YOLO11鏀硅繘 | 娉ㄦ剰鍔涙満鍒?| 杞婚噺绾х殑绌洪棿缁勫寮烘ā鍧桽GE銆愬叏缃戠嫭瀹躲€?

fangxinyzj.com 2025-05-30 01:20:47

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涓撴爮鍦板潃锛?/span>YOLO11鍏ラ棬 + 鏀硅繘娑ㄧ偣鈥斺€旂偣鍑诲嵆鍙烦杞?娆㈣繋璁㈤槄

鐩綍

1. 璁烘枃

2. 灏哠GE娣诲姞鍒癥OLO11涓?/p>
2.1 浠ｇ爜瀹炵幇

2.2 鏇存敼init.py鏂囦欢

2.3 鏂板yaml鏂囦欢

2.4 娉ㄥ唽妯″潡

2.5 鎵ц绋嬪簭

3.淇敼鍚庣殑缃戠粶缁撴瀯鍥?/p>
4. 瀹屾暣浠ｇ爜鍒嗕韩

5. GFLOPs

6. 杩涢樁

7.鎬荤粨

1. 璁烘枃

2. 灏哠GE娣诲姞鍒癥OLO11涓?/h2>
2.1 浠ｇ爜瀹炵幇

鍏抽敭姝ラ涓€锛?/strong>灏嗕笅闈唬鐮佺矘璐村埌鍦?ultralytics/ultralytics/nn/modules/block.py涓?/p>

from torch.nn import init class SpatialGroupEnhance(nn.Module): def __init__(self, groups=8): super().__init__() self.groups = groups self.avg_pool = nn.AdaptiveAvgPool2d(1) self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1)) self.bias = nn.Parameter(torch.zeros(1, groups, 1, 1)) self.sig = nn.Sigmoid() self.init_weights() def init_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): init.constant_(m.weight, 1) init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): init.normal_(m.weight, std=0.001) if m.bias is not None: init.constant_(m.bias, 0) def forward(self, x): b, c, h, w = x.shape x = x.view(b * self.groups, -1, h, w) # bs*g,dim//g,h,w xn = x * self.avg_pool(x) # bs*g,dim//g,h,w xn = xn.sum(dim=1, keepdim=True) # bs*g,1,h,w t = xn.view(b * self.groups, -1) # bs*g,h*w t = t - t.mean(dim=1, keepdim=True) # bs*g,h*w std = t.std(dim=1, keepdim=True) + 1e-5 t = t / std # bs*g,h*w t = t.view(b, self.groups, h, w) # bs,g,h*w t = t * self.weight + self.bias # bs,g,h*w t = t.view(b * self.groups, 1, h, w) # bs*g,1,h*w x = x * self.sig(t) x = x.view(b, c, h, w) return x

2.2 鏇存敼init.py鏂囦欢

鍏抽敭姝ラ浜岋細淇敼modules鏂囦欢澶逛笅鐨刜_init__.py鏂囦欢锛屽厛瀵煎叆鍑芥暟

2.3 鏂板yaml鏂囦欢

鍏抽敭姝ラ涓夛細鍦?\ultralytics\ultralytics\cfg\models\11涓嬫柊寤烘枃浠?yolo11_SGE.yaml骞跺皢涓嬮潰浠ｇ爜澶嶅埗杩涘幓

鐩爣妫€娴?/li>

# Ultralytics YOLO 馃殌, AGPL-3.0 license # YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect # Parameters nc: 80 # number of classes scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n' # [depth, width, max_channels] n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs # YOLO11n backbone backbone: # [from, repeats, module, args] - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 - [-1, 2, C3k2, [256, False, 0.25]] - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 - [-1, 2, C3k2, [512, False, 0.25]] - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 - [-1, 2, C3k2, [512, True]] - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 - [-1, 2, C3k2, [1024, True]] - [-1, 1, SPPF, [1024, 5]] # 9 - [-1, 2, C2PSA, [1024]] # 10 # YOLO11n head head: - [-1, 1, nn.Upsample, [None, 2, "nearest"]] - [[-1, 6], 1, Concat, [1]] # cat backbone P4 - [-1, 2, C3k2, [512, False]] # 13 - [-1, 1, nn.Upsample, [None, 2, "nearest"]] - [[-1, 4], 1, Concat, [1]] # cat backbone P3 - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small) - [-1, 1, Conv, [256, 3, 2]] - [[-1, 13], 1, Concat, [1]] # cat head P4 - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium) - [-1, 1, Conv, [512, 3, 2]] - [[-1, 10], 1, Concat, [1]] # cat head P5 - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large) - [-1, 1, SpatialGroupEnhance, [1024]] - [[16, 19, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)

璇箟鍒嗗壊

# Ultralytics YOLO 馃殌, AGPL-3.0 license # YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect # Parameters nc: 80 # number of classes scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n' # [depth, width, max_channels] n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs # YOLO11n backbone backbone: # [from, repeats, module, args] - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 - [-1, 2, C3k2, [256, False, 0.25]] - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 - [-1, 2, C3k2, [512, False, 0.25]] - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 - [-1, 2, C3k2, [512, True]] - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 - [-1, 2, C3k2, [1024, True]] - [-1, 1, SPPF, [1024, 5]] # 9 - [-1, 2, C2PSA, [1024]] # 10 # YOLO11n head head: - [-1, 1, nn.Upsample, [None, 2, "nearest"]] - [[-1, 6], 1, Concat, [1]] # cat backbone P4 - [-1, 2, C3k2, [512, False]] # 13 - [-1, 1, nn.Upsample, [None, 2, "nearest"]] - [[-1, 4], 1, Concat, [1]] # cat backbone P3 - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small) - [-1, 1, Conv, [256, 3, 2]] - [[-1, 13], 1, Concat, [1]] # cat head P4 - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium) - [-1, 1, Conv, [512, 3, 2]] - [[-1, 10], 1, Concat, [1]] # cat head P5 - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large) - [-1, 1, SpatialGroupEnhance, [1024]] - [[16, 19, 23], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)

鏃嬭浆鐩爣妫€娴嬄?/li>

# Ultralytics YOLO 馃殌, AGPL-3.0 license # YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect # Parameters nc: 80 # number of classes scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n' # [depth, width, max_channels] n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs # YOLO11n backbone backbone: # [from, repeats, module, args] - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4 - [-1, 2, C3k2, [256, False, 0.25]] - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8 - [-1, 2, C3k2, [512, False, 0.25]] - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16 - [-1, 2, C3k2, [512, True]] - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32 - [-1, 2, C3k2, [1024, True]] - [-1, 1, SPPF, [1024, 5]] # 9 - [-1, 2, C2PSA, [1024]] # 10 # YOLO11n head head: - [-1, 1, nn.Upsample, [None, 2, "nearest"]] - [[-1, 6], 1, Concat, [1]] # cat backbone P4 - [-1, 2, C3k2, [512, False]] # 13 - [-1, 1, nn.Upsample, [None, 2, "nearest"]] - [[-1, 4], 1, Concat, [1]] # cat backbone P3 - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small) - [-1, 1, Conv, [256, 3, 2]] - [[-1, 13], 1, Concat, [1]] # cat head P4 - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium) - [-1, 1, Conv, [512, 3, 2]] - [[-1, 10], 1, Concat, [1]] # cat head P5 - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large) - [-1, 1, SpatialGroupEnhance, [1024]] - [[16, 19, 23], 1, OBB, [nc, 1]] # Detect(P3, P4, P5)

娓╅Θ鎻愮ず锛氭湰鏂囧彧鏄yolo11鍩虹涓婃坊鍔犳ā鍧楋紝濡傛灉瑕佸yolo11n/l/m/x杩涜娣诲姞鍒欏彧闇€瑕佹寚瀹氬搴旂殑depth_multiple 鍜?width_multiple銆偮?/p>

# YOLO11n depth_multiple: 0.50 # model depth multiple width_multiple: 0.25 # layer channel multiple max_channel锛?024 # YOLO11s depth_multiple: 0.50 # model depth multiple width_multiple: 0.50 # layer channel multiple max_channel锛?024 # YOLO11m depth_multiple: 0.50 # model depth multiple width_multiple: 1.00 # layer channel multiple max_channel锛?12 # YOLO11l depth_multiple: 1.00 # model depth multiple width_multiple: 1.00 # layer channel multiple max_channel锛?12 # YOLO11x depth_multiple: 1.00 # model depth multiple width_multiple: 1.50 # layer channel multiple max_channel锛?12

2.4 娉ㄥ唽妯″潡

鍏抽敭姝ラ鍥涳細鍦╬arse_model鍑芥暟涓繘琛屾敞鍐岋紝鍦╬arse_model鍑芥暟涓坊鍔犱笅闈㈠唴瀹?/p>

鍏堝湪task.py瀵煎叆鍑芥暟

2.5 鎵ц绋嬪簭
鍦╰rain.py涓紝灏唌odel鐨勫弬鏁拌矾寰勮缃负yolo11_SGE.yaml鐨勮矾寰?/p>
寤鸿澶у鍐欑粷瀵硅矾寰勶紝纭繚涓€瀹氳兘鎵惧埌

from ultralytics import YOLO import warnings warnings.filterwarnings('ignore') from pathlib import Path if __name__ == '__main__': # 鍔犺浇妯″瀷 model = YOLO("ultralytics/cfg/11/yolo11.yaml") # 浣犺閫夋嫨鐨勬ā鍨媦aml鏂囦欢鍦板潃 # Use the model results = model.train(data=r"浣犵殑鏁版嵁闆嗙殑yaml鏂囦欢鍦板潃", epochs=100, batch=16, imgsz=640, workers=4, name=Path(model.cfg).stem) # 璁粌妯″瀷

馃殌杩愯绋嬪簭锛屽鏋滃嚭鐜颁笅闈㈢殑鍐呭鍒欒鏄庢坊鍔犳垚鍔燄煔€聽

from n params module arguments 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25] 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False] 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False] 17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] 19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False] 20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] 23 -1 1 512 ultralytics.nn.modules.block.SpatialGroupEnhance[256] 24 [16, 19, 22] 1 464912 ultralytics.nn.modules.head.Detect [80, [64, 128, 256]] YOLO11_SpatialGroupEnhance summary: 322 layers, 2,624,592 parameters, 2,624,576 gradients, 6.6 GFLOPs

3.淇敼鍚庣殑缃戠粶缁撴瀯鍥?/h2>
4. 瀹屾暣浠ｇ爜鍒嗕韩

杩欎釜鍚庢湡琛ュ厖鍚锛屽厛鎸夌収姝ラ鏉ュ嵆鍙?/strong>

5. GFLOPs

鍏充簬GFLOPs鐨勮绠楁柟寮忓彲浠ユ煡鐪?/strong>锛?/span>鐧鹃潰绠楁硶宸ョ▼甯?| 鍗风Н鍩虹鐭ヨ瘑鈥斺€擟onvolution

鏈敼杩涚殑YOLO11n GFLOPs

6. 杩涢樁
鍙互涓庡叾浠栫殑娉ㄦ剰鍔涙満鍒舵垨鑰呮崯澶卞嚱鏁扮瓑缁撳悎锛岃繘涓€姝ユ彁鍗囨娴嬫晥鏋?/p>
7.鎬荤粨

閫氳繃浠ヤ笂鐨勬敼杩涙柟娉曪紝鎴戜滑鎴愬姛鎻愬崌浜嗘ā鍨嬬殑琛ㄧ幇銆傝繖鍙槸涓€涓紑濮嬶紝鏈潵杩樻湁鏇村浼樺寲鍜屾妧鏈繁鎸栫殑绌洪棿銆傚湪杩欓噷锛屾垜鎯抽殕閲嶅悜澶у鎺ㄨ崘鎴戠殑涓撴爮鈥斺€?strong>銆奩OLO11鏀硅繘鏈夋晥娑ㄧ偣銆?/strong>銆傝繖涓笓鏍忎笓娉ㄤ簬鍓嶆部鐨勬繁搴﹀涔犳妧鏈紝鐗瑰埆鏄洰鏍囨娴嬮鍩熺殑鏈€鏂拌繘灞曪紝涓嶄粎鍖呭惈瀵筜OLO11鐨勬繁鍏ヨВ鏋愬拰鏀硅繘绛栫暐锛岃繕浼氬畾鏈熸洿鏂版潵鑷悇澶ч《浼氾紙濡侰VPR銆丯eurIPS绛夛級鐨勮鏂囧鐜板拰瀹炴垬鍒嗕韩銆?/p>
涓轰粈涔堣闃呮垜鐨勪笓鏍忥紵聽鈥斺€?strong>銆奩OLO11鏀硅繘鏈夋晥娑ㄧ偣銆?/strong>

鍓嶆部鎶€鏈В璇?/strong>锛氫笓鏍忎笉浠呴檺浜嶻OLO绯诲垪鐨勬敼杩涳紝杩樹細娑电洊鍚勭被涓绘祦涓庢柊鍏寸綉缁滅殑鏈€鏂扮爺绌舵垚鏋滐紝甯姪浣犵揣璺熸妧鏈疆娴併€?/p>

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