: Satellite imagery, aerial photography, and drone mapping.
SAMtool has evolved from a simple inference script into a robust model hub. By supporting everything from the 5MB MobileSAM to the 2.6GB SAM-H via quantization, it democratizes access to state-of-the-art segmentation. However, success depends on matching the model to your hardware constraints.
SamsTool provides exhaustive support for Exynos chips, utilizing the to perform deep-system operations without unlocking the bootloader first. Supported Exynos processors include:
[Generated by AI] Affiliation: Computational Genomics Laboratory Date: October 2023 samtool supported models
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| Use Case | Recommended Model | SAMtool Flag | | :--- | :--- | :--- | | Real-time web app (CPU) | MobileSAM | --model-type mobile_sam | | High-res satellite imagery | SAM2 Hiera-Large | --model-type sam2_hiera_large | | Limited VRAM (6GB GPU) | SAM-H + INT8 quantized | --quantize int8 | | Medical imaging (MRI/CT) | MedSAM | --model-type medsam | | Video object segmentation | SAM2.1 (Hiera-Base) | --model-type sam2.1_hiera_b |
The package is part of the openMSE collection and is actively maintained, with version 1.9.1 released in January 2026. : Satellite imagery, aerial photography, and drone mapping
Debugging, manual inspection, and script-based parsing. BAM (Binary Alignment/Map) Type: Binary compressed representation of SAM.
The most valuable feature of a Samtool library is its simple and consistent API. Instead of requiring a deep understanding of the underlying model's architecture and tensors, a developer can use a few lines of code.
While SAMTool by jjshoots is currently based on SAM 1, the broader ecosystem of SAM-based tools now supports the newer SAM 2 and SAM 3 models. However, success depends on matching the model to
A future-oriented "Samtool" would naturally evolve to support these emerging model variants.
Meta AI released SAM with a vision transformer (ViT) backbone trained on the massive SA-1B dataset. The original release includes three primary model sizes, balancing computational cost against segmentation accuracy. ViT-B (Base) : ~91 million