segment-anything

Generates precise object segmentation masks from images using a vision transformer encoder-decoder architecture that accepts flexible prompts (points, bounding boxes, text descriptions, or mask hints). The model uses a two-stage process: an image encoder processes the full image into embeddings, then a lightweight mask decoder generates segmentation masks conditioned on prompt embeddings, enabling real-time inference without task-specific fine-tuning.

Generates multiple candidate segmentation masks for a single image and ranks them by model confidence, allowing users or downstream systems to select the most appropriate mask or iteratively refine masks by adding positive/negative prompts. The decoder outputs IoU predictions alongside masks, enabling confidence-based filtering and automatic selection of high-quality masks without manual review.

Generates class-agnostic segmentation masks (no class labels) that can be post-processed to produce semantic or instance segmentation by applying clustering, connected-component analysis, or external classifiers. The model outputs masks without semantic information, enabling flexible downstream classification and enabling use cases where class information is not available at inference time.

Pre-computes and caches image embeddings using a frozen ViT encoder (ViT-B, ViT-L, or ViT-H variants), enabling fast mask decoding for multiple prompts on the same image without re-encoding. The encoder processes images at 1024x1024 resolution and outputs 64x64 feature maps; embeddings are cached in memory or disk, reducing per-prompt latency from ~500ms to ~50-100ms.

Processes multiple images and prompts in batches, supporting mixed prompt types (some images with point prompts, others with boxes or masks) in a single forward pass. The implementation pads prompts to a fixed size and uses attention masking to ignore padding tokens, enabling efficient GPU utilization without requiring homogeneous prompt types across the batch.

Applies morphological operations (erosion, dilation, opening, closing) and contour-based filtering to refine raw model outputs, removing noise, filling holes, and smoothing boundaries. Post-processing is configurable and can be applied selectively based on mask quality estimates (IoU predictions), enabling automatic quality improvement without manual tuning.

Processes images at multiple scales (0.5x, 1.0x, 2.0x original resolution) and combines predictions using ensemble voting or confidence-weighted averaging, improving robustness to scale variations and small object detection. The implementation reuses cached embeddings at the base scale and computes additional embeddings for upsampled/downsampled variants, trading memory for improved accuracy.

Enables interactive segmentation where users click on image regions to provide positive/negative point prompts, with real-time mask updates after each click. The implementation maintains a prompt history and iteratively refines masks by accumulating prompts, using the previous mask as a hint for the next iteration to improve consistency and reduce flicker.

Accepts bounding box prompts (x_min, y_min, x_max, y_max) and generates segmentation masks for objects within the box. The implementation can automatically refine boxes by detecting object boundaries within the box region, or generate multiple masks for ambiguous boxes, enabling coarse-to-fine segmentation workflows.

Accepts a previous segmentation mask as input and uses it as a hint to refine or extend segmentation in subsequent iterations. The mask is encoded alongside point/box prompts and passed to the decoder, enabling iterative refinement where each iteration builds on the previous mask, useful for correcting errors or extending segmentation to new regions.

Provides three pre-trained model variants (ViT-B, ViT-L, ViT-H) with different speed-accuracy tradeoffs, enabling users to select the appropriate model for their hardware and latency constraints. The implementation includes model loading, quantization support (int8, fp16), and export to ONNX/TorchScript for deployment on edge devices and cloud infrastructure.