Objective:
To explore and practice the capabilities of the SAM model and associated libraries, focusing on understanding the various tasks that can be accomplished. This exercise aims to familiarize contributors with SAM's functionality and potential applications in segmentation and object detection.

Task Description:
The task involves utilizing the 'segment_anything_inference_practice.ipynb' notebook as a sandbox to experiment with the SAM model. It aims to explore different features of the SAM model and its libraries, experimenting with various settings and scenarios. This exploration should serve as a hands-on learning experience to gain insights into the model's versatility and application range.

Required Tasks:

• Experiment with the SAM model using the provided notebook, exploring its various functionalities.
• Document the experiments conducted, including the settings used and the outcomes observed.
• Identify and note any limitations or challenges encountered during the experimentation process.
• Share insights or interesting findings that could guide future projects or experiments with the SAM model.

This issue aims to be a collaborative learning journey, encouraging contributors to share their experiences, tips, and any useful resources discovered along the way.