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YOLOv7 tiny 新增小目标检测层
作者：Gausst松鼠会 | 2024-05-16 13:50:59
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YOLOv7 tiny 新增小目标检测层
YOLOv7 tiny 新增小目标检测层
根据已有的结构进行新增小目标层，，个人理解，仅供参考！！！
修改yolov7-tiny.yaml文件
（1）修改nc 自己数据集类别数；
（2）设置anchors 4 #自动调用autoanchor.py
（3）新增 ###模块
（4）修改[[92,93,94,95], 1, IDetect, [nc, anchors]], # Detect(P2,P3, P4, P5)
# parameters nc: 5 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: 4 # - [10,13, 16,30, 33,23] # P3/8 # - [30,61, 62,45, 59,119] # P4/16 # - [116,90, 156,198, 373,326] # P5/32 # yolov7-tiny backbone backbone: # [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True [[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 0-P1/2 [-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 1-P2/4 [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 7 [-1, 1, MP, []], # 8-P3/8 [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 14 [-1, 1, MP, []], # 15-P4/16 [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 21 [-1, 1, MP, []], # 22-P5/32 [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 28 ] # yolov7-tiny head head: [[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, SP, [5]], [-2, 1, SP, [9]], [-3, 1, SP, [13]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -7], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 37 [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [21, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4 [[-1, -2], 1, Concat, [1]], [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 47 [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [14, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3 [[-1, -2], 1, Concat, [1]], ########################## # ELAN [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 57 # end ELAN # CBL [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # #UP [-1, 1, nn.Upsample, [None, 2, 'nearest']], # # backbone CBL [7, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4 # #Concat [[-1, -2], 1, Concat, [1]], # #ELAN [-1, 1, Conv, [16, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [16, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [16, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [16, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 67 x-small head # #CBL [-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]], [[-1, 57], 1, Concat, [1]], # ############################### [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 75 small head [-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]], [[-1, 47], 1, Concat, [1]], [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 83 middle head [-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]], [[-1, 37], 1, Concat, [1]], [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 91 large head [67, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [75, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [83, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [91, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[92,93,94,95], 1, IDetect, [nc, anchors]], # Detect(P2,P3, P4, P5) ]
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YOLOv7 tiny 结构图
调用 models/yolo.py验证
python models/yolo.py --cfg cfg\training\yolov7-tiny.yaml #修改过的yaml路径
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YOLOR 2023-3-4 torch 1.12.1+cu113 CUDA:0 (NVIDIA RTX A4000, 16375.5MB) from n params module arguments 0 -1 1 928 models.common.Conv [3, 32, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)] 1 -1 1 18560 models.common.Conv [32, 64, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)] 2 -1 1 2112 models.common.Conv [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 3 -2 1 2112 models.common.Conv [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 4 -1 1 9280 models.common.Conv [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 5 -1 1 9280 models.common.Conv [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 6 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 7 -1 1 8320 models.common.Conv [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 8 -1 1 0 models.common.MP [] 9 -1 1 4224 models.common.Conv [64, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 10 -2 1 4224 models.common.Conv [64, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 11 -1 1 36992 models.common.Conv [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 12 -1 1 36992 models.common.Conv [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 13 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 14 -1 1 33024 models.common.Conv [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 15 -1 1 0 models.common.MP [] 16 -1 1 16640 models.common.Conv [128, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 17 -2 1 16640 models.common.Conv [128, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 18 -1 1 147712 models.common.Conv [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 19 -1 1 147712 models.common.Conv [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 20 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 21 -1 1 131584 models.common.Conv [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 22 -1 1 0 models.common.MP [] 23 -1 1 66048 models.common.Conv [256, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 24 -2 1 66048 models.common.Conv [256, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 25 -1 1 590336 models.common.Conv [256, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 26 -1 1 590336 models.common.Conv [256, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 27 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 28 -1 1 525312 models.common.Conv [1024, 512, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 29 -1 1 131584 models.common.Conv [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 30 -2 1 131584 models.common.Conv [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 31 -1 1 0 models.common.SP [5] 32 -2 1 0 models.common.SP [9] 33 -3 1 0 models.common.SP [13] 34 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 35 -1 1 262656 models.common.Conv [1024, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 36 [-1, -7] 1 0 models.common.Concat [1] 37 -1 1 131584 models.common.Conv [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 38 -1 1 33024 models.common.Conv [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 39 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 40 21 1 33024 models.common.Conv [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 41 [-1, -2] 1 0 models.common.Concat [1] 42 -1 1 16512 models.common.Conv [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 43 -2 1 16512 models.common.Conv [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 44 -1 1 36992 models.common.Conv [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 45 -1 1 36992 models.common.Conv [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 46 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 47 -1 1 33024 models.common.Conv [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 48 -1 1 8320 models.common.Conv [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 49 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 50 14 1 8320 models.common.Conv [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 51 [-1, -2] 1 0 models.common.Concat [1] 52 -1 1 4160 models.common.Conv [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 53 -2 1 4160 models.common.Conv [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 54 -1 1 9280 models.common.Conv [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 55 -1 1 9280 models.common.Conv [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 56 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 57 -1 1 8320 models.common.Conv [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 58 -1 1 2112 models.common.Conv [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 59 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 60 7 1 2112 models.common.Conv [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 61 [-1, -2] 1 0 models.common.Concat [1] 62 -1 1 1056 models.common.Conv [64, 16, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 63 -2 1 1056 models.common.Conv [64, 16, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 64 -1 1 2336 models.common.Conv [16, 16, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 65 -1 1 2336 models.common.Conv [16, 16, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 66 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 67 -1 1 2112 models.common.Conv [64, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 68 -1 1 18560 models.common.Conv [32, 64, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)] 69 [-1, 57] 1 0 models.common.Concat [1] 70 -1 1 4160 models.common.Conv [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 71 -2 1 4160 models.common.Conv [128, 32, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 72 -1 1 9280 models.common.Conv [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 73 -1 1 9280 models.common.Conv [32, 32, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 74 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 75 -1 1 8320 models.common.Conv [128, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 76 -1 1 73984 models.common.Conv [64, 128, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)] 77 [-1, 47] 1 0 models.common.Concat [1] 78 -1 1 16512 models.common.Conv [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 79 -2 1 16512 models.common.Conv [256, 64, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 80 -1 1 36992 models.common.Conv [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 81 -1 1 36992 models.common.Conv [64, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 82 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 83 -1 1 33024 models.common.Conv [256, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 84 -1 1 295424 models.common.Conv [128, 256, 3, 2, None, 1, LeakyReLU(negative_slope=0.1)] 85 [-1, 37] 1 0 models.common.Concat [1] 86 -1 1 65792 models.common.Conv [512, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 87 -2 1 65792 models.common.Conv [512, 128, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 88 -1 1 147712 models.common.Conv [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 89 -1 1 147712 models.common.Conv [128, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 90 [-1, -2, -3, -4] 1 0 models.common.Concat [1] 91 -1 1 131584 models.common.Conv [512, 256, 1, 1, None, 1, LeakyReLU(negative_slope=0.1)] 92 67 1 18560 models.common.Conv [32, 64, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 93 75 1 73984 models.common.Conv [64, 128, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 94 83 1 295424 models.common.Conv [128, 256, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 95 91 1 1180672 models.common.Conv [256, 512, 3, 1, None, 1, LeakyReLU(negative_slope=0.1)] 96 [92, 93, 94, 95] 1 39680 IDetect [5, [[0, 1, 2, 3, 4, 5, 6, 7], [0, 1, 2, 3, 4, 5, 6, 7], [0, 1, 2, 3, 4, 5, 6, 7], [0, 1, 2, 3, 4, 5, 6, 7]], [64, 128, 256, 512]] D:\Anaconda3\envs\yolov8\lib\site-packages\torch\functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\TensorShape.cpp:2895.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] Model Summary: 327 layers, 6122976 parameters, 6122976 gradients, 15.6 GFLOPS
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