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Add YOLOv3 and YOLOv5 model *.yaml files (#169)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Ayush Chaurasia 2 years ago committed by GitHub
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commit 341ce0ff3f
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28 changed files with 383 additions and 6 deletions
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2
tests/test_python.py
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@ -96,5 +96,5 @@ def test_export_paddle():
def test_all_model_yamls():
for m in list((ROOT / 'yolo/v8/models').rglob('*.yaml')):
for m in list((ROOT / 'models').rglob('*.yaml')):
YOLO(m.name)

24
ultralytics/models/README.md
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@ -0,0 +1,24 @@
## Models HUB
Here are the models that are supported out-of-the-box with Ultralytics. For a detailed view and navigation, visit [model hub](<>) section of the docs.
### Usage
You can simply set the `model` parameter to any available yaml config or pretained weights
```bash
yolo task=... mode=... model=yolov5n.yaml
```
| Model | Version/ | size (pixels) | mAPval 50-95 | Speed CPU b1 (ms) | params (M) | FLOPs @640 (B) | model file | Pretrained Weights |
| ------------------ | -------- | ------------- | ------------ | ----------------- | ---------- | -------------- | ------------- | ------------------ |
| YOLOv5n | v6.3 | 640 | 28.0 | 45 | 1.9 | 4.5 | yolov5n.yaml | - |
| YOLOv5s | - | 640 | 37.4 | 98 | 7.2 | 16.5 | yolov5s.yaml | - |
| YOLOv5m | - | 640 | 45.4 | 224 | 21.2 | 49.0 | yolov5m.yaml | - |
| YOLOv5l | - | 640 | 49.0 | 430 | 46.5 | 109.1 | yolov5l.yaml | - |
| YOLOv5x | - | 640 | 50.7 | 766 | 86.7 | 205.7 | yolov5x.yaml | - |
| YOLOv5n6 | - | 1280 | 36.0 | 153 | 3.2 | 4.6 | yolov5n6.yaml | - |
| YOLOv5s6 | - | 1280 | 44.8 | 385 | 12.6 | 16.8 | yolov5s6.yaml | - |
| YOLOv5m6 | - | 1280 | 51.3 | 887 | 35.7 | 50.0 | yolov5m6.yaml | - |
| YOLOv5l6 | - | 1280 | 53.7 | 1784 | 76.8 | 111.4 | yolov5l6.yaml | - |
| YOLOv5x6 + \[TTA\] | - | 1280 1536 | 55.0 55.8 | 3136 - | 140.7 - | 209.8 - | yolov5x6.yaml | - |

47
ultralytics/models/v3/yolov3-spp.yaml
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# Ultralytics YOLO 🚀, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# darknet53 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Bottleneck, [64]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 2, Bottleneck, [128]],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
[-1, 8, Bottleneck, [256]],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
[-1, 8, Bottleneck, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
[-1, 4, Bottleneck, [1024]], # 10
]
# YOLOv3-SPP head
head:
[[-1, 1, Bottleneck, [1024, False]],
[-1, 1, SPP, [512, [5, 9, 13]]],
[-1, 1, Conv, [1024, 3, 1]],
[-1, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 8], 1, Concat, [1]], # cat backbone P4
[-1, 1, Bottleneck, [512, False]],
[-1, 1, Bottleneck, [512, False]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P3
[-1, 1, Bottleneck, [256, False]],
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
[[27, 22, 15], 1, Detect, [nc]], # Detect(P3, P4, P5)
]

38
ultralytics/models/v3/yolov3-tiny.yaml
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# Ultralytics YOLO 🚀, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# YOLOv3-tiny backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [16, 3, 1]], # 0
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2
[-1, 1, Conv, [32, 3, 1]],
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11
[-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12
]
# YOLOv3-tiny head
head:
[[-1, 1, Conv, [1024, 3, 1]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large)
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 8], 1, Concat, [1]], # cat backbone P4
[-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium)
[[19, 15], 1, Detect, [nc]], # Detect(P4, P5)
]

47
ultralytics/models/v3/yolov3.yaml
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@ -0,0 +1,47 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# darknet53 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Bottleneck, [64]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 2, Bottleneck, [128]],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
[-1, 8, Bottleneck, [256]],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
[-1, 8, Bottleneck, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
[-1, 4, Bottleneck, [1024]], # 10
]
# YOLOv3 head
head:
[[-1, 1, Bottleneck, [1024, False]],
[-1, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [1024, 3, 1]],
[-1, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 8], 1, Concat, [1]], # cat backbone P4
[-1, 1, Bottleneck, [512, False]],
[-1, 1, Bottleneck, [512, False]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P3
[-1, 1, Bottleneck, [256, False]],
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
[[27, 22, 15], 1, Detect, [nc]], # Detect(P3, P4, P5)
]

44
ultralytics/models/v5/yolov5l.yaml
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@ -0,0 +1,44 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# 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]], # Detect(P3, P4, P5)
]

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ultralytics/models/v5/yolov5m.yaml
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# Ultralytics YOLO 🚀, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 0.67 # model depth multiple
width_multiple: 0.75 # layer channel multiple
# 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]], # Detect(P3, P4, P5)
]

44
ultralytics/models/v5/yolov5n.yaml
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@ -0,0 +1,44 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.25 # layer channel multiple
# 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]], # Detect(P3, P4, P5)
]

45
ultralytics/models/v5/yolov5s.yaml
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@ -0,0 +1,45 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
# 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]], # Detect(P3, P4, P5)
]

44
ultralytics/models/v5/yolov5x.yaml
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@ -0,0 +1,44 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 1.33 # model depth multiple
width_multiple: 1.25 # layer channel multiple
# 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]], # Detect(P3, P4, P5)
]

0
ultralytics/yolo/v8/models/cls/yolov8l-cls.yaml → ultralytics/models/v8/cls/yolov8l-cls.yaml
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ultralytics/yolo/v8/models/cls/yolov8m-cls.yaml → ultralytics/models/v8/cls/yolov8m-cls.yaml
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ultralytics/yolo/v8/models/cls/yolov8n-cls.yaml → ultralytics/models/v8/cls/yolov8n-cls.yaml
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ultralytics/yolo/v8/models/cls/yolov8s-cls.yaml → ultralytics/models/v8/cls/yolov8s-cls.yaml
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ultralytics/yolo/v8/models/cls/yolov8x-cls.yaml → ultralytics/models/v8/cls/yolov8x-cls.yaml
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ultralytics/yolo/v8/models/seg/yolov8l-seg.yaml → ultralytics/models/v8/seg/yolov8l-seg.yaml
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ultralytics/yolo/v8/models/seg/yolov8m-seg.yaml → ultralytics/models/v8/seg/yolov8m-seg.yaml
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ultralytics/yolo/v8/models/seg/yolov8n-seg.yaml → ultralytics/models/v8/seg/yolov8n-seg.yaml
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ultralytics/yolo/v8/models/seg/yolov8s-seg.yaml → ultralytics/models/v8/seg/yolov8s-seg.yaml
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ultralytics/yolo/v8/models/seg/yolov8x-seg.yaml → ultralytics/models/v8/seg/yolov8x-seg.yaml
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ultralytics/yolo/v8/models/yolov8l.yaml → ultralytics/models/v8/yolov8l.yaml
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ultralytics/yolo/v8/models/yolov8m.yaml → ultralytics/models/v8/yolov8m.yaml
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ultralytics/yolo/v8/models/yolov8n.yaml → ultralytics/models/v8/yolov8n.yaml
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ultralytics/yolo/v8/models/yolov8s.yaml → ultralytics/models/v8/yolov8s.yaml
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ultralytics/yolo/v8/models/yolov8x.yaml → ultralytics/models/v8/yolov8x.yaml
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ultralytics/yolo/v8/models/yolov8x6.yaml → ultralytics/models/v8/yolov8x6.yaml
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6
ultralytics/yolo/engine/predictor.py
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@ -57,7 +57,7 @@ class BasePredictor:
dataset (Dataset): Dataset used for prediction.
vid_path (str): Path to video file.
vid_writer (cv2.VideoWriter): Video writer for saving video output.
view_img (bool): Whether to view image output.
show (bool): Whether to view image output.
annotator (Annotator): Annotator used for prediction.
data_path (str): Path to data.
"""
@ -88,7 +88,7 @@ class BasePredictor:
self.device = None
self.dataset = None
self.vid_path, self.vid_writer = None, None
self.view_img = None
self.show = None
self.annotator = None
self.data_path = None
self.callbacks = defaultdict(list, {k: [v] for k, v in callbacks.default_callbacks.items()}) # add callbacks
@ -127,7 +127,7 @@ class BasePredictor:
# Dataloader
bs = 1 # batch_size
if webcam:
self.view_img = check_imshow(warn=True)
self.show = check_imshow(warn=True)
self.dataset = LoadStreams(source, imgsz=imgsz, stride=stride, auto=pt, vid_stride=self.args.vid_stride)
bs = len(self.dataset)
elif screenshot:

4
ultralytics/yolo/utils/checks.py
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@ -217,8 +217,8 @@ def check_file(file, suffix=''):
return file
else: # search
files = []
for d in 'data', 'v8', 'utils': # search directories
files.extend(glob.glob(str(ROOT / "yolo" / d / '**' / file), recursive=True)) # find file
for d in 'models', 'yolo/data': # search directories
files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True)) # find file
assert len(files), f'File not found: {file}' # assert file was found
assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique
return files[0] # return file

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