xdobro23/YOLOv8-16bit
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

ultralytics 8.0.65 YOLOv8 Pose models (#1347)

Browse Source 
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Mert Can Demir <validatedev@gmail.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Fabian Greavu <fabiangreavu@gmail.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Eric Pedley <ericpedley@gmail.com>
Co-authored-by: JustasBart <40023722+JustasBart@users.noreply.github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Aarni Koskela <akx@iki.fi>
Co-authored-by: Sergio Sanchez <sergio.ssm.97@gmail.com>
Co-authored-by: Bogdan Gheorghe <112427971+bogdan-galileo@users.noreply.github.com>
Co-authored-by: Jaap van de Loosdrecht <jaap@vdlmv.nl>
Co-authored-by: Noobtoss <96134731+Noobtoss@users.noreply.github.com>
Co-authored-by: nerdyespresso <106761627+nerdyespresso@users.noreply.github.com>
Co-authored-by: Farid Inawan <frdteknikelektro@gmail.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Alexander Duda <Alexander.Duda@me.com>
Co-authored-by: Mehran Ghandehari <mehran.maps@gmail.com>
Co-authored-by: Snyk bot <snyk-bot@snyk.io>
Co-authored-by: majid nasiri <majnasai@gmail.com>
This commit is contained in:
Ayush Chaurasia
2023-04-06 03:55:32 +05:30
committed by GitHub
parent 9af3e69b1a
commit 1cb92d7f42
57 changed files with 1578 additions and 489 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
docs/index.md
View File

@ -42,4 +42,4 @@ Since its launch YOLO has been employed in various applications, including auton
YOLOv8 is designed with a strong focus on speed, size, and accuracy, making it a compelling choice for various vision AI tasks. It outperforms previous versions by incorporating innovations like a new backbone network, a new anchor-free split head, and new loss functions. These improvements enable YOLOv8 to deliver superior results, while maintaining a compact size and exceptional speed.
Additionally, YOLOv8 supports a full range of vision AI tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/keypoints.md), [tracking](modes/track.md), and [classification](tasks/classify.md). This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.
Additionally, YOLOv8 supports a full range of vision AI tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md). This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.

2
docs/modes/benchmark.md
View File

@ -1,7 +1,7 @@
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
**Benchmark mode** is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks
provide information on the size of the exported format, its `mAP50-95` metrics (for object detection and segmentation)
provide information on the size of the exported format, its `mAP50-95` metrics (for object detection, segmentation and pose)
or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export
formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for
their specific use case based on their requirements for speed and accuracy.

2
docs/modes/index.md
View File

@ -54,7 +54,7 @@ for applications such as surveillance systems or self-driving cars.
## [Benchmark](benchmark.md)
Benchmark mode is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks provide
information on the size of the exported format, its `mAP50-95` metrics (for object detection and segmentation)
information on the size of the exported format, its `mAP50-95` metrics (for object detection, segmentation and pose)
or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export
formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for
their specific use case based on their requirements for speed and accuracy.

2
docs/modes/train.md
View File

@ -88,6 +88,8 @@ task.
| `box` | `7.5` | box loss gain |
| `cls` | `0.5` | cls loss gain (scale with pixels) |
| `dfl` | `1.5` | dfl loss gain |
| `pose` | `12.0` | pose loss gain (pose-only) |
| `kobj` | `2.0` | keypoint obj loss gain (pose-only) |
| `fl_gamma` | `0.0` | focal loss gamma (efficientDet default gamma=1.5) |
| `label_smoothing` | `0.0` | label smoothing (fraction) |
| `nbs` | `64` | nominal batch size |

8
docs/reference/ops.md
View File

@ -175,9 +175,9 @@ show_source: false
show_root_toc_entry: false
---
## scale_segments
## scale_coords
:::ultralytics.yolo.utils.ops.scale_segments
:::ultralytics.yolo.utils.ops.scale_coords
handler: python
options:
show_source: false
@ -193,9 +193,9 @@ show_source: false
show_root_toc_entry: false
---
## clip_segments
## clip_coords
:::ultralytics.yolo.utils.ops.clip_segments
:::ultralytics.yolo.utils.ops.clip_coords
handler: python
options:
show_source: false

5
docs/tasks/classify.md
View File

@ -122,7 +122,7 @@ Use a trained YOLOv8n-cls model to run predictions on images.
yolo classify predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
```
Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
See full `predict` mode details in the [Predict](https://docs.ultralytics.com/modes/predict/) page.
## Export
@ -150,7 +150,7 @@ Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
```
Available YOLOv8-cls export formats are in the table below. You can predict or validate directly on exported models,
i.e. `yolo predict model=yolov8n-cls.onnx`.
i.e. `yolo predict model=yolov8n-cls.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata |
|--------------------------------------------------------------------|-------------------|-------------------------------|----------|
@ -167,3 +167,4 @@ i.e. `yolo predict model=yolov8n-cls.onnx`.
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-cls_web_model/` | ✅ |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

6
docs/tasks/detect.md
View File

@ -123,7 +123,7 @@ Use a trained YOLOv8n model to run predictions on images.
yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
```
Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
See full `predict` mode details in the [Predict](https://docs.ultralytics.com/modes/predict/) page.
## Export
@ -151,7 +151,7 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
```
Available YOLOv8 export formats are in the table below. You can predict or validate directly on exported models,
i.e. `yolo predict model=yolov8n.onnx`.
i.e. `yolo predict model=yolov8n.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata |
|--------------------------------------------------------------------|-------------------|---------------------------|----------|
@ -167,3 +167,5 @@ i.e. `yolo predict model=yolov8n.onnx`.
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

10
docs/tasks/index.md
View File

@ -2,7 +2,7 @@
YOLOv8 is an AI framework that supports multiple computer vision **tasks**. The framework can be used to
perform [detection](detect.md), [segmentation](segment.md), [classification](classify.md),
and [keypoints](keypoints.md) detection. Each of these tasks has a different objective and use case.
and [pose](pose.md) estimation. Each of these tasks has a different objective and use case.
<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
@ -29,15 +29,13 @@ images based on their content. It uses a variant of the EfficientNet architectur
[Classification Examples](classify.md){ .md-button .md-button--primary}
<!--
## [Keypoints](keypoints.md)
## [Pose](pose.md)
Keypoints detection is a task that involves detecting specific points in an image or video frame. These points are
Pose/keypoint detection is a task that involves detecting specific points in an image or video frame. These points are
referred to as keypoints and are used to track movement or pose estimation. YOLOv8 can detect keypoints in an image or
video frame with high accuracy and speed.
[Keypoints Examples](keypoints.md){ .md-button .md-button--primary}
-->
[Pose Examples](pose.md){ .md-button .md-button--primary}
## Conclusion

149
docs/tasks/keypoints.md
View File

@ -1,149 +0,0 @@
Key Point Estimation is a task that involves identifying the location of specific points in an image, usually referred
to as keypoints. The keypoints can represent various parts of the object such as joints, landmarks, or other distinctive
features. The locations of the keypoints are usually represented as a set of 2D `[x, y]` or 3D `[x, y, visible]`
coordinates.
<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
The output of a keypoint detector is a set of points that represent the keypoints on the object in the image, usually
along with the confidence scores for each point. Keypoint estimation is a good choice when you need to identify specific
parts of an object in a scene, and their location in relation to each other.
!!! tip "Tip"
YOLOv8 _keypoints_ models use the `-kpts` suffix, i.e. `yolov8n-kpts.pt`. These models are trained on the COCO dataset and are suitable for a variety of keypoint estimation tasks.
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8){ .md-button .md-button--primary}
## Train TODO
Train an OpenPose model on a custom dataset of keypoints using the OpenPose framework. For more information on how to
train an OpenPose model on a custom dataset, see the OpenPose Training page.
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.yaml') # build a new model from YAML
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n.yaml').load('yolov8n.pt') # build from YAML and transfer weights
# Train the model
model.train(data='coco128.yaml', epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Build a new model from YAML and start training from scratch
yolo detect train data=coco128.yaml model=yolov8n.yaml epochs=100 imgsz=640
# Start training from a pretrained *.pt model
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
# Build a new model from YAML, transfer pretrained weights to it and start training
yolo detect train data=coco128.yaml model=yolov8n.yaml pretrained=yolov8n.pt epochs=100 imgsz=640
```
## Val TODO
Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
training `data` and arguments as model attributes.
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.box.map # map50-95
metrics.box.map50 # map50
metrics.box.map75 # map75
metrics.box.maps # a list contains map50-95 of each category
```
=== "CLI"
```bash
yolo detect val model=yolov8n.pt # val official model
yolo detect val model=path/to/best.pt # val custom model
```
## Predict TODO
Use a trained YOLOv8n model to run predictions on images.
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Predict with the model
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
```
=== "CLI"
```bash
yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
```
Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
## Export TODO
Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom trained
# Export the model
model.export(format='onnx')
```
=== "CLI"
```bash
yolo export model=yolov8n.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
Available YOLOv8-pose export formats are in the table below. You can predict or validate directly on exported models,
i.e. `yolo predict model=yolov8n-pose.onnx`.
| Format | `format` Argument | Model | Metadata |
|--------------------------------------------------------------------|-------------------|---------------------------|----------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ |
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ |

175
docs/tasks/pose.md Normal file
View File

@ -0,0 +1,175 @@
Pose estimation is a task that involves identifying the location of specific points in an image, usually referred
to as keypoints. The keypoints can represent various parts of the object such as joints, landmarks, or other distinctive
features. The locations of the keypoints are usually represented as a set of 2D `[x, y]` or 3D `[x, y, visible]`
coordinates.
<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
The output of a pose estimation model is a set of points that represent the keypoints on an object in the image, usually
along with the confidence scores for each point. Pose estimation is a good choice when you need to identify specific
parts of an object in a scene, and their location in relation to each other.
!!! tip "Tip"
YOLOv8 _pose_ models use the `-pose` suffix, i.e. `yolov8n-pose.pt`. These models are trained on the [COCO keypoints](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco-pose.yaml) dataset and are suitable for a variety of pose estimation tasks.
## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8)
YOLOv8 pretrained Pose models are shown here. Detect, Segment and Pose models are pretrained on
the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify
models are pretrained on
the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>pose<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|------------------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | - | 49.7 | - | - | 3.3 | 9.2 |
| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | - | 59.2 | - | - | 11.6 | 30.2 |
| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | - | 63.6 | - | - | 26.4 | 81.0 |
| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | - | 67.0 | - | - | 44.4 | 168.6 |
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | - | 68.9 | - | - | 69.4 | 263.2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | - | 71.5 | - | - | 99.1 | 1066.4 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org)
dataset.
<br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
instance.
<br>Reproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu`
## Train
Train a YOLOv8-pose model on the COCO128-pose dataset.
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-pose.yaml') # build a new model from YAML
model = YOLO('yolov8n-pose.pt') # load a pretrained model (recommended for training)
model = YOLO('yolov8n-pose.yaml').load('yolov8n-pose.pt') # build from YAML and transfer weights
# Train the model
model.train(data='coco128-pose.yaml', epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Build a new model from YAML and start training from scratch
yolo detect train data=coco128-pose.yaml model=yolov8n-pose.yaml epochs=100 imgsz=640
# Start training from a pretrained *.pt model
yolo detect train data=coco128-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
# Build a new model from YAML, transfer pretrained weights to it and start training
yolo detect train data=coco128-pose.yaml model=yolov8n-pose.yaml pretrained=yolov8n-pose.pt epochs=100 imgsz=640
```
## Val
Validate trained YOLOv8n-pose model accuracy on the COCO128-pose dataset. No argument need to passed as the `model`
retains it's
training `data` and arguments as model attributes.
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-pose.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.box.map # map50-95
metrics.box.map50 # map50
metrics.box.map75 # map75
metrics.box.maps # a list contains map50-95 of each category
```
=== "CLI"
```bash
yolo pose val model=yolov8n-pose.pt # val official model
yolo pose val model=path/to/best.pt # val custom model
```
## Predict
Use a trained YOLOv8n-pose model to run predictions on images.
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-pose.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom model
# Predict with the model
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
```
=== "CLI"
```bash
yolo pose predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
yolo pose predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
```
See full `predict` mode details in the [Predict](https://docs.ultralytics.com/modes/predict/) page.
## Export
Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
!!! example ""
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom trained
# Export the model
model.export(format='onnx')
```
=== "CLI"
```bash
yolo export model=yolov8n.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
Available YOLOv8-pose export formats are in the table below. You can predict or validate directly on exported models,
i.e. `yolo predict model=yolov8n-pose.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata |
|--------------------------------------------------------------------|-------------------|--------------------------------|----------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n-pose.pt` | ✅ |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-pose.torchscript` | ✅ |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-pose.onnx` | ✅ |
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-pose_openvino_model/` | ✅ |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-pose.engine` | ✅ |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-pose.mlmodel` | ✅ |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-pose_saved_model/` | ✅ |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-pose.pb` | ❌ |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-pose.tflite` | ✅ |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-pose_edgetpu.tflite` | ✅ |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-pose_web_model/` | ✅ |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

6
docs/tasks/segment.md
View File

@ -127,7 +127,7 @@ Use a trained YOLOv8n-seg model to run predictions on images.
yolo segment predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
```
Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page.
See full `predict` mode details in the [Predict](https://docs.ultralytics.com/modes/predict/) page.
## Export
@ -155,7 +155,7 @@ Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
```
Available YOLOv8-seg export formats are in the table below. You can predict or validate directly on exported models,
i.e. `yolo predict model=yolov8n-seg.onnx`.
i.e. `yolo predict model=yolov8n-seg.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata |
|--------------------------------------------------------------------|-------------------|-------------------------------|----------|
@ -172,4 +172,4 @@ i.e. `yolo predict model=yolov8n-seg.onnx`.
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-seg_web_model/` | ✅ |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

2
docs/usage/cfg.md
View File

@ -110,6 +110,8 @@ The training settings for YOLO models encompass various hyperparameters and conf
| `box` | `7.5` | box loss gain |
| `cls` | `0.5` | cls loss gain (scale with pixels) |
| `dfl` | `1.5` | dfl loss gain |
| `pose` | `12.0` | pose loss gain (pose-only) |
| `kobj` | `2.0` | keypoint obj loss gain (pose-only) |
| `fl_gamma` | `0.0` | focal loss gamma (efficientDet default gamma=1.5) |
| `label_smoothing` | `0.0` | label smoothing (fraction) |
| `nbs` | `64` | nominal batch size |

2
docs/usage/engine.md
View File

@ -74,7 +74,7 @@ trainer.add_callback("on_train_epoch_end", log_model) # Adds to existing callba
trainer.train()
```
To know more about Callback triggering events and entry point, checkout our Callbacks guide # TODO
To know more about Callback triggering events and entry point, checkout our [Callbacks Guide](callbacks.md)
## Other engine components

1
docs/usage/python.md
View File

@ -59,7 +59,6 @@ accurately predict the classes and locations of objects in an image.
=== "Resume"
```python
# TODO: Resume feature is under development and should be released soon.
model = YOLO("last.pt")
model.train(resume=True)
```