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v2.0 Release (#491)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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16
README.md
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@ -8,26 +8,28 @@ This repository represents Ultralytics open-source research into future object d
<img src="https://user-images.githubusercontent.com/26833433/85340570-30360a80-b49b-11ea-87cf-bdf33d53ae15.png" width="1000">** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. <img src="https://user-images.githubusercontent.com/26833433/85340570-30360a80-b49b-11ea-87cf-bdf33d53ae15.png" width="1000">** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
- **July 23, 2020**: [v2.0 release](https://arxiv.org/abs/1803.01534): improved model definition, training and mAP []().
- **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, faster inference and improved mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972). - **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, faster inference and improved mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972).
- **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145). - **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145).
- **June 9, 2020**: [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks) updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP). - **June 9, 2020**: [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks) updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
- **May 27, 2020**: Public release of repo. YOLOv5 models are SOTA among all known YOLO implementations. - **May 27, 2020**: Public release. YOLOv5 models are SOTA among all known YOLO implementations.
- **April 1, 2020**: Start development of future [YOLOv3](https://github.com/ultralytics/yolov3)/[YOLOv4](https://github.com/AlexeyAB/darknet)-based PyTorch models in a range of compound-scaled sizes. - **April 1, 2020**: Start development of future compound-scaled [YOLOv3](https://github.com/ultralytics/yolov3)/[YOLOv4](https://github.com/AlexeyAB/darknet)-based PyTorch models.
## Pretrained Checkpoints ## Pretrained Checkpoints
| Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPS | | Model | AP<sup>val</sup> | AP<sup>test</sup> | AP<sub>50</sub> | Speed<sub>GPU</sub> | FPS<sub>GPU</sub> || params | FLOPS |
|---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: | |---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: |
| [YOLOv5s](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 36.6 | 36.6 | 55.8 | **2.1ms** | **476** || 7.5M | 13.2B | [YOLOv5.1s](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 36.1 | 36.1 | 55.3 | **2.1ms** | **476** || 7.5M | 13.2B
| [YOLOv5m](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 43.4 | 43.4 | 62.4 | 3.0ms | 333 || 21.8M | 39.4B | [YOLOv5.1m](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 43.5 | 43.5 | 62.5 | 3.0ms | 333 || 21.8M | 39.4B
| [YOLOv5l](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 46.6 | 46.7 | 65.4 | 3.9ms | 256 || 47.8M | 88.1B | [YOLOv5.1l](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 47.0 | 47.1 | 65.6 | 3.9ms | 256 || 47.8M | 88.1B
| [YOLOv5x](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | **48.4** | **48.4** | **66.9** | 6.1ms | 164 || 89.0M | 166.4B | [YOLOv5.1x](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | **49.0** | **49.0** | **67.4** | 6.1ms | 164 || 89.0M | 166.4B
| | | | | | || |
| [YOLOv3-SPP](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 45.6 | 45.5 | 65.2 | 4.5ms | 222 || 63.0M | 118.0B | [YOLOv3-SPP](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 45.6 | 45.5 | 65.2 | 4.5ms | 222 || 63.0M | 118.0B
** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy. ** AP<sup>test</sup> denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy.
** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python test.py --data coco.yaml --img 736 --conf 0.001` ** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python test.py --data coco.yaml --img 672 --conf 0.001`
** Speed<sub>GPU</sub> measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by `python test.py --data coco.yaml --img 640 --conf 0.1` ** Speed<sub>GPU</sub> measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by `python test.py --data coco.yaml --img 640 --conf 0.1`
** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). ** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).

20
models/yolo.py
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@ -5,7 +5,7 @@ from models.experimental import *
class Detect(nn.Module): class Detect(nn.Module):
def __init__(self, nc=80, anchors=()): # detection layer def __init__(self, nc=80, anchors=(), ch=()): # detection layer
super(Detect, self).__init__() super(Detect, self).__init__()
self.stride = None # strides computed during build self.stride = None # strides computed during build
self.nc = nc # number of classes self.nc = nc # number of classes
@ -16,6 +16,7 @@ class Detect(nn.Module):
a = torch.tensor(anchors).float().view(self.nl, -1, 2) a = torch.tensor(anchors).float().view(self.nl, -1, 2)
self.register_buffer('anchors', a) # shape(nl,na,2) self.register_buffer('anchors', a) # shape(nl,na,2)
self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
self.export = False # onnx export self.export = False # onnx export
def forward(self, x): def forward(self, x):
@ -23,6 +24,7 @@ class Detect(nn.Module):
z = [] # inference output z = [] # inference output
self.training |= self.export self.training |= self.export
for i in range(self.nl): for i in range(self.nl):
x[i] = self.m[i](x[i]) # conv
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
@ -124,8 +126,7 @@ class Model(nn.Module):
def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
m = self.model[-1] # Detect() module m = self.model[-1] # Detect() module
for f, s in zip(m.f, m.stride): #  from for mi, s in zip(m.m, m.stride): #  from
mi = self.model[f % m.i]
b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) b[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls b[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls
@ -133,9 +134,9 @@ class Model(nn.Module):
def _print_biases(self): def _print_biases(self):
m = self.model[-1] # Detect() module m = self.model[-1] # Detect() module
for f in sorted([x % m.i for x in m.f]): #  from for mi in m.m: #  from
b = self.model[f].bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85) b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
print(('%g Conv2d.bias:' + '%10.3g' * 6) % (f, *b[:5].mean(1).tolist(), b[5:].mean())) print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
# def _print_weights(self): # def _print_weights(self):
# for m in self.model.modules(): # for m in self.model.modules():
@ -159,7 +160,7 @@ class Model(nn.Module):
def parse_model(d, ch): # model_dict, input_channels(3) def parse_model(d, ch): # model_dict, input_channels(3)
print('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) print('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
na = (len(anchors[0]) // 2) # number of anchors na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
no = na * (nc + 5) # number of outputs = anchors * (classes + 5) no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
@ -181,6 +182,7 @@ def parse_model(d, ch): # model_dict, input_channels(3)
# e = math.log(c2 / ch[1]) / math.log(2) # e = math.log(c2 / ch[1]) / math.log(2)
# c2 = int(ch[1] * ex ** e) # c2 = int(ch[1] * ex ** e)
# if m != Focus: # if m != Focus:
c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
# Experimental # Experimental
@ -201,7 +203,9 @@ def parse_model(d, ch): # model_dict, input_channels(3)
elif m is Concat: elif m is Concat:
c2 = sum([ch[-1 if x == -1 else x + 1] for x in f]) c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
elif m is Detect: elif m is Detect:
f = f or list(reversed([(-1 if j == i else j - 1) for j, x in enumerate(ch) if x == no])) args.append([ch[x + 1] for x in f])
if isinstance(args[1], int): # number of anchors
args[1] = [list(range(args[1] * 2))] * len(f)
else: else:
c2 = ch[f] c2 = ch[f]

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models/yolov5l.yaml
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@ -5,9 +5,9 @@ width_multiple: 1.0 # layer channel multiple
# anchors # anchors
anchors: anchors:
- [116,90, 156,198, 373,326] # P5/32
- [30,61, 62,45, 59,119] # P4/16
- [10,13, 16,30, 33,23] # P3/8 - [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 backbone # YOLOv5 backbone
backbone: backbone:
@ -21,13 +21,12 @@ backbone:
[-1, 9, BottleneckCSP, [512]], [-1, 9, BottleneckCSP, [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, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, BottleneckCSP, [1024, False]], # 9
] ]
# YOLOv5 head # YOLOv5 head
head: head:
[[-1, 3, BottleneckCSP, [1024, False]], # 9 [[-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, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13 [-1, 3, BottleneckCSP, [512, False]], # 13
@ -35,18 +34,15 @@ head:
[-1, 1, Conv, [256, 1, 1]], [-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, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]], [-1, 3, BottleneckCSP, [256, False]], # 17
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 18 (P3/8-small)
[-2, 1, Conv, [256, 3, 2]], [-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4 [[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]], [-1, 3, BottleneckCSP, [512, False]], # 20
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 22 (P4/16-medium)
[-2, 1, Conv, [512, 3, 2]], [-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5 [[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]], [-1, 3, BottleneckCSP, [1024, False]], # 23
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 26 (P5/32-large)
[[], 1, Detect, [nc, anchors]], # Detect(P5, P4, P3) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
] ]

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models/yolov5m.yaml
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@ -5,9 +5,9 @@ width_multiple: 0.75 # layer channel multiple
# anchors # anchors
anchors: anchors:
- [116,90, 156,198, 373,326] # P5/32
- [30,61, 62,45, 59,119] # P4/16
- [10,13, 16,30, 33,23] # P3/8 - [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 backbone # YOLOv5 backbone
backbone: backbone:
@ -21,13 +21,12 @@ backbone:
[-1, 9, BottleneckCSP, [512]], [-1, 9, BottleneckCSP, [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, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, BottleneckCSP, [1024, False]], # 9
] ]
# YOLOv5 head # YOLOv5 head
head: head:
[[-1, 3, BottleneckCSP, [1024, False]], # 9 [[-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, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13 [-1, 3, BottleneckCSP, [512, False]], # 13
@ -35,18 +34,15 @@ head:
[-1, 1, Conv, [256, 1, 1]], [-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, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]], [-1, 3, BottleneckCSP, [256, False]], # 17
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 18 (P3/8-small)
[-2, 1, Conv, [256, 3, 2]], [-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4 [[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]], [-1, 3, BottleneckCSP, [512, False]], # 20
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 22 (P4/16-medium)
[-2, 1, Conv, [512, 3, 2]], [-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5 [[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]], [-1, 3, BottleneckCSP, [1024, False]], # 23
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 26 (P5/32-large)
[[], 1, Detect, [nc, anchors]], # Detect(P5, P4, P3) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
] ]

24
models/yolov5s.yaml
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@ -5,9 +5,9 @@ width_multiple: 0.50 # layer channel multiple
# anchors # anchors
anchors: anchors:
- [116,90, 156,198, 373,326] # P5/32
- [30,61, 62,45, 59,119] # P4/16
- [10,13, 16,30, 33,23] # P3/8 - [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 backbone # YOLOv5 backbone
backbone: backbone:
@ -21,13 +21,12 @@ backbone:
[-1, 9, BottleneckCSP, [512]], [-1, 9, BottleneckCSP, [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, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, BottleneckCSP, [1024, False]], # 9
] ]
# YOLOv5 head # YOLOv5 head
head: head:
[[-1, 3, BottleneckCSP, [1024, False]], # 9 [[-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, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13 [-1, 3, BottleneckCSP, [512, False]], # 13
@ -35,18 +34,15 @@ head:
[-1, 1, Conv, [256, 1, 1]], [-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, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]], [-1, 3, BottleneckCSP, [256, False]], # 17
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 18 (P3/8-small)
[-2, 1, Conv, [256, 3, 2]], [-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4 [[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]], [-1, 3, BottleneckCSP, [512, False]], # 20
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 22 (P4/16-medium)
[-2, 1, Conv, [512, 3, 2]], [-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5 [[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]], [-1, 3, BottleneckCSP, [1024, False]], # 23
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 26 (P5/32-large)
[[], 1, Detect, [nc, anchors]], # Detect(P5, P4, P3) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
] ]

24
models/yolov5x.yaml
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@ -5,9 +5,9 @@ width_multiple: 1.25 # layer channel multiple
# anchors # anchors
anchors: anchors:
- [116,90, 156,198, 373,326] # P5/32
- [30,61, 62,45, 59,119] # P4/16
- [10,13, 16,30, 33,23] # P3/8 - [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 backbone # YOLOv5 backbone
backbone: backbone:
@ -21,13 +21,12 @@ backbone:
[-1, 9, BottleneckCSP, [512]], [-1, 9, BottleneckCSP, [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, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, BottleneckCSP, [1024, False]], # 9
] ]
# YOLOv5 head # YOLOv5 head
head: head:
[[-1, 3, BottleneckCSP, [1024, False]], # 9 [[-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, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13 [-1, 3, BottleneckCSP, [512, False]], # 13
@ -35,18 +34,15 @@ head:
[-1, 1, Conv, [256, 1, 1]], [-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, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]], [-1, 3, BottleneckCSP, [256, False]], # 17
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 18 (P3/8-small)
[-2, 1, Conv, [256, 3, 2]], [-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4 [[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, BottleneckCSP, [512, False]], [-1, 3, BottleneckCSP, [512, False]], # 20
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 22 (P4/16-medium)
[-2, 1, Conv, [512, 3, 2]], [-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5 [[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]], [-1, 3, BottleneckCSP, [1024, False]], # 23
[-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1]], # 26 (P5/32-large)
[[], 1, Detect, [nc, anchors]], # Detect(P5, P4, P3) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
] ]

12
train.py
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@ -27,16 +27,16 @@ hyp = {'optimizer': 'SGD', # ['adam', 'SGD', None] if none, default is SGD
'momentum': 0.937, # SGD momentum/Adam beta1 'momentum': 0.937, # SGD momentum/Adam beta1
'weight_decay': 5e-4, # optimizer weight decay 'weight_decay': 5e-4, # optimizer weight decay
'giou': 0.05, # giou loss gain 'giou': 0.05, # giou loss gain
'cls': 0.58, # cls loss gain 'cls': 0.5, # cls loss gain
'cls_pw': 1.0, # cls BCELoss positive_weight 'cls_pw': 1.0, # cls BCELoss positive_weight
'obj': 1.0, # obj loss gain (*=img_size/320 if img_size != 320) 'obj': 1.0, # obj loss gain (*=img_size/320 if img_size != 320)
'obj_pw': 1.0, # obj BCELoss positive_weight 'obj_pw': 1.0, # obj BCELoss positive_weight
'iou_t': 0.20, # iou training threshold 'iou_t': 0.20, # iou training threshold
'anchor_t': 4.0, # anchor-multiple threshold 'anchor_t': 4.0, # anchor-multiple threshold
'fl_gamma': 0.0, # focal loss gamma (efficientDet default is gamma=1.5) 'fl_gamma': 0.0, # focal loss gamma (efficientDet default is gamma=1.5)
'hsv_h': 0.014, # image HSV-Hue augmentation (fraction) 'hsv_h': 0.015, # image HSV-Hue augmentation (fraction)
'hsv_s': 0.68, # image HSV-Saturation augmentation (fraction) 'hsv_s': 0.7, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.36, # image HSV-Value augmentation (fraction) 'hsv_v': 0.4, # image HSV-Value augmentation (fraction)
'degrees': 0.0, # image rotation (+/- deg) 'degrees': 0.0, # image rotation (+/- deg)
'translate': 0.0, # image translation (+/- fraction) 'translate': 0.0, # image translation (+/- fraction)
'scale': 0.5, # image scale (+/- gain) 'scale': 0.5, # image scale (+/- gain)
@ -159,7 +159,7 @@ def train(hyp, tb_writer, opt, device):
model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0) model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
# Scheduler https://arxiv.org/pdf/1812.01187.pdf # Scheduler https://arxiv.org/pdf/1812.01187.pdf
lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.9 + 0.1 # cosine lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.8 + 0.2 # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822 # https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
# plot_lr_scheduler(optimizer, scheduler, epochs) # plot_lr_scheduler(optimizer, scheduler, epochs)
@ -334,7 +334,7 @@ def train(hyp, tb_writer, opt, device):
if rank in [-1, 0]: if rank in [-1, 0]:
# mAP # mAP
if ema is not None: if ema is not None:
ema.update_attr(model, include=['md', 'nc', 'hyp', 'gr', 'names', 'stride']) ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride'])
final_epoch = epoch + 1 == epochs final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test.test(opt.data, results, maps, times = test.test(opt.data,

2
utils/torch_utils.py
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@ -65,7 +65,7 @@ def initialize_weights(model):
if t is nn.Conv2d: if t is nn.Conv2d:
pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif t is nn.BatchNorm2d: elif t is nn.BatchNorm2d:
m.eps = 1e-4 m.eps = 1e-3
m.momentum = 0.03 m.momentum = 0.03
elif t in [nn.LeakyReLU, nn.ReLU, nn.ReLU6]: elif t in [nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
m.inplace = True m.inplace = True

9
utils/utils.py
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@ -5,10 +5,10 @@ import random
import shutil import shutil
import subprocess import subprocess
import time import time
from contextlib import contextmanager
from copy import copy from copy import copy
from pathlib import Path from pathlib import Path
from sys import platform from sys import platform
from contextlib import contextmanager
import cv2 import cv2
import matplotlib import matplotlib
@ -110,6 +110,7 @@ def check_anchor_order(m):
da = a[-1] - a[0] # delta a da = a[-1] - a[0] # delta a
ds = m.stride[-1] - m.stride[0] # delta s ds = m.stride[-1] - m.stride[0] # delta s
if da.sign() != ds.sign(): # same order if da.sign() != ds.sign(): # same order
print('Reversing anchor order')
m.anchors[:] = m.anchors.flip(0) m.anchors[:] = m.anchors.flip(0)
m.anchor_grid[:] = m.anchor_grid.flip(0) m.anchor_grid[:] = m.anchor_grid.flip(0)
@ -459,7 +460,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
# per output # per output
nt = 0 # number of targets nt = 0 # number of targets
np = len(p) # number of outputs np = len(p) # number of outputs
balance = [1.0, 1.0, 1.0] balance = [4.0, 1.0, 0.4] if np == 3 else [4.0, 1.0, 0.4, 0.1] # P3-5 or P3-6
for i, pi in enumerate(p): # layer index, layer predictions for i, pi in enumerate(p): # layer index, layer predictions
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
tobj = torch.zeros_like(pi[..., 0]).to(device) # target obj tobj = torch.zeros_like(pi[..., 0]).to(device) # target obj
@ -493,7 +494,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
s = 3 / np # output count scaling s = 3 / np # output count scaling
lbox *= h['giou'] * s lbox *= h['giou'] * s
lobj *= h['obj'] * s lobj *= h['obj'] * s * (1.4 if np == 4 else 1.)
lcls *= h['cls'] * s lcls *= h['cls'] * s
bs = tobj.shape[0] # batch size bs = tobj.shape[0] # batch size
if red == 'sum': if red == 'sum':
@ -1119,7 +1120,7 @@ def plot_study_txt(f='study.txt', x=None): # from utils.utils import *; plot_st
ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8, ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8,
label=Path(f).stem.replace('study_coco_', '').replace('yolo', 'YOLO')) label=Path(f).stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [33.5, 39.1, 42.5, 45.9, 49., 50.5], ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [33.8, 39.6, 43.0, 47.5, 49.4, 50.7],
'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet') 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')
ax2.grid() ax2.grid()

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