt  # test images (optional)  2936 images
 names:
   0: object
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   import shutil

data/VOC.yaml

@@ -6,7 +6,6 @@
 # └── datasets
 #     └── VOC  ← downloads here (2.8 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/VOC
 train: # train images (relative to 'path')  16551 images
@@ -42,7 +41,6 @@ names:
   18: train
   19: tvmonitor
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   import xml.etree.ElementTree as ET

data/VisDrone.yaml

@@ -6,7 +6,6 @@
 # └── datasets
 #     └── VisDrone  ← downloads here (2.3 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/VisDrone # dataset root dir
 train: VisDrone2019-DET-train/images # train images (relative to 'path')  6471 images
@@ -26,7 +25,6 @@ names:
   8: bus
   9: motor
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   from utils.general import download, os, Path

data/coco.yaml

@@ -6,7 +6,6 @@
 # └── datasets
 #     └── coco  ← downloads here (20.1 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/coco # dataset root dir
 train: train2017.txt # train images (relative to 'path') 118287 images
@@ -96,7 +95,6 @@ names:
   78: hair drier
   79: toothbrush
 
-
 # Download script/URL (optional)
 download: |
   from utils.general import download, Path

data/coco128-seg.yaml

@@ -6,7 +6,6 @@
 # └── datasets
 #     └── coco128-seg  ← downloads here (7 MB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/coco128-seg # dataset root dir
 train: images/train2017 # train images (relative to 'path') 128 images
@@ -96,6 +95,5 @@ names:
   78: hair drier
   79: toothbrush
 
-
 # Download script/URL (optional)
 download: https://ultralytics.com/assets/coco128-seg.zip

data/coco128.yaml

@@ -6,7 +6,6 @@
 # └── datasets
 #     └── coco128  ← downloads here (7 MB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/coco128 # dataset root dir
 train: images/train2017 # train images (relative to 'path') 128 images
@@ -96,6 +95,5 @@ names:
   78: hair drier
   79: toothbrush
 
-
 # Download script/URL (optional)
 download: https://ultralytics.com/assets/coco128.zip

data/xView.yaml

@@ -7,7 +7,6 @@
 # └── datasets
 #     └── xView  ← downloads here (20.7 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/xView # dataset root dir
 train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
@@ -76,7 +75,6 @@ names:
   58: Pylon
   59: Tower
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   import json

models/hub/anchors.yaml

@@ -1,7 +1,6 @@
 # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 # Default anchors for COCO data
 
-
 # P5 -------------------------------------------------------------------------------------------------------------------
 # P5-640:
 anchors_p5_640:
@@ -9,7 +8,6 @@ anchors_p5_640:
   - [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:
@@ -32,7 +30,6 @@ anchors_p6_1920:
   - [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:

models/hub/yolov3-spp.yaml

@@ -12,7 +12,8 @@ anchors:
 # 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, Bottleneck, [64]],
     [-1, 1, Conv, [128, 3, 2]], # 3-P2/4
@@ -26,15 +27,15 @@ backbone:
   ]
 
 # YOLOv3-SPP head
-head:
+head: [
-  [[-1, 1, Bottleneck, [1024, False]],
+    [-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, 1, nn.Upsample, [None, 2, "nearest"]],
     [[-1, 8], 1, Concat, [1]], # cat backbone P4
     [-1, 1, Bottleneck, [512, False]],
     [-1, 1, Bottleneck, [512, False]],
@@ -42,7 +43,7 @@ head:
     [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
 
     [-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, 1, Bottleneck, [256, False]],
     [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)

models/hub/yolov3-tiny.yaml

@@ -11,7 +11,8 @@ anchors:
 # 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, Conv, [32, 3, 1]],
     [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4
@@ -27,13 +28,13 @@ backbone:
   ]
 
 # YOLOv3-tiny head
-head:
+head: [
-  [[-1, 1, Conv, [1024, 3, 1]],
+    [-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, 1, nn.Upsample, [None, 2, "nearest"]],
     [[-1, 8], 1, Concat, [1]], # cat backbone P4
     [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium)
 

models/hub/yolov3.yaml

@@ -12,7 +12,8 @@ anchors:
 # 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, Bottleneck, [64]],
     [-1, 1, Conv, [128, 3, 2]], # 3-P2/4
@@ -26,15 +27,15 @@ backbone:
   ]
 
 # YOLOv3 head
-head:
+head: [
-  [[-1, 1, Bottleneck, [1024, False]],
+    [-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, 1, nn.Upsample, [None, 2, "nearest"]],
     [[-1, 8], 1, Concat, [1]], # cat backbone P4
     [-1, 1, Bottleneck, [512, False]],
     [-1, 1, Bottleneck, [512, False]],
@@ -42,7 +43,7 @@ head:
     [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
 
     [-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, 1, Bottleneck, [256, False]],
     [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)

models/hub/yolov5-bifpn.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 BiFPN head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/hub/yolov5-fpn.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,15 +26,15 @@ backbone:
   ]
 
 # YOLOv5 v6.0 FPN head
-head:
+head: [
-  [[-1, 3, C3, [1024, False]],  # 10 (P5/32-large)
+    [-1, 3, C3, [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, 1, Conv, [512, 1, 1]],
     [-1, 3, C3, [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, 1, Conv, [256, 1, 1]],
     [-1, 3, C3, [256, False]], # 18 (P3/8-small)

models/hub/yolov5-p2.yaml

@@ -9,7 +9,8 @@ anchors: 3  # AutoAnchor evolves 3 anchors per P output layer
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -22,19 +23,19 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 
     [-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, 1, C3, [128, False]], # 21 (P2/4-xsmall)
 

models/hub/yolov5-p34.yaml

@@ -9,7 +9,8 @@ anchors: 3  # AutoAnchor evolves 3 anchors per P output layer
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Conv, [ 64, 6, 2, 2 ] ],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -22,14 +23,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head with (P3, P4) outputs
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/hub/yolov5-p6.yaml

@@ -9,7 +9,8 @@ anchors: 3  # AutoAnchor evolves 3 anchors per P output layer
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -24,19 +25,19 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs
-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, 3, C3, [768, False]], # 15
 
     [-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, 3, C3, [512, False]], # 19
 
     [-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, 3, C3, [256, False]], # 23 (P3/8-small)
 

models/hub/yolov5-p7.yaml

@@ -9,7 +9,8 @@ anchors: 3  # AutoAnchor evolves 3 anchors per P output layer
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -26,24 +27,24 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs
-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, 3, C3, [1024, False]], # 17
 
     [-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, 3, C3, [768, False]], # 21
 
     [-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, 3, C3, [512, False]], # 25
 
     [-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, 3, C3, [256, False]], # 29 (P3/8-small)
 

models/hub/yolov5-panet.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 PANet head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/hub/yolov5l6.yaml

@@ -13,7 +13,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -28,19 +29,19 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [768, False]], # 15
 
     [-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, 3, C3, [512, False]], # 19
 
     [-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, 3, C3, [256, False]], # 23 (P3/8-small)
 

models/hub/yolov5m6.yaml

@@ -13,7 +13,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -28,19 +29,19 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [768, False]], # 15
 
     [-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, 3, C3, [512, False]], # 19
 
     [-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, 3, C3, [256, False]], # 23 (P3/8-small)
 

models/hub/yolov5n6.yaml

@@ -13,7 +13,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -28,19 +29,19 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [768, False]], # 15
 
     [-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, 3, C3, [512, False]], # 19
 
     [-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, 3, C3, [256, False]], # 23 (P3/8-small)
 

models/hub/yolov5s-LeakyReLU.yaml

@@ -13,7 +13,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -26,14 +27,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/hub/yolov5s-ghost.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
     [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4
     [-1, 3, C3Ghost, [128]],
     [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-head:
+head: [
-  [[-1, 1, GhostConv, [512, 1, 1]],
+    [-1, 1, GhostConv, [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, 3, C3Ghost, [512, False]], # 13
 
     [-1, 1, GhostConv, [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, 3, C3Ghost, [256, False]], # 17 (P3/8-small)
 

models/hub/yolov5s-transformer.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/hub/yolov5s6.yaml

@@ -13,7 +13,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -28,19 +29,19 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [768, False]], # 15
 
     [-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, 3, C3, [512, False]], # 19
 
     [-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, 3, C3, [256, False]], # 23 (P3/8-small)
 

models/hub/yolov5x6.yaml

@@ -13,7 +13,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -28,19 +29,19 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [768, False]], # 15
 
     [-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, 3, C3, [512, False]], # 19
 
     [-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, 3, C3, [256, False]], # 23 (P3/8-small)
 

models/segment/yolov5l-seg.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/segment/yolov5m-seg.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/segment/yolov5n-seg.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/segment/yolov5s-seg.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/segment/yolov5x-seg.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/yolov5l.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/yolov5m.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/yolov5n.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/yolov5s.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

models/yolov5x.yaml

@@ -12,7 +12,8 @@ anchors:
 # YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+  [
+    [-1, 1, Conv, [64, 6, 2, 2]], # 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
@@ -25,14 +26,14 @@ backbone:
   ]
 
 # YOLOv5 v6.0 head
-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, 3, C3, [512, False]], # 13
 
     [-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, 3, C3, [256, False]], # 17 (P3/8-small)
 

utils/loggers/comet/optimizer_config.json

@@ -3,201 +3,127 @@
   "parameters": {
     "anchor_t": {
       "type": "discrete",
-      "values": [
+      "values": [2, 8]
-        2,
-        8
-      ]
     },
     "batch_size": {
       "type": "discrete",
-      "values": [
+      "values": [16, 32, 64]
-        16,
-        32,
-        64
-      ]
     },
     "box": {
       "type": "discrete",
-      "values": [
+      "values": [0.02, 0.2]
-        0.02,
-        0.2
-      ]
     },
     "cls": {
       "type": "discrete",
-      "values": [
+      "values": [0.2]
-        0.2
-      ]
     },
     "cls_pw": {
       "type": "discrete",
-      "values": [
+      "values": [0.5]
-        0.5
-      ]
     },
     "copy_paste": {
       "type": "discrete",
-      "values": [
+      "values": [1]
-        1
-      ]
     },
     "degrees": {
       "type": "discrete",
-      "values": [
+      "values": [0, 45]
-        0,
-        45
-      ]
     },
     "epochs": {
       "type": "discrete",
-      "values": [
+      "values": [5]
-        5
-      ]
     },
     "fl_gamma": {
       "type": "discrete",
-      "values": [
+      "values": [0]
-        0
-      ]
     },
     "fliplr": {
       "type": "discrete",
-      "values": [
+      "values": [0]
-        0
-      ]
     },
     "flipud": {
       "type": "discrete",
-      "values": [
+      "values": [0]
-        0
-      ]
     },
     "hsv_h": {
       "type": "discrete",
-      "values": [
+      "values": [0]
-        0
-      ]
     },
     "hsv_s": {
       "type": "discrete",
-      "values": [
+      "values": [0]
-        0
-      ]
     },
     "hsv_v": {
       "type": "discrete",
-      "values": [
+      "values": [0]
-        0
-      ]
     },
     "iou_t": {
       "type": "discrete",
-      "values": [
+      "values": [0.7]
-        0.7
-      ]
     },
     "lr0": {
       "type": "discrete",
-      "values": [
+      "values": [1e-5, 0.1]
-        1e-05,
-        0.1
-      ]
     },
     "lrf": {
       "type": "discrete",
-      "values": [
+      "values": [0.01, 1]
-        0.01,
-        1
-      ]
     },
     "mixup": {
       "type": "discrete",
-      "values": [
+      "values": [1]
-        1
-      ]
     },
     "momentum": {
       "type": "discrete",
-      "values": [
+      "values": [0.6]
-        0.6
-      ]
     },
     "mosaic": {
       "type": "discrete",
-      "values": [
+      "values": [0]
-        0
-      ]
     },
     "obj": {
       "type": "discrete",
-      "values": [
+      "values": [0.2]
-        0.2
-      ]
     },
     "obj_pw": {
       "type": "discrete",
-      "values": [
+      "values": [0.5]
-        0.5
-      ]
     },
     "optimizer": {
       "type": "categorical",
-      "values": [
+      "values": ["SGD", "Adam", "AdamW"]
-        "SGD",
-        "Adam",
-        "AdamW"
-      ]
     },
     "perspective": {
       "type": "discrete",
-      "values": [
+      "values": [0]
-        0
-      ]
     },
     "scale": {
       "type": "discrete",
-      "values": [
+      "values": [0]
-        0
-      ]
     },
     "shear": {
       "type": "discrete",
-      "values": [
+      "values": [0]
-        0
-      ]
     },
     "translate": {
       "type": "discrete",
-      "values": [
+      "values": [0]
-        0
-      ]
     },
     "warmup_bias_lr": {
       "type": "discrete",
-      "values": [
+      "values": [0, 0.2]
-        0,
-        0.2
-      ]
     },
     "warmup_epochs": {
       "type": "discrete",
-      "values": [
+      "values": [5]
-        5
-      ]
     },
     "warmup_momentum": {
       "type": "discrete",
-      "values": [
+      "values": [0, 0.95]
-        0,
-        0.95
-      ]
     },
     "weight_decay": {
       "type": "discrete",
-      "values": [
+      "values": [0, 0.001]
-        0,
-        0.001
-      ]
     }
   },
   "spec": {