diff --git a/annolid/segmentation/SAM/efficient_sam.py b/annolid/segmentation/SAM/efficient_sam.py
new file mode 100644
index 00000000..16fcc040
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+++ b/annolid/segmentation/SAM/efficient_sam.py
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+import matplotlib.pyplot as plt
+import cv2
+import numpy as np
+import torch
+from torchvision.transforms import ToTensor
+
+
+class EfficientSAM:
+    """
+    Class for EfficientSAM segmentation using TorchScript models.
+    Reference: 
+    https://github.com/yformer/EfficientSAM
+    @article{xiong2023efficientsam,
+  title={EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything},
+  author={Yunyang Xiong, Bala Varadarajan, Lemeng Wu, Xiaoyu Xiang, Fanyi Xiao, Chenchen Zhu,
+    Xiaoliang Dai, Dilin Wang, Fei Sun, Forrest Iandola, Raghuraman Krishnamoorthi, Vikas Chandra},
+  journal={arXiv:2312.00863},
+  year={2023}
+}
+    """
+
+    def __init__(self, model_path):
+        """
+        Initialize the EfficientSAM class.
+
+        Parameters:
+        - model_path (str): Path to the TorchScript model file.
+        """
+        self.device = torch.device(
+            "cuda" if torch.cuda.is_available() else "cpu")
+        self.model = self._load_model(model_path).to(self.device)
+
+    def _load_model(self, model_path):
+        """
+        Load the TorchScript model from the given path.
+
+        Parameters:
+        - model_path (str): Path to the TorchScript model file.
+
+        Returns:
+        - torch.jit.ScriptModule: Loaded TorchScript model.
+        """
+        try:
+            # Download and load the model
+            return torch.jit.load(model_path)
+        except Exception as e:
+            raise ValueError(f"Error loading the model: {e}")
+
+    def _preprocess_image(self, image_path):
+        """
+        Preprocess the input image.
+
+        Parameters:
+        - image_path (str): Path to the input image.
+
+        Returns:
+        - torch.Tensor: Preprocessed image tensor.
+        """
+        image = cv2.imread(image_path)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        img_tensor = ToTensor()(image)
+        return img_tensor[None, ...].to(self.device)
+
+    def _get_mask_and_iou(self, img_tensor, points_sampled, labels):
+        """
+        Get segmentation mask and IoU predictions.
+
+        Parameters:
+        - img_tensor (torch.Tensor): Preprocessed image tensor.
+        - points_sampled (torch.Tensor): Sampled points tensor.
+        - labels (torch.Tensor): Tensor containing labels.
+
+        Returns:
+        - Tuple[np.ndarray, np.ndarray]: Segmentation masks and IoU predictions.
+        """
+        predicted_logits, predicted_iou = self.model(
+            img_tensor, points_sampled.to(self.device), labels.to(self.device))
+        predicted_logits = predicted_logits.cpu()
+        all_masks = torch.ge(torch.sigmoid(
+            predicted_logits[0, 0, :, :, :]), 0.5).numpy()
+        predicted_iou = predicted_iou[0, 0, ...].cpu().detach().numpy()
+        return all_masks, predicted_iou
+
+    def run_ours_point(self, image_path, points_sampled):
+        """
+        Run point segmentation.
+
+        Parameters:
+        - image_path (str): Path to the input image.
+        - points_sampled (np.ndarray): Sampled points.
+
+        Returns:
+        - np.ndarray: Selected mask using predicted IoU.
+        """
+        img_tensor = self._preprocess_image(image_path)
+        points_sampled = torch.reshape(torch.tensor(points_sampled), [
+                                       1, 1, -1, 2]).to(self.device)
+        max_num_pts = points_sampled.shape[2]
+        labels = torch.ones(1, 1, max_num_pts).to(self.device)
+
+        all_masks, predicted_iou = self._get_mask_and_iou(
+            img_tensor, points_sampled, labels)
+
+        max_predicted_iou = -1
+        selected_mask_using_predicted_iou = None
+        for m in range(all_masks.shape[0]):
+            curr_predicted_iou = predicted_iou[m]
+            if curr_predicted_iou > max_predicted_iou or selected_mask_using_predicted_iou is None:
+                max_predicted_iou = curr_predicted_iou
+                selected_mask_using_predicted_iou = all_masks[m]
+
+        return selected_mask_using_predicted_iou
+
+    def run_ours_box(self, image_path, points_sampled):
+        """
+        Run box segmentation.
+
+        Parameters:
+        - image_path (str): Path to the input image.
+        - points_sampled (np.ndarray): Sampled points.
+
+        Returns:
+        - np.ndarray: Selected mask using predicted IoU.
+        """
+        img_tensor = self._preprocess_image(image_path)
+        bbox = torch.reshape(torch.tensor(points_sampled),
+                             [1, 1, 2, 2]).to(self.device)
+        bbox_labels = torch.reshape(torch.tensor(
+            [2, 3]), [1, 1, 2]).to(self.device)
+
+        all_masks, predicted_iou = self._get_mask_and_iou(
+            img_tensor, bbox, bbox_labels)
+
+        max_predicted_iou = -1
+        selected_mask_using_predicted_iou = None
+        for m in range(all_masks.shape[0]):
+            curr_predicted_iou = predicted_iou[m]
+            if curr_predicted_iou > max_predicted_iou or selected_mask_using_predicted_iou is None:
+                max_predicted_iou = curr_predicted_iou
+                selected_mask_using_predicted_iou = all_masks[m]
+
+        return selected_mask_using_predicted_iou
+
+    @staticmethod
+    def show_anns_ours(mask, ax):
+        """
+        Show segmentation annotations.
+
+        Parameters:
+        - mask (np.ndarray): Segmentation mask.
+        - ax (matplotlib.axes.Axes): Matplotlib axes to display the mask.
+        """
+        ax.set_autoscale_on(False)
+        img = np.ones((mask[0].shape[0], mask[0].shape[1], 4))
+        img[:, :, 3] = 0
+        for ann in mask:
+            m = ann
+            color_mask = np.concatenate([np.random.random(3), [0.5]])
+            img[m] = color_mask
+        ax.imshow(img)