Segment Anything
by bCloud LLC
Version 1.0.0 + Free Support on Ubuntu 26.04
Segment Anything (SAM 3.1) is an AI-based image segmentation solution developed by Meta that enables developers and researchers to identify, segment, and analyze objects within images. It is designed for computer vision workflows and supports flexible object segmentation using advanced deep learning models.
The solution supports image segmentation, object mask generation, visual content analysis, and AI-powered computer vision workflows. It is suitable for AI/ML development, image processing, research, automation, and computer vision applications.
Version: 3.1.0
Python Package Version: 0.1.0
Features of Segment Anything:
- AI-powered image and object segmentation.
- Generates accurate segmentation masks for objects within images.
- Supports advanced computer vision and image analysis workflows.
- Built using PyTorch for machine learning and deep learning applications.
- Supports integration with Python-based AI and ML applications.
- Suitable for research, image processing, automation, and visual analysis use cases.
Usage instructions for Segment Anything:
$ sudo su
$ cd /opt/sam3/source
$ /opt/sam3/miniconda/bin/conda run -n sam3 python -c "import sam3; print('SAM3 Import: OK')"
$ /opt/sam3/miniconda/bin/conda run -n sam3 pip show sam3
Installation Location: /opt/sam3/source
Python Environment: /opt/sam3/miniconda/envs/sam3
Verify Segment Anything installation:
/opt/sam3/miniconda/bin/conda run -n sam3 python -c "import sam3; print('SAM3 Import: OK')"
/opt/sam3/miniconda/bin/conda run -n sam3 python -c "import torch; print('PyTorch:', torch.__version__)"
Segment Anything is a Python/CLI-based solution and does not require a default network port.
Disclaimer: Segment Anything is provided under its applicable license terms. Users are responsible for reviewing the applicable Meta license, model usage requirements, secure deployment configuration, and validation of model outputs. Performance and hardware requirements may vary depending on the workload, model configuration, and deployment environment.