diff --git a/README.md b/README.md
index 06a21875aab5..cdefaeab4e60 100644
--- a/README.md
+++ b/README.md
@@ -191,7 +191,7 @@ Get started in seconds with our verified environments. Click each icon below for
-|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) |
@@ -207,53 +207,47 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi
## Why YOLOv5
-
+
YOLOv5-P5 640 Figure (click to expand)
-
+
Figure Notes (click to expand)
-* 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`
### Pretrained Checkpoints
[assets]: https://github.com/ultralytics/yolov5/releases
-
-|Model |size
(pixels) |mAPval
0.5:0.95 |mAPtest
0.5:0.95 |mAPval
0.5 |Speed
V100 (ms) | |params
(M) |FLOPs
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
(pixels) |mAPval
0.5:0.95 |mAPval
0.5 |Speed
CPU b1
(ms) |Speed
V100 b1
(ms) |Speed
V100 b32
(ms) |params
(M) |FLOPs
@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]
+ [TTA][TTA]|1280
1536 |54.7
**55.4** |**72.4**
72.3 |3136
- |26.2
- |19.4
- |140.7
- |209.8
-
Table Notes (click to expand)
* All checkpoints are trained to 300 epochs with default settings and hyperparameters.
-* APtest 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.
**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.
**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.
**Reproduce** by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
+* **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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.
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.
Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
diff --git a/data/hyps/hyp.scratch-p6.yaml b/data/hyps/hyp.scratch-high.yaml
similarity index 90%
rename from data/hyps/hyp.scratch-p6.yaml
rename to data/hyps/hyp.scratch-high.yaml
index 7aad818e5b16..519c82687e09 100644
--- a/data/hyps/hyp.scratch-p6.yaml
+++ b/data/hyps/hyp.scratch-high.yaml
@@ -1,5 +1,5 @@
# 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
@@ -30,5 +30,5 @@ 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)
+mixup: 0.1 # image mixup (probability)
+copy_paste: 0.1 # segment copy-paste (probability)
\ No newline at end of file
diff --git a/data/hyps/hyp.scratch-low.yaml b/data/hyps/hyp.scratch-low.yaml
new file mode 100644
index 000000000000..b093a95ac53b
--- /dev/null
+++ b/data/hyps/hyp.scratch-low.yaml
@@ -0,0 +1,34 @@
+# 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)
\ No newline at end of file
diff --git a/data/hyps/hyp.scratch.yaml b/data/hyps/hyp.scratch.yaml
index 77405a537067..31f6d142e285 100644
--- a/data/hyps/hyp.scratch.yaml
+++ b/data/hyps/hyp.scratch.yaml
@@ -4,7 +4,7 @@
# 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)
diff --git a/hubconf.py b/hubconf.py
index 3a89cf9763da..a697e033b09b 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -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)
+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)
@@ -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)
+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)
diff --git a/models/hub/yolov5l6.yaml b/models/hub/yolov5l6.yaml
index 1288f15f940b..632c2cb699e3 100644
--- a/models/hub/yolov5l6.yaml
+++ b/models/hub/yolov5l6.yaml
@@ -10,24 +10,24 @@ anchors:
- [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']],
diff --git a/models/hub/yolov5m6.yaml b/models/hub/yolov5m6.yaml
index f14f0b0ebcce..ecc53fd68ba6 100644
--- a/models/hub/yolov5m6.yaml
+++ b/models/hub/yolov5m6.yaml
@@ -10,24 +10,24 @@ anchors:
- [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']],
diff --git a/models/hub/yolov5n6.yaml b/models/hub/yolov5n6.yaml
new file mode 100644
index 000000000000..0c0c71d32551
--- /dev/null
+++ b/models/hub/yolov5n6.yaml
@@ -0,0 +1,60 @@
+# 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)
+ ]
diff --git a/models/hub/yolov5s6.yaml b/models/hub/yolov5s6.yaml
index 2baee5af9e05..a28fb559482b 100644
--- a/models/hub/yolov5s6.yaml
+++ b/models/hub/yolov5s6.yaml
@@ -10,24 +10,24 @@ anchors:
- [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']],
diff --git a/models/hub/yolov5x6.yaml b/models/hub/yolov5x6.yaml
index e94f592fc19a..ba795c4aad31 100644
--- a/models/hub/yolov5x6.yaml
+++ b/models/hub/yolov5x6.yaml
@@ -10,24 +10,24 @@ anchors:
- [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']],
diff --git a/models/yolov5l.yaml b/models/yolov5l.yaml
index 30b22a25a483..ce8a5de46a27 100644
--- a/models/yolov5l.yaml
+++ b/models/yolov5l.yaml
@@ -9,22 +9,22 @@ anchors:
- [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']],
diff --git a/models/yolov5m.yaml b/models/yolov5m.yaml
index f5f518ad8ab3..ad13ab370ff6 100644
--- a/models/yolov5m.yaml
+++ b/models/yolov5m.yaml
@@ -9,22 +9,22 @@ anchors:
- [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']],
diff --git a/models/yolov5n.yaml b/models/yolov5n.yaml
new file mode 100644
index 000000000000..8a28a40d6e20
--- /dev/null
+++ b/models/yolov5n.yaml
@@ -0,0 +1,48 @@
+# 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)
+ ]
diff --git a/models/yolov5s.yaml b/models/yolov5s.yaml
index b311ab7fd50a..f35beabb1e1c 100644
--- a/models/yolov5s.yaml
+++ b/models/yolov5s.yaml
@@ -9,22 +9,22 @@ anchors:
- [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']],
diff --git a/models/yolov5x.yaml b/models/yolov5x.yaml
index 7dcb822b8b84..f617a027d8a2 100644
--- a/models/yolov5x.yaml
+++ b/models/yolov5x.yaml
@@ -9,22 +9,22 @@ anchors:
- [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']],