CASIA-LMC-Lab/FastSAM
FastSAM
FastSAM is an image segmentation framework that uses convolutional neural networks to isolate visual elements and generate masks for detectable objects within images. It provides a system for both automatic all-object segmentation and promptable image segmentation.
The project utilizes an inference-optimized architecture to reduce computational overhead, enabling faster mask generation and real-time visual analysis. It supports the creation of precise masks through various prompt inputs, including points, bounding boxes, and text descriptions.
The framework covers broader computer vision capabilities such as dataset labeling, interactive object masking, and model training and validation against ground-truth datasets.
Features
- Image Segmenters - Is a deep learning tool that isolates visual elements and objects using convolutional neural networks.
- Image Segmentation - Automatically partitions images into distinct regions for all detectable objects without manual markers.
- Language-Based Segmentation - Identifies specific objects using points, boxes, or text descriptions to create precise masks.
- Object Mask Generators - Provides an automatic mask generation system that identifies every detectable object in an image without manual prompts.
- Binary Mask Generators - Implements a convolutional neural network to generate pixel-level binary masks for object isolation.
- Inference Optimization - Employs an inference-optimized architecture to reduce computational overhead and accelerate mask generation.
- Image Segmentations - Uses deep learning convolutional networks to partition images into foreground and background pixels for object isolation.
- SAM-Based Implementations - Implements a fast segmentation model using the Segment Anything Model architecture for precise mask generation.
- Promptable Segmentation Tools - Provides a system for creating segmentation masks based on point, bounding box, or text descriptions.
- Image Masking Frameworks - Ships a framework for performing pixel-level classification to isolate all detectable objects.
- Computer Vision Annotation - Provides capabilities to isolate visual elements for the purpose of creating training data or validating model performance.
- Segmentation Model Training - Provides a framework for training instance segmentation models and validating their performance against datasets.
- Model Prediction Evaluation - Includes utilities to evaluate segmentation accuracy by comparing model predictions against ground-truth benchmark datasets.
- Real-Time Visual Stream Processors - Enables high-speed identification and separation of objects for real-time visual analysis.
