WIP. PRs are welcome

Port of Darknet YOLO, but via Gorgonia Both YOLOv3 and tiny-YOLOv3 are implemented.

Usage

Navigate to example/yolo-v3 folder and run main.go.

Available flags

go run main.go -h

  -cfg string
        Path to net configuration file (default "../../test_network_data/yolov3-tiny.cfg")
  -image string
        Path to image file for 'detector' mode (default "../../test_network_data/dog_416x416.jpg")
  -mode string
        Choose the mode: detector/training (default "detector")
  -train string
        Path to folder with labeled data (default "../../test_yolo_op_data")
  -weights string
        Path to weights file (default "../../test_network_data/yolov3-tiny.weights")

For testing tiny-yolov3:

go run main.go --mode detector --cfg ../../test_network_data/yolov3-tiny.cfg --weights ../../test_network_data/yolov3-tiny.weights --image ../../test_network_data/dog_416x416.jpg

For testing yolov3:

go run main.go --mode detector --cfg ../../test_network_data/yolov3.cfg --weights ../../test_network_data/yolov3.weights --image ../../test_network_data/dog_416x416.jpg

For training WIP. PRs are welcome:

go run main.go --mode training --cfg ../../test_network_data/yolov3-tiny.cfg --weights ../../test_network_data/yolov3-tiny.weights --image ../../test_network_data/dog_416x416.jpg --train ../../test_yolo_op_data

Weights and configuration

Weights can be downloaded via curl-scripts download_weights_yolo_v3.sh and download_weights_yolo_tiny_v3.sh. Configuration files: yolov3-tiny.cfg and yolov3.cfg

Network Architecture

Tiny-YOLOv3 Architecture is:

Convolution layer: Filters->16 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Maxpooling layer: Size->2 Stride->2
Convolution layer: Filters->32 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Maxpooling layer: Size->2 Stride->2
Convolution layer: Filters->64 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Maxpooling layer: Size->2 Stride->2
Convolution layer: Filters->128 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Maxpooling layer: Size->2 Stride->2
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Maxpooling layer: Size->2 Stride->2
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Maxpooling layer: Size->2 Stride->1
Convolution layer: Filters->1024 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->255 Padding->0 Kernel->1x1 Stride->1 Activation->linear Batch->0 Bias->true
YOLO layer: Mask->3 Anchors->[81, 82]   |       Mask->4 Anchors->[135, 169]     |       Mask->5 Anchors->[344, 319]
Route layer: Start->13
Convolution layer: Filters->128 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Upsample layer: Scale->2
Route layer: Start->19 End->8
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->255 Padding->0 Kernel->1x1 Stride->1 Activation->linear Batch->0 Bias->true
YOLO layer: Mask->0 Anchors->[10, 14]   |       Mask->1 Anchors->[23, 27]       |       Mask->2 Anchors->[37, 58]

YOLOv3 Architecture is:

Convolution layer: Filters->32 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->64 Padding->1 Kernel->3x3 Stride->2 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->32 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->64 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->1 | index to->3
Convolution layer: Filters->128 Padding->1 Kernel->3x3 Stride->2 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->64 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->128 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->5 | index to->7
Convolution layer: Filters->64 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->128 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->8 | index to->10
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->2 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->128 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->12 | index to->14
Convolution layer: Filters->128 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->15 | index to->17
Convolution layer: Filters->128 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->18 | index to->20
Convolution layer: Filters->128 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->21 | index to->23
Convolution layer: Filters->128 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->24 | index to->26
Convolution layer: Filters->128 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->27 | index to->29
Convolution layer: Filters->128 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->30 | index to->32
Convolution layer: Filters->128 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->33 | index to->35
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->2 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->37 | index to->39
Convolution layer: Filters->256 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->40 | index to->42
Convolution layer: Filters->256 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->43 | index to->45
Convolution layer: Filters->256 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->46 | index to->48
Convolution layer: Filters->256 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->49 | index to->51
Convolution layer: Filters->256 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->52 | index to->54
Convolution layer: Filters->256 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->55 | index to->57
Convolution layer: Filters->256 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->58 | index to->60
Convolution layer: Filters->1024 Padding->1 Kernel->3x3 Stride->2 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->1024 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->62 | index to->64
Convolution layer: Filters->512 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->1024 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->65 | index to->67
Convolution layer: Filters->512 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->1024 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->68 | index to->70
Convolution layer: Filters->512 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->1024 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Shortcut layer: index from->71 | index to->73
Convolution layer: Filters->512 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->1024 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->1024 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->1024 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->255 Padding->0 Kernel->1x1 Stride->1 Activation->linear Batch->0 Bias->true
YOLO layer: Mask->6 Anchors->[116, 90]  |       Mask->7 Anchors->[156, 198]     |       Mask->8 Anchors->[373, 326]
Route layer: Start->79
Convolution layer: Filters->256 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Upsample layer: Scale->2
Route layer: Start->85 End->61
Convolution layer: Filters->256 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->512 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->255 Padding->0 Kernel->1x1 Stride->1 Activation->linear Batch->0 Bias->true
YOLO layer: Mask->3 Anchors->[30, 61]   |       Mask->4 Anchors->[62, 45]       |       Mask->5 Anchors->[59, 119]
Route layer: Start->91
Convolution layer: Filters->128 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Upsample layer: Scale->2
Route layer: Start->97 End->36
Convolution layer: Filters->128 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->128 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->128 Padding->0 Kernel->1x1 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->256 Padding->1 Kernel->3x3 Stride->1 Activation->leaky Batch->1 Bias->false
Convolution layer: Filters->255 Padding->0 Kernel->1x1 Stride->1 Activation->linear Batch->0 Bias->true
YOLO layer: Mask->0 Anchors->[10, 13]   |       Mask->1 Anchors->[16, 30]       |       Mask->2 Anchors->[33, 23]

Name		Name	Last commit message	Last commit date
Latest commit History 32 Commits
.github/ISSUE_TEMPLATE		.github/ISSUE_TEMPLATE
example/yolo-v3		example/yolo-v3
test_network_data		test_network_data
test_yolo_op_data		test_yolo_op_data
.gitignore		.gitignore
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
CONTRIBUTORS.md		CONTRIBUTORS.md
LICENSE		LICENSE
README.md		README.md
ROADMAP.md		ROADMAP.md
conv_layer.go		conv_layer.go
detections.go		detections.go
go.mod		go.mod
go.sum		go.sum
layer.go		layer.go
max_pooling_layer.go		max_pooling_layer.go
route_layer.go		route_layer.go
shortcut_layer.go		shortcut_layer.go
upsample_layer.go		upsample_layer.go
upsample_op.go		upsample_op.go
utils.go		utils.go
weights_darknet.go		weights_darknet.go
yolo_diff_op.go		yolo_diff_op.go
yolo_layer.go		yolo_layer.go
yolo_op.go		yolo_op.go
yolo_op_test.go		yolo_op_test.go
yolo_trainer.go		yolo_trainer.go
yolov3.go		yolov3.go

License

LdDl/yolo-go

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WIP. PRs are welcome

Usage

Weights and configuration

Network Architecture

Tiny-YOLOv3 Architecture is:

YOLOv3 Architecture is:

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