Meta’s new Segment Anything Model has been revealed. SAM models are a new way to create high-quality masks for image segmentation.
reminder: Image segmentation is a fundamental task in computer vision aimed at dividing an image into regions corresponding to different objects or semantic categories, and is used in many applications such as object detection, scene understanding, image editing, and video analysis. There are uses.
However, image segmentation is also a difficult problem, especially when dealing with complex scenes containing multiple objects with different shapes, sizes, and appearances. Moreover, most existing image segmentation methods require large amounts of annotated data for training, which can be costly and time-consuming to acquire. Meta wants to solve this problem with the SAM model.
SAM Model: What is Meta’s New Segment Anything Model?
The Segment Anything Model (SAM) is a new and powerful artificial intelligence model that can segment any object in an image or video efficiently with high quality. Segmentation is the process of separating an object from the background and other objects and creating a mask that outlines its shape and boundaries. SAM models facilitate the tasks of editing, synthesizing, tracking, recognition, and analysis.
SAM differs from other segmentation models in several ways:
- SAM is promptable. This means that you can specify which objects to segment using different input prompts such as points and boxes. For example, if you draw a box around a person’s face, the Segment Anything Model will generate a mask for the face. You can also display multiple prompts to split multiple objects at once. SAM models can handle complex scenes with occlusions, reflections and shadows.
- SAM is trained on a large dataset of 11 million images and 1.1 billion masks, the largest segmentation dataset to date. This dataset covers a wide range of objects and categories, including animals, plants, vehicles, furniture, and food. Thanks to its generalization power and data diversity, SAM can segment never-before-seen objects.
- SAM exhibits strong zero-shot performance in various segmentation tasks. Zero-shot means that SAM can segment objects without additional training or fine-tuning on a specific task or domain. For example, SAM can segment faces, hands, hair, clothing, and accessories without prior knowledge or supervision. SAM can also segment objects with different modalities, such as infrared images and depth maps.
SAM models have achieved impressive results on various image segmentation benchmarks such as COCO. SAM also outperforms or is comparable to previous fully supervised methods on several zero-shot segmentation tasks, such as segmenting logos, text, faces, or sketches. It shows its versatility and robustness in different domains and scenarios.
future: The Segment Anything Model (SAM model) project is still in its early stages. According to Meta, these are some of the future applications of the Segment Anything model.
- Future AR glasses may employ SAMs to recognize mundane objects and provide helpful reminders and instructions.
- SAM has the ability to influence many other fields, such as agriculture and biology. Someday, farmers and scientists might benefit too.
SAM models have the potential to be breakthroughs in computer vision and artificial intelligence research. This demonstrates the potential of Vision’s underlying model, a model that can learn from large-scale data and migrate to new tasks and domains.
Capabilities of the Segment Anything Model (SAM Model)
Here are some of the features of the SAM model:
- SAM models allow you to quickly and easily segment objects by selecting individual points to include or exclude in the segmentation. Bounding boxes can also be used as cues for the model.
- If there is uncertainty about the item being segmented, the SAM model can generate many valid masks. This is an important and important skill for solving segmentation in the real world.
- Automatic object detection and masking made simple with the Segment Anything Model.
- After precomputing image embeddings, Segment Anything Model provides segmentation masks on the fly for any prompt, allowing real-time interaction with the model.
Impressive, isn’t it? So what is the technology behind it?
How does the SAM model work?
One of the most interesting discoveries in NLP, and more recently in computer vision, is the use of a “prompt” approach that allows zero-shot and few-shot learning on new datasets and tasks using underlying models. Meta found motivation in this area.
Given foreground/background points, rough boxes or masks, free-form text, or any other input that dictates what to segment in an image, the Meta AI team provides the Segment Anything Model with an appropriate segmentation mask. was taught to generate The need for a proper mask only means that the output is a proper mask for one of the things the prompt might refer to (e.g. a shirt point is a shirt or a can represent any person). This task is used to pre-train the model and to guide solutions to common downstream segmentation problems.
Meta found that pre-training tasks and interactive data collection impose certain limitations on model building. In particular, their annotators should be able to effectively consume the Segment Anything Model interactively in the browser, in real-time, and on the CPU. Despite the fact that meeting runtime requirements requires some compromise between quality and speed, they have found that a simple approach yields satisfactory results.
On the backend, image encoders create unique embeddings for images, while lightweight encoders can convert queries to embedding vectors on the fly. A lightweight decoder is then used to merge these two data sources and predict the segmentation mask. After the image embeddings are computed, SAM can respond to all her web browser queries with segments in about 50ms.
SAM is a useful tool for creative professionals and hobbyists who want easy and flexible editing of images and videos. But first, you need to learn how to access and use it.
How to use the Segment Anything Model (SAM Model)
SAM is developed by Meta AI Research (formerly Facebook AI Research) and published on GitHub. You can try SAM online with a demo, or you can download the dataset of 1 billion masks and 11 million images (SA-1B). This model is very easy to use. Please follow these steps:
- Download the demo or visit the Segment Anything Model demo.
- Upload an image or select one from your gallery.
- Additions and Subject Areas
- Add points to mask areas.[エリアの追加]and select an object.[領域の削除]to adjust the mask and select an area.
Then complete the task as required!
Click here for more information.
Image courtesy: meta
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