Git Product home page Git Product logo

yolov5's Introduction

Hi ๐Ÿ‘‹, I'm SpongeBab

A graduate student studying CV(computer vision).

spongebab spongebab

MyLover GitHub

  • ๐Ÿ”ญ I am currently in the THIRD year of my graduate.
  • ๐ŸŒฑ Iโ€™m currently learning CV.
  • ๐Ÿ‘จโ€๐Ÿ’ป All of my projects are available at here.
  • ๐Ÿ“ I regularly write articles on CSDN.
  • ๐Ÿ’ฌ Ask me about C/C++,Python.
  • ๐Ÿ“ซ How to reach me @me.

spongebab

Weekly Coding Status

yolov5's People

Contributors

aehogan avatar albinxavi avatar alexstoken avatar anon-artist avatar ayushexel avatar borda avatar cristifati avatar dependabot[bot] avatar developer0hye avatar fcakyon avatar glenn-jocher avatar imyhxy avatar jdfr avatar jebastin-nadar avatar kalenmike avatar kinoute avatar laughing-q avatar lorenzomammana avatar lornatang avatar nanocode012 avatar nauchtanrobotics avatar olehb avatar skalskip avatar taoxiesz avatar tkianai avatar wanghaoyang0106 avatar yeric1789 avatar yxnong avatar zhiqwang avatar zldrobit avatar

Stargazers

avatar

yolov5's Issues

bifpn็š„ๆ”นๅŠจ

Search before asking

Question

bifpn็š„ๆ”นๅŠจๅฏไปฅๆ”นๅœจ sใ€n็š„็‰ˆๆœฌไธŠ้ขๅ—๏ผŸ

Additional

็›ฎๅ‰ๆˆ‘็œ‹ๅˆฐ็š„ๆ”นๅŠจ้ƒฝๆ˜ฏๅœจ xใ€l่ฟ™็งๅคง็‰ˆๆœฌไธŠ้ข็š„ใ€‚

yolov5m-four

๐Ÿš€ 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%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 50/50 [00:00<?, ?it/s]
               Class     Images     Labels          P          R     [email protected] [email protected]:.95: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 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%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 50/50 [00:00<?, ?it/s]
               Class     Images     Labels          P          R     [email protected] [email protected]:.95: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 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.

yolov5-four-PANet

๐Ÿš€ Feature

yolov5-PANet-fourhead

# Parameters
nc: 80  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # 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

#  - [ 5,7, 8,12, 15,13 ]  # P2/4  # custom
#  - [ 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, BottleneckCSP, [ 128 ] ],
    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
    [ -1, 9, BottleneckCSP, [ 256 ] ],
    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
    [ -1, 9, BottleneckCSP, [ 512 ] ],
    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 7-P5/32
    [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
    [ -1, 3, BottleneckCSP, [ 1024, False ] ],  # 9
  ]

# YOLOv5 PANet 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, BottleneckCSP, [ 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, BottleneckCSP, [ 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, 3, BottleneckCSP, [ 128, False ] ],  # 17 (P2/4-small)

    [ -1, 1, Conv, [ 128, 3, 2 ] ],
    [ [ -1, 18 ], 1, Concat, [ 1 ] ],  # cat head P3
    [ -1, 3, BottleneckCSP, [ 256, False ] ],  # 17 (P3/8-small)

    [ -1, 1, Conv, [ 256, 3, 2 ] ],
    [ [ -1, 14 ], 1, Concat, [ 1 ] ],  # cat head P4
    [ -1, 3, BottleneckCSP, [ 512, False ] ],  # 20 (P4/16-medium)

    [ -1, 1, Conv, [ 512, 3, 2 ] ],
    [ [ -1, 10 ], 1, Concat, [ 1 ] ],  # cat head P5
    [ -1, 3, BottleneckCSP, [ 1024, False ] ],  # 23 (P5/32-large)

    [ [ 14, 17, 20, 23 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5)
  ]

@glenn-jocher Can you help me with this?yolov5-panet four head(p5).I modified the network as the format of P6. But AP is not normal. What went wrong?

     Epoch   gpu_mem       box       obj       cls     total    labels  img_size
   297/299     3.22G   0.01925    0.0256         0   0.04485       133       640: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 117/117 [00:35<00:00,  3.30it/s]
               Class     Images     Labels          P          R     [email protected] [email protected]:.95: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 9/9 [00:01<00:00,  5.17it/s]
                 all         50       1532     0.0064     0.0627   0.000436    4.9e-05

     Epoch   gpu_mem       box       obj       cls     total    labels  img_size
   298/299     3.22G   0.01946   0.02482         0   0.04428        87       640: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 117/117 [00:35<00:00,  3.28it/s]
               Class     Images     Labels          P          R     [email protected] [email protected]:.95: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 9/9 [00:01<00:00,  5.09it/s]
                 all         50       1532     0.0066     0.0646   0.000461   5.19e-05

     Epoch   gpu_mem       box       obj       cls     total    labels  img_size
   299/299     3.22G   0.01916   0.02602         0   0.04517        75       640: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 117/117 [00:35<00:00,  3.31it/s]
               Class     Images     Labels          P          R     [email protected] [email protected]:.95: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 9/9 [00:02<00:00,  3.40it/s]
                 all         50       1532    0.00629     0.0614    0.00042   4.61e-05

yolov5s-four

๐Ÿš€ Feature

yolov5s-four head.

yaml:

# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # 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)
  ]

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.