1. Brief introduction
Fundamental models in artificial intelligence are becoming increasingly important. They are defined as large AI models trained on large amounts of data that can be adapted to a wide range of tasks.
Early examples of foundational models are large language models (LLMs) such as GPT and BERT. Subsequently, the industry has seen the same idea applied to multimodal base models such as DALLE, CLIP, etc. The term base model is starting to be practiced in the field of NLP and is picking up pace further.
Segment Anything is a project of Meta, which aims to build a starting point for the basic model of image segmentation, and its ambition is also evident.
The project mainly consists of two important components:
1. Large datasets for image segmentation
2. Segment any model (SAM) as the (interactive) base model for image segmentation
This project is also inspired by the field of NLP,
Since image segmentation is one of the core tasks of computer vision, the creators chose a huge model and dataset as a starting point. Image segmentation has many uses in science and artificial intelligence.
These include analyzing biomedical images, editing photos, and autonomous driving, among others. To solve any of these problems, you must train a specialized model that can only perform one task. This requires extensive domain knowledge, and the time required for specific data collection, not to mention the training time required for deep learning models.
The Segment Anything project aims to make image segmentation more general.
2. Segment Anything model
Usually deep learning methods require dedicated training data collection,