yolov5m-four

[SpongeBab]

🚀 Feature

yolov5-m-four

# Parameters
nc: 80  # number of classes
depth_multiple: 0.67  # model depth multiple
width_multiple: 0.75  # layer channel multiple
anchors: 3
#  - [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
backbone:
  # [from, number, module, args]
  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 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, 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
  ]

# YOLOv5 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, [ 128, 1, 1 ] ],
    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
    [ [ -1, 2 ], 1, Concat, [ 1 ] ],  # cat backbone P2
    [ -1, 1, C3, [ 128, False ] ],  # 21 (P2/4-xsmall)

    [ -1, 1, Conv, [ 128, 3, 2 ] ],
    [ [ -1, 18 ], 1, Concat, [ 1 ] ],  # cat head P3
    [ -1, 3, C3, [ 256, False ] ],  # 24 (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)

    [ [ 21, 24, 27, 30 ], 1, Detect, [ nc, anchors ] ],  # Detect(P2, P3, P4, P5)
  ]

---

result:
yolov5m:

val: Scanning '..\Dataset\desk_3_400\valid.cache' images and labels... 50 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 50/50 [00:00<?, ?it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100%|██████████| 25/25 [00:04<00:00,  6.17it/s]
                 all         50       1532      0.994      0.964      0.988      0.764
Speed: 0.4ms pre-process, 20.2ms inference, 2.7ms NMS per image at shape (2, 3, 640, 640)
Results saved to runs\test\desk-400-m-640-300-bs3-bs2

mAp: 0.764, latency: 23.3ms

yolov5m-four:

val: Scanning '..\Dataset\desk_3_400\valid.cache' images and labels... 50 found, 0 missing, 0 empty, 0 corrupted: 100%|██████████| 50/50 [00:00<?, ?it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100%|██████████| 25/25 [00:04<00:00,  5.25it/s]
                 all         50       1532      0.989      0.945      0.982      0.745
Speed: 0.4ms pre-process, 27.6ms inference, 3.2ms NMS per image at shape (2, 3, 640, 640)
Results saved to runs\test\desk-400-m-four-640-300-bs2-bs2

mAP: 0.745, latency: 31.2ms

yolov5m: mAp: 0.764, latency: 23.3ms
yolov5m-four: mAP: 0.745, latency: 31.2ms

I think the reason for the lower mAP is batch-size.

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