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40
ultralytics/cfg/models/README.md
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40
ultralytics/cfg/models/README.md
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## Models
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|
||||
Welcome to the Ultralytics Models directory! Here you will find a wide variety of pre-configured model configuration files (`*.yaml`s) that can be used to create custom YOLO models. The models in this directory have been expertly crafted and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image segmentation tasks.
|
||||
|
||||
These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like instance segmentation and object tracking. They are also designed to run efficiently on a variety of hardware platforms, from CPUs to GPUs. Whether you are a seasoned machine learning practitioner or just getting started with YOLO, this directory provides a great starting point for your custom model development needs.
|
||||
|
||||
To get started, simply browse through the models in this directory and find one that best suits your needs. Once you've selected a model, you can use the provided `*.yaml` file to train and deploy your custom YOLO model with ease. See full details at the Ultralytics [Docs](https://docs.ultralytics.com/models), and if you need help or have any questions, feel free to reach out to the Ultralytics team for support. So, don't wait, start creating your custom YOLO model now!
|
||||
|
||||
### Usage
|
||||
|
||||
Model `*.yaml` files may be used directly in the Command Line Interface (CLI) with a `yolo` command:
|
||||
|
||||
```bash
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yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
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```
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||||
|
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They may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
|
||||
|
||||
```python
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from ultralytics import YOLO
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model = YOLO("model.yaml") # build a YOLOv8n model from scratch
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# YOLO("model.pt") use pre-trained model if available
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model.info() # display model information
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model.train(data="coco128.yaml", epochs=100) # train the model
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||||
```
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||||
|
||||
## Pre-trained Model Architectures
|
||||
|
||||
Ultralytics supports many model architectures. Visit https://docs.ultralytics.com/models to view detailed information and usage. Any of these models can be used by loading their configs or pretrained checkpoints if available.
|
||||
|
||||
## Contribute New Models
|
||||
|
||||
Have you trained a new YOLO variant or achieved state-of-the-art performance with specific tuning? We'd love to showcase your work in our Models section! Contributions from the community in the form of new models, architectures, or optimizations are highly valued and can significantly enrich our repository.
|
||||
|
||||
By contributing to this section, you're helping us offer a wider array of model choices and configurations to the community. It's a fantastic way to share your knowledge and expertise while making the Ultralytics YOLO ecosystem even more versatile.
|
||||
|
||||
To get started, please consult our [Contributing Guide](https://docs.ultralytics.com/help/contributing) for step-by-step instructions on how to submit a Pull Request (PR) 🛠️. Your contributions are eagerly awaited!
|
||||
|
||||
Let's join hands to extend the range and capabilities of the Ultralytics YOLO models 🙏!
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50
ultralytics/cfg/models/rt-detr/rtdetr-l.yaml
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50
ultralytics/cfg/models/rt-detr/rtdetr-l.yaml
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|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# RT-DETR-l object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
l: [1.00, 1.00, 1024]
|
||||
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, HGStem, [32, 48]] # 0-P2/4
|
||||
- [-1, 6, HGBlock, [48, 128, 3]] # stage 1
|
||||
|
||||
- [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
|
||||
- [-1, 6, HGBlock, [96, 512, 3]] # stage 2
|
||||
|
||||
- [-1, 1, DWConv, [512, 3, 2, 1, False]] # 4-P3/16
|
||||
- [-1, 6, HGBlock, [192, 1024, 5, True, False]] # cm, c2, k, light, shortcut
|
||||
- [-1, 6, HGBlock, [192, 1024, 5, True, True]]
|
||||
- [-1, 6, HGBlock, [192, 1024, 5, True, True]] # stage 3
|
||||
|
||||
- [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 8-P4/32
|
||||
- [-1, 6, HGBlock, [384, 2048, 5, True, False]] # stage 4
|
||||
|
||||
head:
|
||||
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 10 input_proj.2
|
||||
- [-1, 1, AIFI, [1024, 8]]
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 12, Y5, lateral_convs.0
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [7, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14 input_proj.1
|
||||
- [[-2, -1], 1, Concat, [1]]
|
||||
- [-1, 3, RepC3, [256]] # 16, fpn_blocks.0
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 17, Y4, lateral_convs.1
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 19 input_proj.0
|
||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, RepC3, [256]] # X3 (21), fpn_blocks.1
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0
|
||||
- [[-1, 17], 1, Concat, [1]] # cat Y4
|
||||
- [-1, 3, RepC3, [256]] # F4 (24), pan_blocks.0
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 25, downsample_convs.1
|
||||
- [[-1, 12], 1, Concat, [1]] # cat Y5
|
||||
- [-1, 3, RepC3, [256]] # F5 (27), pan_blocks.1
|
||||
|
||||
- [[21, 24, 27], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||
42
ultralytics/cfg/models/rt-detr/rtdetr-resnet101.yaml
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42
ultralytics/cfg/models/rt-detr/rtdetr-resnet101.yaml
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|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# RT-DETR-ResNet101 object detection model with P3-P5 outputs.
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
l: [1.00, 1.00, 1024]
|
||||
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
|
||||
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
|
||||
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
|
||||
- [-1, 1, ResNetLayer, [512, 256, 2, False, 23]] # 3
|
||||
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
|
||||
|
||||
head:
|
||||
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
|
||||
- [-1, 1, AIFI, [1024, 8]]
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 7
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
|
||||
- [[-2, -1], 1, Concat, [1]]
|
||||
- [-1, 3, RepC3, [256]] # 11
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
|
||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
|
||||
- [[-1, 12], 1, Concat, [1]] # cat Y4
|
||||
- [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
|
||||
- [[-1, 7], 1, Concat, [1]] # cat Y5
|
||||
- [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
|
||||
|
||||
- [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||
42
ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml
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42
ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml
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|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# RT-DETR-ResNet50 object detection model with P3-P5 outputs.
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
l: [1.00, 1.00, 1024]
|
||||
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
|
||||
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
|
||||
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
|
||||
- [-1, 1, ResNetLayer, [512, 256, 2, False, 6]] # 3
|
||||
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
|
||||
|
||||
head:
|
||||
- [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
|
||||
- [-1, 1, AIFI, [1024, 8]]
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 7
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
|
||||
- [[-2, -1], 1, Concat, [1]]
|
||||
- [-1, 3, RepC3, [256]] # 11
|
||||
- [-1, 1, Conv, [256, 1, 1]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
|
||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
|
||||
- [[-1, 12], 1, Concat, [1]] # cat Y4
|
||||
- [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
|
||||
- [[-1, 7], 1, Concat, [1]] # cat Y5
|
||||
- [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
|
||||
|
||||
- [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||
54
ultralytics/cfg/models/rt-detr/rtdetr-x.yaml
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54
ultralytics/cfg/models/rt-detr/rtdetr-x.yaml
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|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# RT-DETR-x object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/rtdetr
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
x: [1.00, 1.00, 2048]
|
||||
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, HGStem, [32, 64]] # 0-P2/4
|
||||
- [-1, 6, HGBlock, [64, 128, 3]] # stage 1
|
||||
|
||||
- [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
|
||||
- [-1, 6, HGBlock, [128, 512, 3]]
|
||||
- [-1, 6, HGBlock, [128, 512, 3, False, True]] # 4-stage 2
|
||||
|
||||
- [-1, 1, DWConv, [512, 3, 2, 1, False]] # 5-P3/16
|
||||
- [-1, 6, HGBlock, [256, 1024, 5, True, False]] # cm, c2, k, light, shortcut
|
||||
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
|
||||
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
|
||||
- [-1, 6, HGBlock, [256, 1024, 5, True, True]]
|
||||
- [-1, 6, HGBlock, [256, 1024, 5, True, True]] # 10-stage 3
|
||||
|
||||
- [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 11-P4/32
|
||||
- [-1, 6, HGBlock, [512, 2048, 5, True, False]]
|
||||
- [-1, 6, HGBlock, [512, 2048, 5, True, True]] # 13-stage 4
|
||||
|
||||
head:
|
||||
- [-1, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 14 input_proj.2
|
||||
- [-1, 1, AIFI, [2048, 8]]
|
||||
- [-1, 1, Conv, [384, 1, 1]] # 16, Y5, lateral_convs.0
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [10, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 18 input_proj.1
|
||||
- [[-2, -1], 1, Concat, [1]]
|
||||
- [-1, 3, RepC3, [384]] # 20, fpn_blocks.0
|
||||
- [-1, 1, Conv, [384, 1, 1]] # 21, Y4, lateral_convs.1
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [4, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 23 input_proj.0
|
||||
- [[-2, -1], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, RepC3, [384]] # X3 (25), fpn_blocks.1
|
||||
|
||||
- [-1, 1, Conv, [384, 3, 2]] # 26, downsample_convs.0
|
||||
- [[-1, 21], 1, Concat, [1]] # cat Y4
|
||||
- [-1, 3, RepC3, [384]] # F4 (28), pan_blocks.0
|
||||
|
||||
- [-1, 1, Conv, [384, 3, 2]] # 29, downsample_convs.1
|
||||
- [[-1, 16], 1, Concat, [1]] # cat Y5
|
||||
- [-1, 3, RepC3, [384]] # F5 (31), pan_blocks.1
|
||||
|
||||
- [[25, 28, 31], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||
40
ultralytics/cfg/models/v10/yolov10b.yaml
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40
ultralytics/cfg/models/v10/yolov10b.yaml
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@ -0,0 +1,40 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
b: [0.67, 1.00, 512]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
|
||||
40
ultralytics/cfg/models/v10/yolov10l.yaml
Normal file
40
ultralytics/cfg/models/v10/yolov10l.yaml
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@ -0,0 +1,40 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
|
||||
43
ultralytics/cfg/models/v10/yolov10m.yaml
Normal file
43
ultralytics/cfg/models/v10/yolov10m.yaml
Normal file
@ -0,0 +1,43 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
|
||||
40
ultralytics/cfg/models/v10/yolov10n.yaml
Normal file
40
ultralytics/cfg/models/v10/yolov10n.yaml
Normal file
@ -0,0 +1,40 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
|
||||
39
ultralytics/cfg/models/v10/yolov10s.yaml
Normal file
39
ultralytics/cfg/models/v10/yolov10s.yaml
Normal file
@ -0,0 +1,39 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
s: [0.33, 0.50, 1024]
|
||||
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
|
||||
40
ultralytics/cfg/models/v10/yolov10x.yaml
Normal file
40
ultralytics/cfg/models/v10/yolov10x.yaml
Normal file
@ -0,0 +1,40 @@
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
x: [1.00, 1.25, 512]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2fCIB, [512, True]]
|
||||
- [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2fCIB, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
- [-1, 1, PSA, [1024]] # 10
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 13
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 13], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, SCDown, [512, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
|
||||
|
||||
- [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
|
||||
46
ultralytics/cfg/models/v3/yolov3-spp.yaml
Normal file
46
ultralytics/cfg/models/v3/yolov3-spp.yaml
Normal file
@ -0,0 +1,46 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv3-SPP object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# darknet53 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
- [-1, 1, Conv, [32, 3, 1]] # 0
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
|
||||
- [-1, 1, Bottleneck, [64]]
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
|
||||
- [-1, 2, Bottleneck, [128]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
|
||||
- [-1, 8, Bottleneck, [256]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
|
||||
- [-1, 8, Bottleneck, [512]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
|
||||
- [-1, 4, Bottleneck, [1024]] # 10
|
||||
|
||||
# YOLOv3-SPP head
|
||||
head:
|
||||
- [-1, 1, Bottleneck, [1024, False]]
|
||||
- [-1, 1, SPP, [512, [5, 9, 13]]]
|
||||
- [-1, 1, Conv, [1024, 3, 1]]
|
||||
- [-1, 1, Conv, [512, 1, 1]]
|
||||
- [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
|
||||
|
||||
- [-2, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 1, Bottleneck, [512, False]]
|
||||
- [-1, 1, Bottleneck, [512, False]]
|
||||
- [-1, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
|
||||
|
||||
- [-2, 1, Conv, [128, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 1, Bottleneck, [256, False]]
|
||||
- [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
|
||||
|
||||
- [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
37
ultralytics/cfg/models/v3/yolov3-tiny.yaml
Normal file
37
ultralytics/cfg/models/v3/yolov3-tiny.yaml
Normal file
@ -0,0 +1,37 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv3-tiny object detection model with P4-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# YOLOv3-tiny backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
- [-1, 1, Conv, [16, 3, 1]] # 0
|
||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 1-P1/2
|
||||
- [-1, 1, Conv, [32, 3, 1]]
|
||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 3-P2/4
|
||||
- [-1, 1, Conv, [64, 3, 1]]
|
||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 5-P3/8
|
||||
- [-1, 1, Conv, [128, 3, 1]]
|
||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 7-P4/16
|
||||
- [-1, 1, Conv, [256, 3, 1]]
|
||||
- [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 9-P5/32
|
||||
- [-1, 1, Conv, [512, 3, 1]]
|
||||
- [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]] # 11
|
||||
- [-1, 1, nn.MaxPool2d, [2, 1, 0]] # 12
|
||||
|
||||
# YOLOv3-tiny head
|
||||
head:
|
||||
- [-1, 1, Conv, [1024, 3, 1]]
|
||||
- [-1, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, Conv, [512, 3, 1]] # 15 (P5/32-large)
|
||||
|
||||
- [-2, 1, Conv, [128, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 1, Conv, [256, 3, 1]] # 19 (P4/16-medium)
|
||||
|
||||
- [[19, 15], 1, Detect, [nc]] # Detect(P4, P5)
|
||||
46
ultralytics/cfg/models/v3/yolov3.yaml
Normal file
46
ultralytics/cfg/models/v3/yolov3.yaml
Normal file
@ -0,0 +1,46 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv3 object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 1.0 # model depth multiple
|
||||
width_multiple: 1.0 # layer channel multiple
|
||||
|
||||
# darknet53 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
- [-1, 1, Conv, [32, 3, 1]] # 0
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
|
||||
- [-1, 1, Bottleneck, [64]]
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
|
||||
- [-1, 2, Bottleneck, [128]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
|
||||
- [-1, 8, Bottleneck, [256]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
|
||||
- [-1, 8, Bottleneck, [512]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
|
||||
- [-1, 4, Bottleneck, [1024]] # 10
|
||||
|
||||
# YOLOv3 head
|
||||
head:
|
||||
- [-1, 1, Bottleneck, [1024, False]]
|
||||
- [-1, 1, Conv, [512, 1, 1]]
|
||||
- [-1, 1, Conv, [1024, 3, 1]]
|
||||
- [-1, 1, Conv, [512, 1, 1]]
|
||||
- [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
|
||||
|
||||
- [-2, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 1, Bottleneck, [512, False]]
|
||||
- [-1, 1, Bottleneck, [512, False]]
|
||||
- [-1, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
|
||||
|
||||
- [-2, 1, Conv, [128, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 1, Bottleneck, [256, False]]
|
||||
- [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
|
||||
|
||||
- [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
59
ultralytics/cfg/models/v5/yolov5-p6.yaml
Normal file
59
ultralytics/cfg/models/v5/yolov5-p6.yaml
Normal file
@ -0,0 +1,59 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv5 object detection model with P3-P6 outputs. For details see https://docs.ultralytics.com/models/yolov5
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov5n-p6.yaml' will call yolov5-p6.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 1024]
|
||||
l: [1.00, 1.00, 1024]
|
||||
x: [1.33, 1.25, 1024]
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
- [-1, 1, Conv, [64, 6, 2, 2]] # 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, 6, C3, [256]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 9, C3, [512]]
|
||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C3, [768]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||
- [-1, 3, C3, [1024]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||
|
||||
# YOLOv5 v6.0 head
|
||||
head:
|
||||
- [-1, 1, Conv, [768, 1, 1]]
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||
- [-1, 3, C3, [768, False]] # 15
|
||||
|
||||
- [-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]] # 19
|
||||
|
||||
- [-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]] # 23 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 20], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C3, [512, False]] # 26 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 16], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C3, [768, False]] # 29 (P5/32-large)
|
||||
|
||||
- [-1, 1, Conv, [768, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P6
|
||||
- [-1, 3, C3, [1024, False]] # 32 (P6/64-xlarge)
|
||||
|
||||
- [[23, 26, 29, 32], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
|
||||
48
ultralytics/cfg/models/v5/yolov5.yaml
Normal file
48
ultralytics/cfg/models/v5/yolov5.yaml
Normal file
@ -0,0 +1,48 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv5 object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/models/yolov5
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov5n.yaml' will call yolov5.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 1024]
|
||||
l: [1.00, 1.00, 1024]
|
||||
x: [1.33, 1.25, 1024]
|
||||
|
||||
# YOLOv5 v6.0 backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
- [-1, 1, Conv, [64, 6, 2, 2]] # 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, 6, 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, 3, C3, [1024]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv5 v6.0 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, [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)
|
||||
|
||||
- [[17, 20, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
53
ultralytics/cfg/models/v6/yolov6.yaml
Normal file
53
ultralytics/cfg/models/v6/yolov6.yaml
Normal file
@ -0,0 +1,53 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv6 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/models/yolov6
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
activation: nn.ReLU() # (optional) model default activation function
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 768]
|
||||
l: [1.00, 1.00, 512]
|
||||
x: [1.00, 1.25, 512]
|
||||
|
||||
# YOLOv6-3.0s backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 6, Conv, [128, 3, 1]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 12, Conv, [256, 3, 1]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 18, Conv, [512, 3, 1]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 6, Conv, [1024, 3, 1]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv6-3.0s head
|
||||
head:
|
||||
- [-1, 1, Conv, [256, 1, 1]]
|
||||
- [-1, 1, nn.ConvTranspose2d, [256, 2, 2, 0]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 1, Conv, [256, 3, 1]]
|
||||
- [-1, 9, Conv, [256, 3, 1]] # 14
|
||||
|
||||
- [-1, 1, Conv, [128, 1, 1]]
|
||||
- [-1, 1, nn.ConvTranspose2d, [128, 2, 2, 0]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 1, Conv, [128, 3, 1]]
|
||||
- [-1, 9, Conv, [128, 3, 1]] # 19
|
||||
|
||||
- [-1, 1, Conv, [128, 3, 2]]
|
||||
- [[-1, 15], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 1, Conv, [256, 3, 1]]
|
||||
- [-1, 9, Conv, [256, 3, 1]] # 23
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 10], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 1, Conv, [512, 3, 1]]
|
||||
- [-1, 9, Conv, [512, 3, 1]] # 27
|
||||
|
||||
- [[19, 23, 27], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
25
ultralytics/cfg/models/v8/yolov8-cls-resnet101.yaml
Normal file
25
ultralytics/cfg/models/v8/yolov8-cls-resnet101.yaml
Normal file
@ -0,0 +1,25 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8-cls image classification model. For Usage examples see https://docs.ultralytics.com/tasks/classify
|
||||
|
||||
# Parameters
|
||||
nc: 1000 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 1024]
|
||||
l: [1.00, 1.00, 1024]
|
||||
x: [1.00, 1.25, 1024]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0-P1/2
|
||||
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1-P2/4
|
||||
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2-P3/8
|
||||
- [-1, 1, ResNetLayer, [512, 256, 2, False, 23]] # 3-P4/16
|
||||
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4-P5/32
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, Classify, [nc]] # Classify
|
||||
25
ultralytics/cfg/models/v8/yolov8-cls-resnet50.yaml
Normal file
25
ultralytics/cfg/models/v8/yolov8-cls-resnet50.yaml
Normal file
@ -0,0 +1,25 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8-cls image classification model. For Usage examples see https://docs.ultralytics.com/tasks/classify
|
||||
|
||||
# Parameters
|
||||
nc: 1000 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 1024]
|
||||
l: [1.00, 1.00, 1024]
|
||||
x: [1.00, 1.25, 1024]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0-P1/2
|
||||
- [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1-P2/4
|
||||
- [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2-P3/8
|
||||
- [-1, 1, ResNetLayer, [512, 256, 2, False, 6]] # 3-P4/16
|
||||
- [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4-P5/32
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, Classify, [nc]] # Classify
|
||||
29
ultralytics/cfg/models/v8/yolov8-cls.yaml
Normal file
29
ultralytics/cfg/models/v8/yolov8-cls.yaml
Normal file
@ -0,0 +1,29 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8-cls image classification model. For Usage examples see https://docs.ultralytics.com/tasks/classify
|
||||
|
||||
# Parameters
|
||||
nc: 1000 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 1024]
|
||||
l: [1.00, 1.00, 1024]
|
||||
x: [1.00, 1.25, 1024]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, Classify, [nc]] # Classify
|
||||
54
ultralytics/cfg/models/v8/yolov8-ghost-p2.yaml
Normal file
54
ultralytics/cfg/models/v8/yolov8-ghost-p2.yaml
Normal file
@ -0,0 +1,54 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 object detection model with P2-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n-ghost-p2 summary: 491 layers, 2033944 parameters, 2033928 gradients, 13.8 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p2 summary: 491 layers, 5562080 parameters, 5562064 gradients, 25.1 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m-ghost-p2 summary: 731 layers, 9031728 parameters, 9031712 gradients, 42.8 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l-ghost-p2 summary: 971 layers, 12214448 parameters, 12214432 gradients, 69.1 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x-ghost-p2 summary: 971 layers, 18664776 parameters, 18664760 gradients, 103.3 GFLOPs
|
||||
|
||||
# YOLOv8.0-ghost backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C3Ghost, [128, True]]
|
||||
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C3Ghost, [256, True]]
|
||||
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C3Ghost, [512, True]]
|
||||
- [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C3Ghost, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0-ghost-p2 head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C3Ghost, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 2], 1, Concat, [1]] # cat backbone P2
|
||||
- [-1, 3, C3Ghost, [128]] # 18 (P2/4-xsmall)
|
||||
|
||||
- [-1, 1, GhostConv, [128, 3, 2]]
|
||||
- [[-1, 15], 1, Concat, [1]] # cat head P3
|
||||
- [-1, 3, C3Ghost, [256]] # 21 (P3/8-small)
|
||||
|
||||
- [-1, 1, GhostConv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C3Ghost, [512]] # 24 (P4/16-medium)
|
||||
|
||||
- [-1, 1, GhostConv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C3Ghost, [1024]] # 27 (P5/32-large)
|
||||
|
||||
- [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)
|
||||
56
ultralytics/cfg/models/v8/yolov8-ghost-p6.yaml
Normal file
56
ultralytics/cfg/models/v8/yolov8-ghost-p6.yaml
Normal file
@ -0,0 +1,56 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n-ghost-p6 summary: 529 layers, 2901100 parameters, 2901084 gradients, 5.8 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p6 summary: 529 layers, 9520008 parameters, 9519992 gradients, 16.4 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m-ghost-p6 summary: 789 layers, 18002904 parameters, 18002888 gradients, 34.4 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l-ghost-p6 summary: 1049 layers, 21227584 parameters, 21227568 gradients, 55.3 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x-ghost-p6 summary: 1049 layers, 33057852 parameters, 33057836 gradients, 85.7 GFLOPs
|
||||
|
||||
# YOLOv8.0-ghost backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C3Ghost, [128, True]]
|
||||
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C3Ghost, [256, True]]
|
||||
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C3Ghost, [512, True]]
|
||||
- [-1, 1, GhostConv, [768, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C3Ghost, [768, True]]
|
||||
- [-1, 1, GhostConv, [1024, 3, 2]] # 9-P6/64
|
||||
- [-1, 3, C3Ghost, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||
|
||||
# YOLOv8.0-ghost-p6 head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||
- [-1, 3, C3Ghost, [768]] # 14
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C3Ghost, [512]] # 17
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C3Ghost, [256]] # 20 (P3/8-small)
|
||||
|
||||
- [-1, 1, GhostConv, [256, 3, 2]]
|
||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C3Ghost, [512]] # 23 (P4/16-medium)
|
||||
|
||||
- [-1, 1, GhostConv, [512, 3, 2]]
|
||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C3Ghost, [768]] # 26 (P5/32-large)
|
||||
|
||||
- [-1, 1, GhostConv, [768, 3, 2]]
|
||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||
- [-1, 3, C3Ghost, [1024]] # 29 (P6/64-xlarge)
|
||||
|
||||
- [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
|
||||
47
ultralytics/cfg/models/v8/yolov8-ghost.yaml
Normal file
47
ultralytics/cfg/models/v8/yolov8-ghost.yaml
Normal file
@ -0,0 +1,47 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
# Employs Ghost convolutions and modules proposed in Huawei's GhostNet in https://arxiv.org/abs/1911.11907v2
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n-ghost summary: 403 layers, 1865316 parameters, 1865300 gradients, 5.8 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s-ghost summary: 403 layers, 5960072 parameters, 5960056 gradients, 16.4 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m-ghost summary: 603 layers, 10336312 parameters, 10336296 gradients, 32.7 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l-ghost summary: 803 layers, 14277872 parameters, 14277856 gradients, 53.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x-ghost summary: 803 layers, 22229308 parameters, 22229292 gradients, 83.3 GFLOPs
|
||||
|
||||
# YOLOv8.0n-ghost backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C3Ghost, [128, True]]
|
||||
- [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C3Ghost, [256, True]]
|
||||
- [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C3Ghost, [512, True]]
|
||||
- [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C3Ghost, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C3Ghost, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, GhostConv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C3Ghost, [512]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, GhostConv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C3Ghost, [1024]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
46
ultralytics/cfg/models/v8/yolov8-obb.yaml
Normal file
46
ultralytics/cfg/models/v8/yolov8-obb.yaml
Normal file
@ -0,0 +1,46 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 Oriented Bounding Boxes (OBB) model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, OBB, [nc, 1]] # OBB(P3, P4, P5)
|
||||
54
ultralytics/cfg/models/v8/yolov8-p2.yaml
Normal file
54
ultralytics/cfg/models/v8/yolov8-p2.yaml
Normal file
@ -0,0 +1,54 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 object detection model with P2-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 768]
|
||||
l: [1.00, 1.00, 512]
|
||||
x: [1.00, 1.25, 512]
|
||||
|
||||
# YOLOv8.0 backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0-p2 head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 2], 1, Concat, [1]] # cat backbone P2
|
||||
- [-1, 3, C2f, [128]] # 18 (P2/4-xsmall)
|
||||
|
||||
- [-1, 1, Conv, [128, 3, 2]]
|
||||
- [[-1, 15], 1, Concat, [1]] # cat head P3
|
||||
- [-1, 3, C2f, [256]] # 21 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 24 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 27 (P5/32-large)
|
||||
|
||||
- [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)
|
||||
56
ultralytics/cfg/models/v8/yolov8-p6.yaml
Normal file
56
ultralytics/cfg/models/v8/yolov8-p6.yaml
Normal file
@ -0,0 +1,56 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 object detection model with P3-P6 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 768]
|
||||
l: [1.00, 1.00, 512]
|
||||
x: [1.00, 1.25, 512]
|
||||
|
||||
# YOLOv8.0x6 backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [768, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||
|
||||
# YOLOv8.0x6 head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||
- [-1, 3, C2, [768, False]] # 14
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2, [512, False]] # 17
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
||||
|
||||
- [-1, 1, Conv, [768, 3, 2]]
|
||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
||||
|
||||
- [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
|
||||
57
ultralytics/cfg/models/v8/yolov8-pose-p6.yaml
Normal file
57
ultralytics/cfg/models/v8/yolov8-pose-p6.yaml
Normal file
@ -0,0 +1,57 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8-pose-p6 keypoints/pose estimation model. For Usage examples see https://docs.ultralytics.com/tasks/pose
|
||||
|
||||
# Parameters
|
||||
nc: 1 # number of classes
|
||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 768]
|
||||
l: [1.00, 1.00, 512]
|
||||
x: [1.00, 1.25, 512]
|
||||
|
||||
# YOLOv8.0x6 backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [768, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||
|
||||
# YOLOv8.0x6 head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||
- [-1, 3, C2, [768, False]] # 14
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2, [512, False]] # 17
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
||||
|
||||
- [-1, 1, Conv, [768, 3, 2]]
|
||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
||||
|
||||
- [[20, 23, 26, 29], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5, P6)
|
||||
47
ultralytics/cfg/models/v8/yolov8-pose.yaml
Normal file
47
ultralytics/cfg/models/v8/yolov8-pose.yaml
Normal file
@ -0,0 +1,47 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8-pose keypoints/pose estimation model. For Usage examples see https://docs.ultralytics.com/tasks/pose
|
||||
|
||||
# Parameters
|
||||
nc: 1 # number of classes
|
||||
kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-pose.yaml' will call yolov8-pose.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 768]
|
||||
l: [1.00, 1.00, 512]
|
||||
x: [1.00, 1.25, 512]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5)
|
||||
46
ultralytics/cfg/models/v8/yolov8-rtdetr.yaml
Normal file
46
ultralytics/cfg/models/v8/yolov8-rtdetr.yaml
Normal file
@ -0,0 +1,46 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
|
||||
56
ultralytics/cfg/models/v8/yolov8-seg-p6.yaml
Normal file
56
ultralytics/cfg/models/v8/yolov8-seg-p6.yaml
Normal file
@ -0,0 +1,56 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8-seg-p6 instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 768]
|
||||
l: [1.00, 1.00, 512]
|
||||
x: [1.00, 1.25, 512]
|
||||
|
||||
# YOLOv8.0x6 backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [768, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 11
|
||||
|
||||
# YOLOv8.0x6 head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 8], 1, Concat, [1]] # cat backbone P5
|
||||
- [-1, 3, C2, [768, False]] # 14
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2, [512, False]] # 17
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2, [256, False]] # 20 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 17], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 14], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2, [768, False]] # 26 (P5/32-large)
|
||||
|
||||
- [-1, 1, Conv, [768, 3, 2]]
|
||||
- [[-1, 11], 1, Concat, [1]] # cat head P6
|
||||
- [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
|
||||
|
||||
- [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6)
|
||||
46
ultralytics/cfg/models/v8/yolov8-seg.yaml
Normal file
46
ultralytics/cfg/models/v8/yolov8-seg.yaml
Normal file
@ -0,0 +1,46 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8-seg instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n-seg.yaml' will call yolov8-seg.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024]
|
||||
s: [0.33, 0.50, 1024]
|
||||
m: [0.67, 0.75, 768]
|
||||
l: [1.00, 1.00, 512]
|
||||
x: [1.00, 1.25, 512]
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)
|
||||
48
ultralytics/cfg/models/v8/yolov8-world.yaml
Normal file
48
ultralytics/cfg/models/v8/yolov8-world.yaml
Normal file
@ -0,0 +1,48 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8-World object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2fAttn, [512, 256, 8]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2fAttn, [256, 128, 4]] # 15 (P3/8-small)
|
||||
|
||||
- [[15, 12, 9], 1, ImagePoolingAttn, [256]] # 16 (P3/8-small)
|
||||
|
||||
- [15, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fAttn, [512, 256, 8]] # 19 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fAttn, [1024, 512, 16]] # 22 (P5/32-large)
|
||||
|
||||
- [[15, 19, 22], 1, WorldDetect, [nc, 512, False]] # Detect(P3, P4, P5)
|
||||
46
ultralytics/cfg/models/v8/yolov8-worldv2.yaml
Normal file
46
ultralytics/cfg/models/v8/yolov8-worldv2.yaml
Normal file
@ -0,0 +1,46 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8-World-v2 object detection model with P3-P5 outputs. For details see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2fAttn, [512, 256, 8]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2fAttn, [256, 128, 4]] # 15 (P3/8-small)
|
||||
|
||||
- [15, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2fAttn, [512, 256, 8]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2fAttn, [1024, 512, 16]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, WorldDetect, [nc, 512, True]] # Detect(P3, P4, P5)
|
||||
46
ultralytics/cfg/models/v8/yolov8.yaml
Normal file
46
ultralytics/cfg/models/v8/yolov8.yaml
Normal file
@ -0,0 +1,46 @@
|
||||
# Ultralytics YOLO 🚀, AGPL-3.0 license
|
||||
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
|
||||
# [depth, width, max_channels]
|
||||
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
|
||||
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
|
||||
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
|
||||
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
|
||||
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
|
||||
|
||||
# YOLOv8.0n backbone
|
||||
backbone:
|
||||
# [from, repeats, module, args]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 3, C2f, [128, True]]
|
||||
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
|
||||
- [-1, 6, C2f, [256, True]]
|
||||
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
|
||||
- [-1, 6, C2f, [512, True]]
|
||||
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
|
||||
- [-1, 3, C2f, [1024, True]]
|
||||
- [-1, 1, SPPF, [1024, 5]] # 9
|
||||
|
||||
# YOLOv8.0n head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 3, C2f, [512]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, "nearest"]]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, Conv, [256, 3, 2]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, Conv, [512, 3, 2]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
36
ultralytics/cfg/models/v9/yolov9c.yaml
Normal file
36
ultralytics/cfg/models/v9/yolov9c.yaml
Normal file
@ -0,0 +1,36 @@
|
||||
# YOLOv9
|
||||
|
||||
# parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
# gelan backbone
|
||||
backbone:
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
|
||||
- [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]] # 2
|
||||
- [-1, 1, ADown, [256]] # 3-P3/8
|
||||
- [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]] # 4
|
||||
- [-1, 1, ADown, [512]] # 5-P4/16
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 6
|
||||
- [-1, 1, ADown, [512]] # 7-P5/32
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 8
|
||||
- [-1, 1, SPPELAN, [512, 256]] # 9
|
||||
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 12
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]] # 15 (P3/8-small)
|
||||
|
||||
- [-1, 1, ADown, [256]]
|
||||
- [[-1, 12], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 18 (P4/16-medium)
|
||||
|
||||
- [-1, 1, ADown, [512]]
|
||||
- [[-1, 9], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 21 (P5/32-large)
|
||||
|
||||
- [[15, 18, 21], 1, Detect, [nc]] # DDetect(P3, P4, P5)
|
||||
60
ultralytics/cfg/models/v9/yolov9e.yaml
Normal file
60
ultralytics/cfg/models/v9/yolov9e.yaml
Normal file
@ -0,0 +1,60 @@
|
||||
# YOLOv9
|
||||
|
||||
# parameters
|
||||
nc: 80 # number of classes
|
||||
|
||||
# gelan backbone
|
||||
backbone:
|
||||
- [-1, 1, Silence, []]
|
||||
- [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 2-P2/4
|
||||
- [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]] # 3
|
||||
- [-1, 1, ADown, [256]] # 4-P3/8
|
||||
- [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]] # 5
|
||||
- [-1, 1, ADown, [512]] # 6-P4/16
|
||||
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 7
|
||||
- [-1, 1, ADown, [1024]] # 8-P5/32
|
||||
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 9
|
||||
|
||||
- [1, 1, CBLinear, [[64]]] # 10
|
||||
- [3, 1, CBLinear, [[64, 128]]] # 11
|
||||
- [5, 1, CBLinear, [[64, 128, 256]]] # 12
|
||||
- [7, 1, CBLinear, [[64, 128, 256, 512]]] # 13
|
||||
- [9, 1, CBLinear, [[64, 128, 256, 512, 1024]]] # 14
|
||||
|
||||
- [0, 1, Conv, [64, 3, 2]] # 15-P1/2
|
||||
- [[10, 11, 12, 13, 14, -1], 1, CBFuse, [[0, 0, 0, 0, 0]]] # 16
|
||||
- [-1, 1, Conv, [128, 3, 2]] # 17-P2/4
|
||||
- [[11, 12, 13, 14, -1], 1, CBFuse, [[1, 1, 1, 1]]] # 18
|
||||
- [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]] # 19
|
||||
- [-1, 1, ADown, [256]] # 20-P3/8
|
||||
- [[12, 13, 14, -1], 1, CBFuse, [[2, 2, 2]]] # 21
|
||||
- [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]] # 22
|
||||
- [-1, 1, ADown, [512]] # 23-P4/16
|
||||
- [[13, 14, -1], 1, CBFuse, [[3, 3]]] # 24
|
||||
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 25
|
||||
- [-1, 1, ADown, [1024]] # 26-P5/32
|
||||
- [[14, -1], 1, CBFuse, [[4]]] # 27
|
||||
- [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 28
|
||||
- [-1, 1, SPPELAN, [512, 256]] # 29
|
||||
|
||||
# gelan head
|
||||
head:
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 25], 1, Concat, [1]] # cat backbone P4
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 32
|
||||
|
||||
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
|
||||
- [[-1, 22], 1, Concat, [1]] # cat backbone P3
|
||||
- [-1, 1, RepNCSPELAN4, [256, 256, 128, 2]] # 35 (P3/8-small)
|
||||
|
||||
- [-1, 1, ADown, [256]]
|
||||
- [[-1, 32], 1, Concat, [1]] # cat head P4
|
||||
- [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 38 (P4/16-medium)
|
||||
|
||||
- [-1, 1, ADown, [512]]
|
||||
- [[-1, 29], 1, Concat, [1]] # cat head P5
|
||||
- [-1, 1, RepNCSPELAN4, [512, 1024, 512, 2]] # 41 (P5/32-large)
|
||||
|
||||
# detect
|
||||
- [[35, 38, 41], 1, Detect, [nc]] # Detect(P3, P4, P5)
|
||||
Reference in New Issue
Block a user