Answer with Step by Step Explanation

Instance Segmentation Model Implementation

2. • Use the SAM model (Segment Anything Model) to generate segmentation masks for each bounding box in the detection dataset.

3. Data Preprocessing and Augmentation:

5. Model Evaluation:

   • Visualize the performance using metrics such as Precision, Recall, Intersection over Union (IoU), and Mean Average Precision (mAP).

• Explore the Roboflow Universe to find a suitable annotated detection dataset relevant to your object segmentation task.

Step 2: Segmentation Mask Generation

masks = model.generate_masks(detection_dataset)

Step 3: Data Preprocessing and Augmentation

def preprocess(image):

image = cv2.resize(image, (640, 640))

A.HorizontalFlip(p=0.5),

A.RandomRotate90(p=0.5),

from sklearn.model_selection import train_test_split

train_data, test_data = train_test_split(dataset, test_size=0.2, random_state=42)

model = YOLO('yolov8.yaml')

model.train(data='path/to/dataset', epochs=50)

recall = results['recall']

iou = results['iou']

for result in results:

result.show() # This will display the segmented results

import matplotlib.pyplot as plt

from PIL import Image

fig, axs = plt.subplots(1, 2, figsize=(10, 5))

axs[0].imshow(source_image)