Remove unused code (#4327)
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@ -152,29 +152,33 @@ This comparison shows the order-of-magnitude differences in the model sizes and
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Tests run on a 2023 Apple M2 Macbook with 16GB of RAM. To reproduce this test:
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```python
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from ultralytics import FastSAM, SAM, YOLO
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# Profile SAM-b
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model = SAM('sam_b.pt')
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model.info()
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model('ultralytics/assets')
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!!! example ""
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# Profile MobileSAM
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model = SAM('mobile_sam.pt')
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model.info()
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model('ultralytics/assets')
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# Profile FastSAM-s
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model = FastSAM('FastSAM-s.pt')
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model.info()
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model('ultralytics/assets')
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# Profile YOLOv8n-seg
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model = YOLO('yolov8n-seg.pt')
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model.info()
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model('ultralytics/assets')
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```
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=== "Python"
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```python
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from ultralytics import FastSAM, SAM, YOLO
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# Profile SAM-b
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model = SAM('sam_b.pt')
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model.info()
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model('ultralytics/assets')
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# Profile MobileSAM
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model = SAM('mobile_sam.pt')
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model.info()
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model('ultralytics/assets')
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# Profile FastSAM-s
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model = FastSAM('FastSAM-s.pt')
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model.info()
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model('ultralytics/assets')
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# Profile YOLOv8n-seg
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model = YOLO('yolov8n-seg.pt')
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model.info()
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model('ultralytics/assets')
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```
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## Auto-Annotation: A Quick Path to Segmentation Datasets
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@ -184,11 +188,14 @@ Auto-annotation is a key feature of SAM, allowing users to generate a [segmentat
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To auto-annotate your dataset with the Ultralytics framework, use the `auto_annotate` function as shown below:
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```python
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from ultralytics.data.annotator import auto_annotate
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!!! example ""
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auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
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```
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=== "Python"
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```python
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from ultralytics.data.annotator import auto_annotate
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auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
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```
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| Argument | Type | Description | Default |
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|------------|---------------------|---------------------------------------------------------------------------------------------------------|--------------|
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