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Add Quickstart Docs YouTube video (#5733)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher
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@ -118,6 +118,17 @@ Ultralytics provides various installation methods including pip, conda, and Dock
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See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) file for a list of dependencies. Note that all examples above install all required dependencies.
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<p align="center">
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<br>
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<iframe width="720" height="405" src="https://www.youtube.com/embed/MWq1UxqTClU?si=nHAW-lYDzrz68jR0"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> Ultralytics YOLO for Object Detection: Quickstart Guide for Installation and Setup.
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</p>
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!!! tip "Tip"
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PyTorch requirements vary by operating system and CUDA requirements, so it's recommended to install PyTorch first following instructions at [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally).
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@ -61,15 +61,14 @@ class SAM(Model):
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Performs segmentation prediction on the given image or video source.
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Args:
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source: Path to the image or video file, or a PIL.Image object, or a numpy.ndarray object.
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source (str): Path to the image or video file, or a PIL.Image object, or a numpy.ndarray object.
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stream (bool, optional): If True, enables real-time streaming. Defaults to False.
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bboxes (list, optional): List of bounding box coordinates for prompted segmentation. Defaults to None.
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points (list, optional): List of points for prompted segmentation. Defaults to None.
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labels (list, optional): List of labels for prompted segmentation. Defaults to None.
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**kwargs: Additional keyword arguments.
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Returns:
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The segmentation masks.
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(list): The model predictions.
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"""
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overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024)
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kwargs.update(overrides)
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@ -81,15 +80,14 @@ class SAM(Model):
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Alias for the 'predict' method.
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Args:
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source: Path to the image or video file, or a PIL.Image object, or a numpy.ndarray object.
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source (str): Path to the image or video file, or a PIL.Image object, or a numpy.ndarray object.
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stream (bool, optional): If True, enables real-time streaming. Defaults to False.
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bboxes (list, optional): List of bounding box coordinates for prompted segmentation. Defaults to None.
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points (list, optional): List of points for prompted segmentation. Defaults to None.
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labels (list, optional): List of labels for prompted segmentation. Defaults to None.
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**kwargs: Additional keyword arguments.
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Returns:
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The segmentation masks.
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(list): The model predictions.
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"""
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return self.predict(source, stream, bboxes, points, labels, **kwargs)
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@ -112,6 +110,6 @@ class SAM(Model):
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Provides a mapping from the 'segment' task to its corresponding 'Predictor'.
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Returns:
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dict: A dictionary mapping the 'segment' task to its corresponding 'Predictor'.
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(dict): A dictionary mapping the 'segment' task to its corresponding 'Predictor'.
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"""
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return {'segment': {'predictor': Predictor}}
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@ -77,7 +77,7 @@ class Predictor(BasePredictor):
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im (torch.Tensor | List[np.ndarray]): BCHW tensor format or list of HWC numpy arrays.
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Returns:
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torch.Tensor: The preprocessed image tensor.
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(torch.Tensor): The preprocessed image tensor.
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"""
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if self.im is not None:
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return self.im
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@ -105,7 +105,7 @@ class Predictor(BasePredictor):
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im (List[np.ndarray]): List containing images in HWC numpy array format.
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Returns:
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List[np.ndarray]: List of transformed images.
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(List[np.ndarray]): List of transformed images.
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"""
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assert len(im) == 1, 'SAM model does not currently support batched inference'
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letterbox = LetterBox(self.args.imgsz, auto=False, center=False)
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@ -126,7 +126,7 @@ class Predictor(BasePredictor):
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multimask_output (bool, optional): Flag to return multiple masks. Helpful for ambiguous prompts. Defaults to False.
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Returns:
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tuple: Contains the following three elements.
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(tuple): Contains the following three elements.
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- np.ndarray: The output masks in shape CxHxW, where C is the number of generated masks.
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- np.ndarray: An array of length C containing quality scores predicted by the model for each mask.
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- np.ndarray: Low-resolution logits of shape CxHxW for subsequent inference, where H=W=256.
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@ -155,7 +155,7 @@ class Predictor(BasePredictor):
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multimask_output (bool, optional): Flag to return multiple masks. Helpful for ambiguous prompts. Defaults to False.
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Returns:
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tuple: Contains the following three elements.
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(tuple): Contains the following three elements.
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- np.ndarray: The output masks in shape CxHxW, where C is the number of generated masks.
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- np.ndarray: An array of length C containing quality scores predicted by the model for each mask.
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- np.ndarray: Low-resolution logits of shape CxHxW for subsequent inference, where H=W=256.
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@ -234,7 +234,7 @@ class Predictor(BasePredictor):
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crop_nms_thresh (float): IoU cutoff for Non-Maximum Suppression (NMS) to remove duplicate masks between crops.
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Returns:
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tuple: A tuple containing segmented masks, confidence scores, and bounding boxes.
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(tuple): A tuple containing segmented masks, confidence scores, and bounding boxes.
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"""
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self.segment_all = True
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ih, iw = im.shape[2:]
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@ -434,7 +434,7 @@ class Predictor(BasePredictor):
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nms_thresh (float): The IoU threshold for the NMS algorithm. Defaults to 0.7.
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Returns:
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T(uple[torch.Tensor, List[int]]):
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(tuple([torch.Tensor, List[int]])):
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- new_masks (torch.Tensor): The processed masks with small regions removed. Shape is (N, H, W).
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- keep (List[int]): The indices of the remaining masks post-NMS, which can be used to filter the boxes.
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"""
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