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ultralytics 8.0.54 TFLite export improvements and fixes (#1447)

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Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-03-16 15:42:44 +01:00
committed by GitHub
parent 30fc4b537f
commit 701fba4770
30 changed files with 198 additions and 166 deletions
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26
docs/tasks/classify.md
View File

@ -26,11 +26,11 @@ see the [Configuration](../usage/cfg.md) page.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-cls.yaml") # build a new model from scratch
model = YOLO("yolov8n-cls.pt") # load a pretrained model (recommended for training)
model = YOLO('yolov8n-cls.yaml') # build a new model from scratch
model = YOLO('yolov8n-cls.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data="mnist160", epochs=100, imgsz=64)
model.train(data='mnist160', epochs=100, imgsz=64)
```
=== "CLI"
@ -51,8 +51,8 @@ it's training `data` and arguments as model attributes.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-cls.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
model = YOLO('yolov8n-cls.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
@ -78,17 +78,17 @@ Use a trained YOLOv8n-cls model to run predictions on images.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-cls.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
model = YOLO('yolov8n-cls.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
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
```
=== "CLI"
```bash
yolo classify predict model=yolov8n-cls.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo classify predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
yolo classify predict model=yolov8n-cls.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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.
@ -105,11 +105,11 @@ Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-cls.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained
model = YOLO('yolov8n-cls.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom trained
# Export the model
model.export(format="onnx")
model.export(format='onnx')
```
=== "CLI"

26
docs/tasks/detect.md
View File

@ -26,11 +26,11 @@ the [Configuration](../usage/cfg.md) page.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO('yolov8n.yaml') # build a new model from scratch
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data="coco128.yaml", epochs=100, imgsz=640)
model.train(data='coco128.yaml', epochs=100, imgsz=640)
```
=== "CLI"
@ -51,8 +51,8 @@ training `data` and arguments as model attributes.
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
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
@ -80,17 +80,17 @@ Use a trained YOLOv8n model to run predictions on images.
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
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
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
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.
@ -107,11 +107,11 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
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
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")
model.export(format='onnx')
```
=== "CLI"

26
docs/tasks/keypoints.md
View File

@ -28,11 +28,11 @@ train an OpenPose model on a custom dataset, see the OpenPose Training page.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
model = YOLO('yolov8n.yaml') # build a new model from scratch
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data="coco128.yaml", epochs=100, imgsz=640)
model.train(data='coco128.yaml', epochs=100, imgsz=640)
```
=== "CLI"
@ -53,8 +53,8 @@ training `data` and arguments as model attributes.
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
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
@ -82,17 +82,17 @@ Use a trained YOLOv8n model to run predictions on images.
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
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
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
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.
@ -109,11 +109,11 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
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
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")
model.export(format='onnx')
```
=== "CLI"

26
docs/tasks/segment.md
View File

@ -26,11 +26,11 @@ arguments see the [Configuration](../usage/cfg.md) page.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-seg.yaml") # build a new model from scratch
model = YOLO("yolov8n-seg.pt") # load a pretrained model (recommended for training)
model = YOLO('yolov8n-seg.yaml') # build a new model from scratch
model = YOLO('yolov8n-seg.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)
model.train(data='coco128-seg.yaml', epochs=100, imgsz=640)
```
=== "CLI"
@ -51,8 +51,8 @@ retains it's training `data` and arguments as model attributes.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-seg.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
model = YOLO('yolov8n-seg.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
@ -84,17 +84,17 @@ Use a trained YOLOv8n-seg model to run predictions on images.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-seg.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
model = YOLO('yolov8n-seg.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
results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
```
=== "CLI"
```bash
yolo segment predict model=yolov8n-seg.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo segment predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
yolo segment predict model=yolov8n-seg.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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.
@ -111,11 +111,11 @@ Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-seg.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained
model = YOLO('yolov8n-seg.pt') # load an official model
model = YOLO('path/to/best.pt') # load a custom trained
# Export the model
model.export(format="onnx")
model.export(format='onnx')
```
=== "CLI"