https://github.com/lddl/yolo-go
Port of Darknet YOLO for Go ecosystem
https://github.com/lddl/yolo-go
computer-vision darknet data-science hacktoberfest machine-learning neural-network object-detection yolo-darknet yolov3 yolov3-tiny
Last synced: 3 months ago
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Port of Darknet YOLO for Go ecosystem
- Host: GitHub
- URL: https://github.com/lddl/yolo-go
- Owner: LdDl
- License: apache-2.0
- Created: 2020-10-06T14:33:34.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-12-21T14:01:58.000Z (almost 5 years ago)
- Last Synced: 2025-04-05T15:51:10.446Z (6 months ago)
- Topics: computer-vision, darknet, data-science, hacktoberfest, machine-learning, neural-network, object-detection, yolo-darknet, yolov3, yolov3-tiny
- Language: Go
- Homepage:
- Size: 6.42 MB
- Stars: 29
- Watchers: 3
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# WIP. PRs are welcome
Port of [Darknet YOLO](https://github.com/pjreddie/darknet#darknet), but via [Gorgonia](https://github.com/gorgonia/gorgonia)
Both YOLOv3 and tiny-YOLOv3 are implemented.
# Usage
Navigate to [example/yolo-v3](example/yolo-v3) folder and run [main.go](example/yolo-v3/main.go).
Available flags
```go
go run main.go -h
```
```shell
-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:
```shell
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:
```shell
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**:
```shell
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](test_network_data/download_weights_yolo_v3.sh) and [download_weights_yolo_tiny_v3.sh](test_network_data/download_weights_yolo_tiny_v3.sh).
Configuration files: [yolov3-tiny.cfg](test_network_data/yolov3-tiny.cfg) and [yolov3.cfg](test_network_data/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]
```