For a moving industrial inspection line, start with FastSAM if you need throughput above ~20 FPS on a single GPU and your defects are visually distinct; use MobileSAM if you are edge-constrained on a Jetson-class device and need better boundary fidelity than FastSAM’s CNN encoder gives you. Reserve the original SAM (ViT-H) for offline labeling, ground-truth generation, or low-volume high-precision stations where latency is not a constraint. Why isn’t the original SAM a drop-in inspection segmenter? The most common integration mistake is treating SAM as a full-scene segmenter. It is not. SAM is prompt-driven: it produces one mask per point or box prompt you feed it. If you want it to segment “everything” you either run its automatic mask generator — which grids the image with a dense point prompt array and is expensive — or you supply prompts from somewhere else. For a production line, that “somewhere else” is an upstream detector. You run a lightweight object or region detector (a small YOLO variant, a blob detector, or even fixed ROIs from a jig-registered part), then hand those boxes to SAM as prompts. SAM refines them into pixel-accurate masks. This two-stage pattern is the realistic deployment shape — SAM alone will not tell you where the defects or parts are, and its automatic mode is too slow for most inline budgets. Understanding this changes the model-selection question. You are not choosing “the best segmenter”; you are choosing the mask-refinement stage in a detect-then-segment pipeline, and its latency stacks on top of your detector’s latency. How much speed do MobileSAM and FastSAM actually buy you? Both MobileSAM and FastSAM exist because the ViT-H encoder in the original SAM dominates its runtime. The heavy lifting is the image encoder, not the mask decoder, so both variants attack the encoder. MobileSAM keeps SAM’s architecture and prompt interface but replaces the ViT-H image encoder with a distilled, much smaller encoder. The prompt encoder and mask decoder are unchanged, so it behaves like SAM with the same prompting semantics. FastSAM takes a different route: it reframes segment-anything as an instance-segmentation problem on a YOLOv8-style CNN backbone, then filters masks by prompt afterward. In practice, both deliver roughly a 5–7x inference speedup over the original SAM ViT-H backbone as reported in the MobileSAM and FastSAM papers, trading some segmentation quality for it. In the inspection deployments we’ve profiled, that speedup is real but it is the encoder swap that buys it — the mask decoder and prompt interface stay close to SAM’s own behaviour, which is why MobileSAM’s distillation tends to preserve mask fidelity closer to SAM on fine boundaries; FastSAM’s CNN-based approach is faster still on full-scene generation but is weaker on thin structures, small defects, and cluttered edges — exactly the failure modes that matter for scratch, crack, or hairline-solder inspection. The quality gap is not uniform. On large, high-contrast regions all three converge. The differences show up on the hard cases, which for inspection are usually the cases you actually care about. Which variant fits which inspection scenario? Criterion SAM (ViT-H) MobileSAM FastSAM Relative inference speed 1x (baseline) ~5–7x ~5–7x Encoder type ViT-H (heavy) Distilled small ViT YOLOv8-style CNN Prompt semantics Point/box, SAM-native Point/box, SAM-native Prompt-filtered instances Fine-boundary fidelity Best Good Weakest Small-defect sensitivity Best Good Fair Edge/embedded suitability Poor Good Fair–Good Best inline throughput target Offline / low-volume ~15–30 FPS edge >20 FPS GPU Primary use Labeling, ground truth Edge inspection High-throughput inline Read the table against your bottleneck. If your line cadence gives the vision system a fixed per-part budget — say 30 ms — and you have already spent 10 ms on detection, your segmentation stage has 20 ms. That number, plus your target hardware, usually decides the variant before quality does. What should you verify before committing to a variant? Run this checklist on your own defect images, not on public benchmarks. Segment-anything benchmark scores are dominated by natural-image classes that do not resemble machined surfaces, PCBs, or textiles. Measure end-to-end latency, not encoder latency in isolation — include the detector, prompt formatting, and mask post-processing. Test on your hardest defect class first. If FastSAM misses hairline cracks in a 50-image spot check, no throughput advantage rescues it. Check mask stability across frames. For moving lines, an unstable boundary between consecutive frames creates false measurement variance. Confirm the prompt source is reliable. SAM-family quality is capped by the box you feed it; a sloppy detector produces sloppy masks regardless of variant. Profile on the target device, not a workstation GPU. MobileSAM’s advantage over FastSAM is most visible on constrained edge accelerators. Decide fixed-ROI vs. dynamic prompting early — a jig-registered part with fixed positions may not need a detector at all, which changes the latency math. If the spot check shows all three variants pass on quality, choose the fastest one that runs on your existing hardware. If only SAM passes on quality, your problem is likely too fine-grained for the distilled variants and you should either upscale the ROI crop before segmentation or reconsider whether pixel-accurate segmentation is the right tool versus a classifier on detected boxes. We treat this model choice as one input to a wider feasibility read on when industrial computer vision inspection actually works, which scopes whether inline segmentation is warranted at all versus simpler detection or anomaly-scoring approaches — part of the broader computer vision engineering work we do before a client commits to a pilot. For the sensor and optics groundwork any detect-then-segment pipeline depends on, see how a machine vision camera works in industrial inspection; for where inspection fits inside the broader quality discipline, see quality control vs quality assurance: where CV inspection fits. Frequently Asked Questions Can I use SAM without an object detector? Only in fixed-geometry setups where the regions of interest are known in advance — for example, parts held in a jig at repeatable positions, where you can hard-code box prompts. In any scenario with variable part position or unknown defect location, SAM needs an upstream detector to generate prompts, because it produces a mask per prompt rather than segmenting the whole scene autonomously. Is FastSAM always faster than MobileSAM? Not reliably. Both deliver roughly a 5–7x speedup over the original SAM ViT-H backbone, and their relative ranking depends on your hardware and whether you run full-scene generation or single-prompt refinement. FastSAM’s CNN backbone often wins on full-scene throughput, while MobileSAM’s distilled encoder can be competitive or better on single-prompt latency and edge accelerators. Which variant gives the best mask quality on fine defects? The original SAM with the ViT-H encoder gives the best boundary fidelity and small-defect sensitivity. MobileSAM stays close to it because it preserves SAM’s decoder and prompt interface; FastSAM is the weakest on thin structures and cluttered edges due to its CNN-based approach. Should I fine-tune the SAM variant on my inspection data? Fine-tuning the mask decoder or adapting the encoder can help on domain-specific textures, but first confirm the base variant passes a spot check on your hardest defect class. Often the larger gain comes from improving the upstream detector’s prompt quality rather than retraining the segmenter, since mask accuracy is capped by the prompt box you supply.
