YOLOv5 release v6.0 (#5141)

* Update P5 models

* Update P6 models

* Update with GFLOPs and Params

* Update with GFLOPs and Params

* Update README

* Update

* Update README

* Update

* Update

* Add times

* Update README

* Update results

* Update results

* Update results

* Update hyps

* Update plots

* Update plots

* Update README.md

* Add nano models to hubconf.py

README.md

     </a>
 </div>
 
-|Weights and Biases|Roboflow - ⭐ NEW|
+|Weights and Biases|Roboflow ⭐ NEW|
 |:-:|:-:|
 |Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)|Label and automatically export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |
 
 
 ## <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>
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136901921-abcfcd9d-f978-4942-9b97-0e3f202907df.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>
+<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136763877-b174052b-c12f-48d2-8bc4-545e3853398e.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`
-
+* **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
+* **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
+* **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 yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
 </details>
 
 ### Pretrained Checkpoints
 
 [assets]: https://github.com/ultralytics/yolov5/releases
-
-|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>test<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>V100 (ms) | |params<br><sup>(M) |FLOPs<br><sup>640 (B)
-|---                    |---  |---      |---      |---      |---     |---|---   |---
-|[YOLOv5s][assets]      |640  |36.7     |36.7     |55.4     |**2.0** |   |7.3   |17.0
-|[YOLOv5m][assets]      |640  |44.5     |44.5     |63.1     |2.7     |   |21.4  |51.3
-|[YOLOv5l][assets]      |640  |48.2     |48.2     |66.9     |3.8     |   |47.0  |115.4
-|[YOLOv5x][assets]      |640  |**50.4** |**50.4** |**68.8** |6.1     |   |87.7  |218.8
-|                       |     |         |         |         |        |   |      |
-|[YOLOv5s6][assets]     |1280 |43.3     |43.3     |61.9     |**4.3** |   |12.7  |17.4
-|[YOLOv5m6][assets]     |1280 |50.5     |50.5     |68.7     |8.4     |   |35.9  |52.4
-|[YOLOv5l6][assets]     |1280 |53.4     |53.4     |71.1     |12.3    |   |77.2  |117.7
-|[YOLOv5x6][assets]     |1280 |**54.4** |**54.4** |**72.0** |22.4    |   |141.8 |222.9
-|                       |     |         |         |         |        |   |      |
-|[YOLOv5x6][assets] TTA |1280 |**55.0** |**55.0** |**72.0** |70.8    |   |-     |-
+[TTA]: https://github.com/ultralytics/yolov5/issues/303
+
+|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>CPU b1<br>(ms) |Speed<br><sup>V100 b1<br>(ms) |Speed<br><sup>V100 b32<br>(ms) |params<br><sup>(M) |FLOPs<br><sup>@640 (B)
+|---                    |---  |---    |---    |---    |---    |---    |---    |---
+|[YOLOv5n][assets]      |640  |28.4   |46.0   |**45** |**6.3**|**0.6**|**1.9**|**4.5**
+|[YOLOv5s][assets]      |640  |37.2   |56.0   |98     |6.4    |0.9    |7.2    |16.5
+|[YOLOv5m][assets]      |640  |45.2   |63.9   |224    |8.2    |1.7    |21.2   |49.0
+|[YOLOv5l][assets]      |640  |48.8   |67.2   |430    |10.1   |2.7    |46.5   |109.1
+|[YOLOv5x][assets]      |640  |50.7   |68.9   |766    |12.1   |4.8    |86.7   |205.7
+|                       |     |       |       |       |       |       |       |
+|[YOLOv5n6][assets]     |1280 |34.0   |50.7   |153    |8.1    |2.1    |3.2    |4.6
+|[YOLOv5s6][assets]     |1280 |44.5   |63.0   |385    |8.2    |3.6    |16.8   |12.6
+|[YOLOv5m6][assets]     |1280 |51.0   |69.0   |887    |11.1   |6.8    |35.7   |50.0
+|[YOLOv5l6][assets]     |1280 |53.6   |71.6   |1784   |15.8   |10.5   |76.8   |111.4
+|[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |54.7<br>**55.4** |**72.4**<br>72.3 |3136<br>- |26.2<br>- |19.4<br>- |140.7<br>- |209.8<br>- 
 
 <details>
   <summary>Table Notes (click to expand)</summary>
 
 * All checkpoints are trained to 300 epochs with default settings and hyperparameters.
-* AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results
-  denote val2017 accuracy.
-* **mAP** values are for single-model single-scale unless otherwise noted.<br>**Reproduce** by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
-* **Speed** 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.<br>**Reproduce**
-  by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 --half`
-* **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale.<br>**Reproduce** by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+* **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
+* **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`
+* **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
 
 </details>
 

data/hyps/hyp.scratch-p6.yaml → data/hyps/hyp.scratch-high.yaml

 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for COCO training from scratch
+# Hyperparameters for high-augmentation COCO training from scratch
 # python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
 
 flipud: 0.0  # image flip up-down (probability)
 fliplr: 0.5  # image flip left-right (probability)
 mosaic: 1.0  # image mosaic (probability)
-mixup: 0.0  # image mixup (probability)
-copy_paste: 0.0  # segment copy-paste (probability)
+mixup: 0.1  # image mixup (probability)
+copy_paste: 0.1  # segment copy-paste (probability)

data/hyps/hyp.scratch-low.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for low-augmentation COCO training from scratch
+# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.01  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 0.05  # box loss gain
+cls: 0.5  # cls loss gain
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 1.0  # obj loss gain (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+iou_t: 0.20  # IoU training threshold
+anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.5  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0  # image flip up-down (probability)
+fliplr: 0.5  # image flip left-right (probability)
+mosaic: 1.0  # image mosaic (probability)
+mixup: 0.0  # image mixup (probability)
+copy_paste: 0.0  # segment copy-paste (probability)

data/hyps/hyp.scratch.yaml

 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
 
 lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
-lrf: 0.2  # final OneCycleLR learning rate (lr0 * lrf)
+lrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)
 momentum: 0.937  # SGD momentum/Adam beta1
 weight_decay: 0.0005  # optimizer weight decay 5e-4
 warmup_epochs: 3.0  # warmup epochs (fractions ok)

hubconf.py

     return _create(path, autoshape=autoshape, verbose=verbose, device=device)
 
 
+def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+    # YOLOv5-nano model https://github.com/ultralytics/yolov5
+    return _create('yolov5n', pretrained, channels, classes, autoshape, verbose, device)
+
+
 def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
     # YOLOv5-small model https://github.com/ultralytics/yolov5
     return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device)
     return _create('yolov5x', pretrained, channels, classes, autoshape, verbose, device)
 
 
+def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+    # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
+    return _create('yolov5n6', pretrained, channels, classes, autoshape, verbose, device)
+
+
 def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
     # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
     return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device)

models/hub/yolov5l6.yaml

   - [140,301,  303,264,  238,542]  # P5/32
   - [436,615,  739,380,  925,792]  # P6/64
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 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
-   [-1, 9, C3, [256]],
+   [-1, 6, 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, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 11
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [768, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

models/hub/yolov5m6.yaml

   - [140,301,  303,264,  238,542]  # P5/32
   - [436,615,  739,380,  925,792]  # P6/64
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 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
-   [-1, 9, C3, [256]],
+   [-1, 6, 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, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 11
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [768, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

models/hub/yolov5n6.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 0.33  # model depth multiple
+width_multiple: 0.25  # 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
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [[-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
+   [-1, 6, 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, SPPF, [1024, 5]],  # 11
+  ]
+
+# YOLOv5 v6.0 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)
+  ]

models/hub/yolov5s6.yaml

   - [140,301,  303,264,  238,542]  # P5/32
   - [436,615,  739,380,  925,792]  # P6/64
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 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
-   [-1, 9, C3, [256]],
+   [-1, 6, 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, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 11
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [768, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

models/hub/yolov5x6.yaml

   - [140,301,  303,264,  238,542]  # P5/32
   - [436,615,  739,380,  925,792]  # P6/64
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 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
-   [-1, 9, C3, [256]],
+   [-1, 6, 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, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 11
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [768, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

models/yolov5l.yaml

   - [30,61, 62,45, 59,119]  # P4/16
   - [116,90, 156,198, 373,326]  # P5/32
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 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
-   [-1, 9, C3, [256]],
+   [-1, 6, 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, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [512, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

models/yolov5m.yaml

   - [30,61, 62,45, 59,119]  # P4/16
   - [116,90, 156,198, 373,326]  # P5/32
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 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
-   [-1, 9, C3, [256]],
+   [-1, 6, 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, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [512, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

models/yolov5n.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 0.33  # model depth multiple
+width_multiple: 0.25  # 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
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [[-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
+   [-1, 6, 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, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
+  ]
+
+# YOLOv5 v6.0 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, [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)
+
+   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
+  ]

models/yolov5s.yaml

   - [30,61, 62,45, 59,119]  # P4/16
   - [116,90, 156,198, 373,326]  # P5/32
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 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
-   [-1, 9, C3, [256]],
+   [-1, 6, 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, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [512, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

models/yolov5x.yaml

   - [30,61, 62,45, 59,119]  # P4/16
   - [116,90, 156,198, 373,326]  # P5/32
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 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
-   [-1, 9, C3, [256]],
+   [-1, 6, 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, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [512, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],