update

ultralytics/models/fastsam/__init__.py

@@ -0,0 +1,8 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from .model import FastSAM
+from .predict import FastSAMPredictor
+from .prompt import FastSAMPrompt
+from .val import FastSAMValidator
+
+__all__ = "FastSAMPredictor", "FastSAM", "FastSAMPrompt", "FastSAMValidator"

ultralytics/models/fastsam/model.py

@@ -0,0 +1,33 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from pathlib import Path
+
+from ultralytics.engine.model import Model
+from .predict import FastSAMPredictor
+from .val import FastSAMValidator
+
+
+class FastSAM(Model):
+    """
+    FastSAM model interface.
+
+    Example:
+        ```python
+        from ultralytics import FastSAM
+
+        model = FastSAM('last.pt')
+        results = model.predict('ultralytics/assets/bus.jpg')
+        ```
+    """
+
+    def __init__(self, model="FastSAM-x.pt"):
+        """Call the __init__ method of the parent class (YOLO) with the updated default model."""
+        if str(model) == "FastSAM.pt":
+            model = "FastSAM-x.pt"
+        assert Path(model).suffix not in (".yaml", ".yml"), "FastSAM models only support pre-trained models."
+        super().__init__(model=model, task="segment")
+
+    @property
+    def task_map(self):
+        """Returns a dictionary mapping segment task to corresponding predictor and validator classes."""
+        return {"segment": {"predictor": FastSAMPredictor, "validator": FastSAMValidator}}

ultralytics/models/fastsam/predict.py

@@ -0,0 +1,86 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import torch
+
+from ultralytics.engine.results import Results
+from ultralytics.models.fastsam.utils import bbox_iou
+from ultralytics.models.yolo.detect.predict import DetectionPredictor
+from ultralytics.utils import DEFAULT_CFG, ops
+
+
+class FastSAMPredictor(DetectionPredictor):
+    """
+    FastSAMPredictor is specialized for fast SAM (Segment Anything Model) segmentation prediction tasks in Ultralytics
+    YOLO framework.
+
+    This class extends the DetectionPredictor, customizing the prediction pipeline specifically for fast SAM.
+    It adjusts post-processing steps to incorporate mask prediction and non-max suppression while optimizing
+    for single-class segmentation.
+
+    Attributes:
+        cfg (dict): Configuration parameters for prediction.
+        overrides (dict, optional): Optional parameter overrides for custom behavior.
+        _callbacks (dict, optional): Optional list of callback functions to be invoked during prediction.
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """
+        Initializes the FastSAMPredictor class, inheriting from DetectionPredictor and setting the task to 'segment'.
+
+        Args:
+            cfg (dict): Configuration parameters for prediction.
+            overrides (dict, optional): Optional parameter overrides for custom behavior.
+            _callbacks (dict, optional): Optional list of callback functions to be invoked during prediction.
+        """
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = "segment"
+
+    def postprocess(self, preds, img, orig_imgs):
+        """
+        Perform post-processing steps on predictions, including non-max suppression and scaling boxes to original image
+        size, and returns the final results.
+
+        Args:
+            preds (list): The raw output predictions from the model.
+            img (torch.Tensor): The processed image tensor.
+            orig_imgs (list | torch.Tensor): The original image or list of images.
+
+        Returns:
+            (list): A list of Results objects, each containing processed boxes, masks, and other metadata.
+        """
+        p = ops.non_max_suppression(
+            preds[0],
+            self.args.conf,
+            self.args.iou,
+            agnostic=self.args.agnostic_nms,
+            max_det=self.args.max_det,
+            nc=1,  # set to 1 class since SAM has no class predictions
+            classes=self.args.classes,
+        )
+        full_box = torch.zeros(p[0].shape[1], device=p[0].device)
+        full_box[2], full_box[3], full_box[4], full_box[6:] = img.shape[3], img.shape[2], 1.0, 1.0
+        full_box = full_box.view(1, -1)
+        critical_iou_index = bbox_iou(full_box[0][:4], p[0][:, :4], iou_thres=0.9, image_shape=img.shape[2:])
+        if critical_iou_index.numel() != 0:
+            full_box[0][4] = p[0][critical_iou_index][:, 4]
+            full_box[0][6:] = p[0][critical_iou_index][:, 6:]
+            p[0][critical_iou_index] = full_box
+
+        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
+            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
+
+        results = []
+        proto = preds[1][-1] if len(preds[1]) == 3 else preds[1]  # second output is len 3 if pt, but only 1 if exported
+        for i, pred in enumerate(p):
+            orig_img = orig_imgs[i]
+            img_path = self.batch[0][i]
+            if not len(pred):  # save empty boxes
+                masks = None
+            elif self.args.retina_masks:
+                pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
+                masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2])  # HWC
+            else:
+                masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)  # HWC
+                pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
+            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
+        return results

ultralytics/models/fastsam/prompt.py

@@ -0,0 +1,357 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import os
+from pathlib import Path
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from PIL import Image
+
+from ultralytics.utils import TQDM
+
+
+class FastSAMPrompt:
+    """
+    Fast Segment Anything Model class for image annotation and visualization.
+
+    Attributes:
+        device (str): Computing device ('cuda' or 'cpu').
+        results: Object detection or segmentation results.
+        source: Source image or image path.
+        clip: CLIP model for linear assignment.
+    """
+
+    def __init__(self, source, results, device="cuda") -> None:
+        """Initializes FastSAMPrompt with given source, results and device, and assigns clip for linear assignment."""
+        self.device = device
+        self.results = results
+        self.source = source
+
+        # Import and assign clip
+        try:
+            import clip
+        except ImportError:
+            from ultralytics.utils.checks import check_requirements
+
+            check_requirements("git+https://github.com/openai/CLIP.git")
+            import clip
+        self.clip = clip
+
+    @staticmethod
+    def _segment_image(image, bbox):
+        """Segments the given image according to the provided bounding box coordinates."""
+        image_array = np.array(image)
+        segmented_image_array = np.zeros_like(image_array)
+        x1, y1, x2, y2 = bbox
+        segmented_image_array[y1:y2, x1:x2] = image_array[y1:y2, x1:x2]
+        segmented_image = Image.fromarray(segmented_image_array)
+        black_image = Image.new("RGB", image.size, (255, 255, 255))
+        # transparency_mask = np.zeros_like((), dtype=np.uint8)
+        transparency_mask = np.zeros((image_array.shape[0], image_array.shape[1]), dtype=np.uint8)
+        transparency_mask[y1:y2, x1:x2] = 255
+        transparency_mask_image = Image.fromarray(transparency_mask, mode="L")
+        black_image.paste(segmented_image, mask=transparency_mask_image)
+        return black_image
+
+    @staticmethod
+    def _format_results(result, filter=0):
+        """Formats detection results into list of annotations each containing ID, segmentation, bounding box, score and
+        area.
+        """
+        annotations = []
+        n = len(result.masks.data) if result.masks is not None else 0
+        for i in range(n):
+            mask = result.masks.data[i] == 1.0
+            if torch.sum(mask) >= filter:
+                annotation = {
+                    "id": i,
+                    "segmentation": mask.cpu().numpy(),
+                    "bbox": result.boxes.data[i],
+                    "score": result.boxes.conf[i],
+                }
+                annotation["area"] = annotation["segmentation"].sum()
+                annotations.append(annotation)
+        return annotations
+
+    @staticmethod
+    def _get_bbox_from_mask(mask):
+        """Applies morphological transformations to the mask, displays it, and if with_contours is True, draws
+        contours.
+        """
+        mask = mask.astype(np.uint8)
+        contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        x1, y1, w, h = cv2.boundingRect(contours[0])
+        x2, y2 = x1 + w, y1 + h
+        if len(contours) > 1:
+            for b in contours:
+                x_t, y_t, w_t, h_t = cv2.boundingRect(b)
+                x1 = min(x1, x_t)
+                y1 = min(y1, y_t)
+                x2 = max(x2, x_t + w_t)
+                y2 = max(y2, y_t + h_t)
+        return [x1, y1, x2, y2]
+
+    def plot(
+        self,
+        annotations,
+        output,
+        bbox=None,
+        points=None,
+        point_label=None,
+        mask_random_color=True,
+        better_quality=True,
+        retina=False,
+        with_contours=True,
+    ):
+        """
+        Plots annotations, bounding boxes, and points on images and saves the output.
+
+        Args:
+            annotations (list): Annotations to be plotted.
+            output (str or Path): Output directory for saving the plots.
+            bbox (list, optional): Bounding box coordinates [x1, y1, x2, y2]. Defaults to None.
+            points (list, optional): Points to be plotted. Defaults to None.
+            point_label (list, optional): Labels for the points. Defaults to None.
+            mask_random_color (bool, optional): Whether to use random color for masks. Defaults to True.
+            better_quality (bool, optional): Whether to apply morphological transformations for better mask quality. Defaults to True.
+            retina (bool, optional): Whether to use retina mask. Defaults to False.
+            with_contours (bool, optional): Whether to plot contours. Defaults to True.
+        """
+        pbar = TQDM(annotations, total=len(annotations))
+        for ann in pbar:
+            result_name = os.path.basename(ann.path)
+            image = ann.orig_img[..., ::-1]  # BGR to RGB
+            original_h, original_w = ann.orig_shape
+            # For macOS only
+            # plt.switch_backend('TkAgg')
+            plt.figure(figsize=(original_w / 100, original_h / 100))
+            # Add subplot with no margin.
+            plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
+            plt.margins(0, 0)
+            plt.gca().xaxis.set_major_locator(plt.NullLocator())
+            plt.gca().yaxis.set_major_locator(plt.NullLocator())
+            plt.imshow(image)
+
+            if ann.masks is not None:
+                masks = ann.masks.data
+                if better_quality:
+                    if isinstance(masks[0], torch.Tensor):
+                        masks = np.array(masks.cpu())
+                    for i, mask in enumerate(masks):
+                        mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
+                        masks[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))
+
+                self.fast_show_mask(
+                    masks,
+                    plt.gca(),
+                    random_color=mask_random_color,
+                    bbox=bbox,
+                    points=points,
+                    pointlabel=point_label,
+                    retinamask=retina,
+                    target_height=original_h,
+                    target_width=original_w,
+                )
+
+                if with_contours:
+                    contour_all = []
+                    temp = np.zeros((original_h, original_w, 1))
+                    for i, mask in enumerate(masks):
+                        mask = mask.astype(np.uint8)
+                        if not retina:
+                            mask = cv2.resize(mask, (original_w, original_h), interpolation=cv2.INTER_NEAREST)
+                        contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+                        contour_all.extend(iter(contours))
+                    cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2)
+                    color = np.array([0 / 255, 0 / 255, 1.0, 0.8])
+                    contour_mask = temp / 255 * color.reshape(1, 1, -1)
+                    plt.imshow(contour_mask)
+
+            # Save the figure
+            save_path = Path(output) / result_name
+            save_path.parent.mkdir(exist_ok=True, parents=True)
+            plt.axis("off")
+            plt.savefig(save_path, bbox_inches="tight", pad_inches=0, transparent=True)
+            plt.close()
+            pbar.set_description(f"Saving {result_name} to {save_path}")
+
+    @staticmethod
+    def fast_show_mask(
+        annotation,
+        ax,
+        random_color=False,
+        bbox=None,
+        points=None,
+        pointlabel=None,
+        retinamask=True,
+        target_height=960,
+        target_width=960,
+    ):
+        """
+        Quickly shows the mask annotations on the given matplotlib axis.
+
+        Args:
+            annotation (array-like): Mask annotation.
+            ax (matplotlib.axes.Axes): Matplotlib axis.
+            random_color (bool, optional): Whether to use random color for masks. Defaults to False.
+            bbox (list, optional): Bounding box coordinates [x1, y1, x2, y2]. Defaults to None.
+            points (list, optional): Points to be plotted. Defaults to None.
+            pointlabel (list, optional): Labels for the points. Defaults to None.
+            retinamask (bool, optional): Whether to use retina mask. Defaults to True.
+            target_height (int, optional): Target height for resizing. Defaults to 960.
+            target_width (int, optional): Target width for resizing. Defaults to 960.
+        """
+        n, h, w = annotation.shape  # batch, height, width
+
+        areas = np.sum(annotation, axis=(1, 2))
+        annotation = annotation[np.argsort(areas)]
+
+        index = (annotation != 0).argmax(axis=0)
+        if random_color:
+            color = np.random.random((n, 1, 1, 3))
+        else:
+            color = np.ones((n, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 1.0])
+        transparency = np.ones((n, 1, 1, 1)) * 0.6
+        visual = np.concatenate([color, transparency], axis=-1)
+        mask_image = np.expand_dims(annotation, -1) * visual
+
+        show = np.zeros((h, w, 4))
+        h_indices, w_indices = np.meshgrid(np.arange(h), np.arange(w), indexing="ij")
+        indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
+
+        show[h_indices, w_indices, :] = mask_image[indices]
+        if bbox is not None:
+            x1, y1, x2, y2 = bbox
+            ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1))
+        # Draw point
+        if points is not None:
+            plt.scatter(
+                [point[0] for i, point in enumerate(points) if pointlabel[i] == 1],
+                [point[1] for i, point in enumerate(points) if pointlabel[i] == 1],
+                s=20,
+                c="y",
+            )
+            plt.scatter(
+                [point[0] for i, point in enumerate(points) if pointlabel[i] == 0],
+                [point[1] for i, point in enumerate(points) if pointlabel[i] == 0],
+                s=20,
+                c="m",
+            )
+
+        if not retinamask:
+            show = cv2.resize(show, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
+        ax.imshow(show)
+
+    @torch.no_grad()
+    def retrieve(self, model, preprocess, elements, search_text: str, device) -> int:
+        """Processes images and text with a model, calculates similarity, and returns softmax score."""
+        preprocessed_images = [preprocess(image).to(device) for image in elements]
+        tokenized_text = self.clip.tokenize([search_text]).to(device)
+        stacked_images = torch.stack(preprocessed_images)
+        image_features = model.encode_image(stacked_images)
+        text_features = model.encode_text(tokenized_text)
+        image_features /= image_features.norm(dim=-1, keepdim=True)
+        text_features /= text_features.norm(dim=-1, keepdim=True)
+        probs = 100.0 * image_features @ text_features.T
+        return probs[:, 0].softmax(dim=0)
+
+    def _crop_image(self, format_results):
+        """Crops an image based on provided annotation format and returns cropped images and related data."""
+        if os.path.isdir(self.source):
+            raise ValueError(f"'{self.source}' is a directory, not a valid source for this function.")
+        image = Image.fromarray(cv2.cvtColor(self.results[0].orig_img, cv2.COLOR_BGR2RGB))
+        ori_w, ori_h = image.size
+        annotations = format_results
+        mask_h, mask_w = annotations[0]["segmentation"].shape
+        if ori_w != mask_w or ori_h != mask_h:
+            image = image.resize((mask_w, mask_h))
+        cropped_boxes = []
+        cropped_images = []
+        not_crop = []
+        filter_id = []
+        for _, mask in enumerate(annotations):
+            if np.sum(mask["segmentation"]) <= 100:
+                filter_id.append(_)
+                continue
+            bbox = self._get_bbox_from_mask(mask["segmentation"])  # bbox from mask
+            cropped_boxes.append(self._segment_image(image, bbox))  # save cropped image
+            cropped_images.append(bbox)  # save cropped image bbox
+
+        return cropped_boxes, cropped_images, not_crop, filter_id, annotations
+
+    def box_prompt(self, bbox):
+        """Modifies the bounding box properties and calculates IoU between masks and bounding box."""
+        if self.results[0].masks is not None:
+            assert bbox[2] != 0 and bbox[3] != 0
+            if os.path.isdir(self.source):
+                raise ValueError(f"'{self.source}' is a directory, not a valid source for this function.")
+            masks = self.results[0].masks.data
+            target_height, target_width = self.results[0].orig_shape
+            h = masks.shape[1]
+            w = masks.shape[2]
+            if h != target_height or w != target_width:
+                bbox = [
+                    int(bbox[0] * w / target_width),
+                    int(bbox[1] * h / target_height),
+                    int(bbox[2] * w / target_width),
+                    int(bbox[3] * h / target_height),
+                ]
+            bbox[0] = max(round(bbox[0]), 0)
+            bbox[1] = max(round(bbox[1]), 0)
+            bbox[2] = min(round(bbox[2]), w)
+            bbox[3] = min(round(bbox[3]), h)
+
+            # IoUs = torch.zeros(len(masks), dtype=torch.float32)
+            bbox_area = (bbox[3] - bbox[1]) * (bbox[2] - bbox[0])
+
+            masks_area = torch.sum(masks[:, bbox[1] : bbox[3], bbox[0] : bbox[2]], dim=(1, 2))
+            orig_masks_area = torch.sum(masks, dim=(1, 2))
+
+            union = bbox_area + orig_masks_area - masks_area
+            iou = masks_area / union
+            max_iou_index = torch.argmax(iou)
+
+            self.results[0].masks.data = torch.tensor(np.array([masks[max_iou_index].cpu().numpy()]))
+        return self.results
+
+    def point_prompt(self, points, pointlabel):  # numpy
+        """Adjusts points on detected masks based on user input and returns the modified results."""
+        if self.results[0].masks is not None:
+            if os.path.isdir(self.source):
+                raise ValueError(f"'{self.source}' is a directory, not a valid source for this function.")
+            masks = self._format_results(self.results[0], 0)
+            target_height, target_width = self.results[0].orig_shape
+            h = masks[0]["segmentation"].shape[0]
+            w = masks[0]["segmentation"].shape[1]
+            if h != target_height or w != target_width:
+                points = [[int(point[0] * w / target_width), int(point[1] * h / target_height)] for point in points]
+            onemask = np.zeros((h, w))
+            for annotation in masks:
+                mask = annotation["segmentation"] if isinstance(annotation, dict) else annotation
+                for i, point in enumerate(points):
+                    if mask[point[1], point[0]] == 1 and pointlabel[i] == 1:
+                        onemask += mask
+                    if mask[point[1], point[0]] == 1 and pointlabel[i] == 0:
+                        onemask -= mask
+            onemask = onemask >= 1
+            self.results[0].masks.data = torch.tensor(np.array([onemask]))
+        return self.results
+
+    def text_prompt(self, text):
+        """Processes a text prompt, applies it to existing results and returns the updated results."""
+        if self.results[0].masks is not None:
+            format_results = self._format_results(self.results[0], 0)
+            cropped_boxes, cropped_images, not_crop, filter_id, annotations = self._crop_image(format_results)
+            clip_model, preprocess = self.clip.load("ViT-B/32", device=self.device)
+            scores = self.retrieve(clip_model, preprocess, cropped_boxes, text, device=self.device)
+            max_idx = scores.argsort()
+            max_idx = max_idx[-1]
+            max_idx += sum(np.array(filter_id) <= int(max_idx))
+            self.results[0].masks.data = torch.tensor(np.array([annotations[max_idx]["segmentation"]]))
+        return self.results
+
+    def everything_prompt(self):
+        """Returns the processed results from the previous methods in the class."""
+        return self.results

ultralytics/models/fastsam/utils.py

@@ -0,0 +1,67 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import torch
+
+
+def adjust_bboxes_to_image_border(boxes, image_shape, threshold=20):
+    """
+    Adjust bounding boxes to stick to image border if they are within a certain threshold.
+
+    Args:
+        boxes (torch.Tensor): (n, 4)
+        image_shape (tuple): (height, width)
+        threshold (int): pixel threshold
+
+    Returns:
+        adjusted_boxes (torch.Tensor): adjusted bounding boxes
+    """
+
+    # Image dimensions
+    h, w = image_shape
+
+    # Adjust boxes
+    boxes[boxes[:, 0] < threshold, 0] = 0  # x1
+    boxes[boxes[:, 1] < threshold, 1] = 0  # y1
+    boxes[boxes[:, 2] > w - threshold, 2] = w  # x2
+    boxes[boxes[:, 3] > h - threshold, 3] = h  # y2
+    return boxes
+
+
+def bbox_iou(box1, boxes, iou_thres=0.9, image_shape=(640, 640), raw_output=False):
+    """
+    Compute the Intersection-Over-Union of a bounding box with respect to an array of other bounding boxes.
+
+    Args:
+        box1 (torch.Tensor): (4, )
+        boxes (torch.Tensor): (n, 4)
+        iou_thres (float): IoU threshold
+        image_shape (tuple): (height, width)
+        raw_output (bool): If True, return the raw IoU values instead of the indices
+
+    Returns:
+        high_iou_indices (torch.Tensor): Indices of boxes with IoU > thres
+    """
+    boxes = adjust_bboxes_to_image_border(boxes, image_shape)
+    # Obtain coordinates for intersections
+    x1 = torch.max(box1[0], boxes[:, 0])
+    y1 = torch.max(box1[1], boxes[:, 1])
+    x2 = torch.min(box1[2], boxes[:, 2])
+    y2 = torch.min(box1[3], boxes[:, 3])
+
+    # Compute the area of intersection
+    intersection = (x2 - x1).clamp(0) * (y2 - y1).clamp(0)
+
+    # Compute the area of both individual boxes
+    box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
+    box2_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
+
+    # Compute the area of union
+    union = box1_area + box2_area - intersection
+
+    # Compute the IoU
+    iou = intersection / union  # Should be shape (n, )
+    if raw_output:
+        return 0 if iou.numel() == 0 else iou
+
+    # return indices of boxes with IoU > thres
+    return torch.nonzero(iou > iou_thres).flatten()

ultralytics/models/fastsam/val.py

@@ -0,0 +1,40 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from ultralytics.models.yolo.segment import SegmentationValidator
+from ultralytics.utils.metrics import SegmentMetrics
+
+
+class FastSAMValidator(SegmentationValidator):
+    """
+    Custom validation class for fast SAM (Segment Anything Model) segmentation in Ultralytics YOLO framework.
+
+    Extends the SegmentationValidator class, customizing the validation process specifically for fast SAM. This class
+    sets the task to 'segment' and uses the SegmentMetrics for evaluation. Additionally, plotting features are disabled
+    to avoid errors during validation.
+
+    Attributes:
+        dataloader: The data loader object used for validation.
+        save_dir (str): The directory where validation results will be saved.
+        pbar: A progress bar object.
+        args: Additional arguments for customization.
+        _callbacks: List of callback functions to be invoked during validation.
+    """
+
+    def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
+        """
+        Initialize the FastSAMValidator class, setting the task to 'segment' and metrics to SegmentMetrics.
+
+        Args:
+            dataloader (torch.utils.data.DataLoader): Dataloader to be used for validation.
+            save_dir (Path, optional): Directory to save results.
+            pbar (tqdm.tqdm): Progress bar for displaying progress.
+            args (SimpleNamespace): Configuration for the validator.
+            _callbacks (dict): Dictionary to store various callback functions.
+
+        Notes:
+            Plots for ConfusionMatrix and other related metrics are disabled in this class to avoid errors.
+        """
+        super().__init__(dataloader, save_dir, pbar, args, _callbacks)
+        self.args.task = "segment"
+        self.args.plots = False  # disable ConfusionMatrix and other plots to avoid errors
+        self.metrics = SegmentMetrics(save_dir=self.save_dir, on_plot=self.on_plot)