ultralytics 8.0.54 TFLite export improvements and fixes (ultralytic…

…s#1447) Co-authored-by: Laughing <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
CV4EcologySchool · Mar 16, 2023 · 701fba4 · 701fba4
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diff --git a/README.md b/README.md
@@ -79,7 +79,7 @@ pip install ultralytics
 YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
 
 ```bash
-yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
+yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
 ```
 
 `yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8

diff --git a/README.zh-CN.md b/README.zh-CN.md
@@ -71,7 +71,7 @@ pip install ultralytics
 YOLOv8 可以直接在命令行界面（CLI）中使用 `yolo` 命令运行：
 
 ```bash
-yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
+yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
 ```
 
 `yolo`可以用于各种任务和模式，并接受额外的参数，例如 `imgsz=640`。参见 YOLOv8 [文档](https://docs.ultralytics.com)

diff --git a/docs/modes/export.md b/docs/modes/export.md
@@ -22,11 +22,11 @@ export arguments.
         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"
 

diff --git a/docs/modes/track.md b/docs/modes/track.md
@@ -26,9 +26,9 @@ Use a trained YOLOv8n/YOLOv8n-seg model to run tracker on video streams.
         from ultralytics import YOLO
         
         # Load a model
-        model = YOLO("yolov8n.pt")  # load an official detection model
-        model = YOLO("yolov8n-seg.pt")  # load an official segmentation model
-        model = YOLO("path/to/best.pt")  # load a custom model
+        model = YOLO('yolov8n.pt')  # load an official detection model
+        model = YOLO('yolov8n-seg.pt')  # load an official segmentation model
+        model = YOLO('path/to/best.pt')  # load a custom model
         
         # Track with the model
         results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True) 
@@ -60,7 +60,7 @@ to [predict page](https://docs.ultralytics.com/modes/predict/).
         ```python
         from ultralytics import YOLO
         
-        model = YOLO("yolov8n.pt")
+        model = YOLO('yolov8n.pt')
         results = model.track(source="https://youtu.be/Zgi9g1ksQHc", conf=0.3, iou=0.5, show=True) 
         ```
     === "CLI"
@@ -82,7 +82,7 @@ any configurations(expect the `tracker_type`) you need to.
         ```python
         from ultralytics import YOLO
         
-        model = YOLO("yolov8n.pt")
+        model = YOLO('yolov8n.pt')
         results = model.track(source="https://youtu.be/Zgi9g1ksQHc", tracker='custom_tracker.yaml') 
         ```
     === "CLI"

diff --git a/docs/modes/train.md b/docs/modes/train.md
@@ -21,16 +21,24 @@ training arguments.
         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 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)
+        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
         ```
 
 ## Arguments

diff --git a/docs/modes/val.md b/docs/modes/val.md
@@ -21,8 +21,8 @@ training `data` and arguments as model attributes. See Arguments section below f
         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

diff --git a/docs/quickstart.md b/docs/quickstart.md
@@ -60,14 +60,14 @@ classification into their Python projects using YOLOv8.
     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)
 
     # Use the model
-    results = model.train(data="coco128.yaml", epochs=3)  # train the model
+    results = model.train(data='coco128.yaml', epochs=3)  # train the model
     results = model.val()  # evaluate model performance on the validation set
-    results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
-    success = model.export(format="onnx")  # export the model to ONNX format
+    results = model('https://ultralytics.com/images/bus.jpg')  # predict on an image
+    success = model.export(format='onnx')  # export the model to ONNX format
     ```
 
 [Python Guide](usage/python.md){.md-button .md-button--primary}
diff --git a/docs/tasks/classify.md b/docs/tasks/classify.md
@@ -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"
 

diff --git a/docs/tasks/detect.md b/docs/tasks/detect.md
@@ -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"
 

diff --git a/docs/tasks/keypoints.md b/docs/tasks/keypoints.md
@@ -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"
 

diff --git a/docs/tasks/segment.md b/docs/tasks/segment.md
@@ -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"
 

diff --git a/docs/usage/callbacks.md b/docs/usage/callbacks.md
@@ -17,7 +17,7 @@ def on_predict_batch_end(predictor):
     im0s = im0s if isinstance(im0s, list) else [im0s]
     predictor.results = zip(predictor.results, im0s)
 
-model = YOLO(f"yolov8n.pt")
+model = YOLO(f'yolov8n.pt')
 model.add_callback("on_predict_batch_end", on_predict_batch_end)
 for (result, frame) in model.track/predict():
     pass

diff --git a/docs/usage/cli.md b/docs/usage/cli.md
@@ -59,8 +59,8 @@ Use a trained YOLOv8n model to run predictions on images.
 !!! example ""
 
       ```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
       ```
 
 ## Export