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yolov5
Unverified 提交 956be8e6 authored 作者: Glenn Jocher's avatar Glenn Jocher 提交者： GitHub
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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
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README.md
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...@@ -191,7 +191,7 @@ Get started in seconds with our verified environments. Click each icon below for ...@@ -191,7 +191,7 @@ Get started in seconds with our verified environments. Click each icon below for
</a> </a>
</div> </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) | |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) |
...@@ -207,53 +207,47 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi ...@@ -207,53 +207,47 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
## <div align="center">Why YOLOv5</div> ## <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> <details>
<summary>YOLOv5-P5 640 Figure (click to expand)</summary> <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>
<details> <details>
<summary>Figure Notes (click to expand)</summary> <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 * **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.
32, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. * **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. * **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
* **Reproduce** by * **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
`python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
</details> </details>
### Pretrained Checkpoints ### Pretrained Checkpoints
[assets]: https://github.com/ultralytics/yolov5/releases [assets]: https://github.com/ultralytics/yolov5/releases
[TTA]: https://github.com/ultralytics/yolov5/issues/303
|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)
|--- |--- |--- |--- |--- |--- |---|--- |--- |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)
|[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 |[YOLOv5n][assets] |640 |28.4 |46.0 |**45** |**6.3**|**0.6**|**1.9**|**4.5**
|[YOLOv5l][assets] |640 |48.2 |48.2 |66.9 |3.8 | |47.0 |115.4 |[YOLOv5s][assets] |640 |37.2 |56.0 |98 |6.4 |0.9 |7.2 |16.5
|[YOLOv5x][assets] |640 |**50.4** |**50.4** |**68.8** |6.1 | |87.7 |218.8 |[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
|[YOLOv5s6][assets] |1280 |43.3 |43.3 |61.9 |**4.3** | |12.7 |17.4 |[YOLOv5x][assets] |640 |50.7 |68.9 |766 |12.1 |4.8 |86.7 |205.7
|[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 | |- |- |[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> <details>
<summary>Table Notes (click to expand)</summary> <summary>Table Notes (click to expand)</summary>
* All checkpoints are trained to 300 epochs with default settings and hyperparameters. * 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 * **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`
denote val2017 accuracy. * **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`
* **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` * **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`
* **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`
</details> </details>
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data/hyps/hyp.scratch-p6.yaml → data/hyps/hyp.scratch-high.yaml
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license # 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 # 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 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
...@@ -30,5 +30,5 @@ perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 ...@@ -30,5 +30,5 @@ perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability) flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability) fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability) mosaic: 1.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability) mixup: 0.1 # image mixup (probability)
copy_paste: 0.0 # segment copy-paste (probability) copy_paste: 0.1 # segment copy-paste (probability)
\ No newline at end of file
data/hyps/hyp.scratch-low.yaml 0 → 100644
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# 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)
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data/hyps/hyp.scratch.yaml
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...@@ -4,7 +4,7 @@ ...@@ -4,7 +4,7 @@
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 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 momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4 weight_decay: 0.0005 # optimizer weight decay 5e-4
warmup_epochs: 3.0 # warmup epochs (fractions ok) warmup_epochs: 3.0 # warmup epochs (fractions ok)
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hubconf.py
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...@@ -70,6 +70,11 @@ def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None): ...@@ -70,6 +70,11 @@ def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None):
return _create(path, autoshape=autoshape, verbose=verbose, device=device) 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): def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv5-small model https://github.com/ultralytics/yolov5 # YOLOv5-small model https://github.com/ultralytics/yolov5
return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device) return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device)
...@@ -90,6 +95,11 @@ def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=Tru ...@@ -90,6 +95,11 @@ def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=Tru
return _create('yolov5x', 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): def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
# YOLOv5-small-P6 model https://github.com/ultralytics/yolov5 # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device) return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device)
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models/hub/yolov5l6.yaml
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...@@ -10,24 +10,24 @@ anchors: ...@@ -10,24 +10,24 @@ anchors:
- [140,301, 303,264, 238,542] # P5/32 - [140,301, 303,264, 238,542] # P5/32
- [436,615, 739,380, 925,792] # P6/64 - [436,615, 739,380, 925,792] # P6/64
# YOLOv5 backbone # YOLOv5 v6.0 backbone
backbone: backbone:
# [from, number, module, args] # [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, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, C3, [256]], [-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32 [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
[-1, 3, C3, [768]], [-1, 3, C3, [768]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
[-1, 1, SPP, [1024, [3, 5, 7]]], [-1, 3, C3, [1024]],
[-1, 3, C3, [1024, False]], # 11 [-1, 1, SPPF, [1024, 5]], # 11
] ]
# YOLOv5 head # 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']],
......
models/hub/yolov5m6.yaml
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...@@ -10,24 +10,24 @@ anchors: ...@@ -10,24 +10,24 @@ anchors:
- [140,301, 303,264, 238,542] # P5/32 - [140,301, 303,264, 238,542] # P5/32
- [436,615, 739,380, 925,792] # P6/64 - [436,615, 739,380, 925,792] # P6/64
# YOLOv5 backbone # YOLOv5 v6.0 backbone
backbone: backbone:
# [from, number, module, args] # [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, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, C3, [256]], [-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32 [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
[-1, 3, C3, [768]], [-1, 3, C3, [768]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
[-1, 1, SPP, [1024, [3, 5, 7]]], [-1, 3, C3, [1024]],
[-1, 3, C3, [1024, False]], # 11 [-1, 1, SPPF, [1024, 5]], # 11
] ]
# YOLOv5 head # 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']],
......
models/hub/yolov5n6.yaml 0 → 100644
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# 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
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...@@ -10,24 +10,24 @@ anchors: ...@@ -10,24 +10,24 @@ anchors:
- [140,301, 303,264, 238,542] # P5/32 - [140,301, 303,264, 238,542] # P5/32
- [436,615, 739,380, 925,792] # P6/64 - [436,615, 739,380, 925,792] # P6/64
# YOLOv5 backbone # YOLOv5 v6.0 backbone
backbone: backbone:
# [from, number, module, args] # [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, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, C3, [256]], [-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32 [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
[-1, 3, C3, [768]], [-1, 3, C3, [768]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
[-1, 1, SPP, [1024, [3, 5, 7]]], [-1, 3, C3, [1024]],
[-1, 3, C3, [1024, False]], # 11 [-1, 1, SPPF, [1024, 5]], # 11
] ]
# YOLOv5 head # 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']],
......
models/hub/yolov5x6.yaml
浏览文件 @ 956be8e6
...@@ -10,24 +10,24 @@ anchors: ...@@ -10,24 +10,24 @@ anchors:
- [140,301, 303,264, 238,542] # P5/32 - [140,301, 303,264, 238,542] # P5/32
- [436,615, 739,380, 925,792] # P6/64 - [436,615, 739,380, 925,792] # P6/64
# YOLOv5 backbone # YOLOv5 v6.0 backbone
backbone: backbone:
# [from, number, module, args] # [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, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, C3, [256]], [-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32 [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
[-1, 3, C3, [768]], [-1, 3, C3, [768]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
[-1, 1, SPP, [1024, [3, 5, 7]]], [-1, 3, C3, [1024]],
[-1, 3, C3, [1024, False]], # 11 [-1, 1, SPPF, [1024, 5]], # 11
] ]
# YOLOv5 head # 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']],
......
models/yolov5l.yaml
浏览文件 @ 956be8e6
...@@ -9,22 +9,22 @@ anchors: ...@@ -9,22 +9,22 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16 - [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [116,90, 156,198, 373,326] # P5/32
# YOLOv5 backbone # YOLOv5 v6.0 backbone
backbone: backbone:
# [from, number, module, args] # [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, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, C3, [256]], [-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 3, C3, [1024]],
[-1, 3, C3, [1024, False]], # 9 [-1, 1, SPPF, [1024, 5]], # 9
] ]
# YOLOv5 head # 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']],
......
models/yolov5m.yaml
浏览文件 @ 956be8e6
...@@ -9,22 +9,22 @@ anchors: ...@@ -9,22 +9,22 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16 - [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [116,90, 156,198, 373,326] # P5/32
# YOLOv5 backbone # YOLOv5 v6.0 backbone
backbone: backbone:
# [from, number, module, args] # [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, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, C3, [256]], [-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 3, C3, [1024]],
[-1, 3, C3, [1024, False]], # 9 [-1, 1, SPPF, [1024, 5]], # 9
] ]
# YOLOv5 head # 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']],
......
models/yolov5n.yaml 0 → 100644
浏览文件 @ 956be8e6
# 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
浏览文件 @ 956be8e6
...@@ -9,22 +9,22 @@ anchors: ...@@ -9,22 +9,22 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16 - [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [116,90, 156,198, 373,326] # P5/32
# YOLOv5 backbone # YOLOv5 v6.0 backbone
backbone: backbone:
# [from, number, module, args] # [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, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, C3, [256]], [-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 3, C3, [1024]],
[-1, 3, C3, [1024, False]], # 9 [-1, 1, SPPF, [1024, 5]], # 9
] ]
# YOLOv5 head # 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']],
......
models/yolov5x.yaml
浏览文件 @ 956be8e6
...@@ -9,22 +9,22 @@ anchors: ...@@ -9,22 +9,22 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16 - [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [116,90, 156,198, 373,326] # P5/32
# YOLOv5 backbone # YOLOv5 v6.0 backbone
backbone: backbone:
# [from, number, module, args] # [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, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, C3, [256]], [-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 3, C3, [1024]],
[-1, 3, C3, [1024, False]], # 9 [-1, 1, SPPF, [1024, 5]], # 9
] ]
# YOLOv5 head # 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']],
......
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