Best Segmentation Models for Apple Silicon Macs in 2026: Mask2Former Swin Large Cityscapes Semantic

Quick Answer

Facebook’s Mask2Former Swin Large Cityscapes Semantic is the top pick as of September 2026 under the ordering “newest release first, then downloads,” with no public benchmark yet [1][6]. In order, the ranking is Mask2Former Swin Large Cityscapes Semantic (Facebook), Mask2Former Swin Large ADE Semantic (Facebook), SegFormer B0 Fine-Tuned ADE 512-512 (NVIDIA), SegFormer B2 Fine-Tuned ADE 512-512 (NVIDIA), BEiT Large Fine-Tuned ADE 640-640 (Microsoft), and BEiT Base Fine-Tuned ADE 640-640 (Microsoft) [1][6].

Key Takeaways

• Ranking note: Scope admits only image segmentation models from labs with a published paper or leaderboard record, using Hugging Face’s image-segmentation tag.[2][4][8] Too few recent releases qualify, so the newest available candidates are listed.[1][3][7] All have no public benchmark yet; ordering is newest release first, then downloads.
• Facebook’s Mask2Former Swin Large Cityscapes Semantic ranks first because it shares the latest listed publication date and wins the download tiebreak.[1][6] Ranking note: established picks are that model, NVIDIA’s SegFormer B0 Fine-Tuned ADE 512-512, and NVIDIA’s SegFormer B2 Fine-Tuned ADE 512-512; their download-based recency exemption does not itself confer first place.[1][3][5]
• Facebook’s Mask2Former Swin Large ADE Semantic and the Cityscapes variant each have 216M parameters: 108 MB at 4-bit, estimated (params x 0.5 bytes) for weights alone.[1][6] Those estimates do not establish total memory use or Mac compatibility.
• NVIDIA’s SegFormer B0 Fine-Tuned ADE 512-512 has 4M parameters: 2 MB at 4-bit, estimated (params x 0.5 bytes) for weights alone.[3] Its position ahead of the B2 variant follows their matching publication dates and download tiebreak, without establishing a performance advantage.[3][5]
• Microsoft’s BEiT Large Fine-Tuned ADE 640-640 and Microsoft’s BEiT Base Fine-Tuned ADE 640-640 carry Apache-2.0 licenses.[7][9] The Large variant has 503M parameters: 251.5 MB at 4-bit, estimated (params x 0.5 bytes) for weights alone.[7]
• Treat this ranking as a shortlist for local evaluation: verified Apple Silicon speed, working quantization, peak unified-memory requirements and input/context limits are not established for these candidates. Parameter counts alone cannot establish which model fits and runs well on a particular Mac.[1][3][5][6][7][9]

How do segmentation models compare on parameters, estimated weight memory, licenses, and benchmarks?

Which image segmentation models should you consider for an Apple Silicon Mac?

1. Mask2Former Swin Large Cityscapes Semantic

Mask2Former Swin Large Cityscapes Semantic by Facebook ranks first because its publication date ties with the ADE variant, while its higher download count breaks the tie.[1][6] Published on Hugging Face on January 5, 2023, the checkpoint has 216 million parameters and 0.9 GB of F32 weights.[1] Its 483,243 downloads over the preceding 30 days, measured on September 25, 2026, establish popularity rather than segmentation quality or Mac performance.[1]

For local deployment, the weight-only footprint at 4-bit precision is 108 MB, estimated (params x 0.5 bytes) from the cited 216 million parameters.[1] Treat that calculation as a storage estimate, not a total RAM requirement or confirmation that a compatible quantized implementation exists. A specific Apple Silicon chip or memory configuration cannot be recommended without runtime validation. The practical use case is Cityscapes semantic segmentation.[1] The deployment caveat is that Apple Silicon compatibility, working memory requirements and inference speed remain unverified.

Ranking note: no public benchmark scores any candidate, so the ordering is newest release first, then downloads. The ranking admits only image segmentation models from labs with a published paper or leaderboard record, using Hugging Face’s image-segmentation pipeline tag. Too few recent releases qualify, so the newest available candidates are included despite their older publication dates.[1][3][5][6][7][9] The benchmark status for this checkpoint is “no public benchmark yet”; its position does not establish superior accuracy.

2. Mask2Former Swin Large ADE Semantic

Mask2Former Swin Large ADE Semantic by Facebook ranks second because it shares the newest publication date in this shortlist, January 5, 2023, but has fewer downloads than the Cityscapes variant: 279,927 versus 483,243 over the reporting period.[1][6] The ordering is “newest release first, then downloads”; no public benchmark scores these candidates. The selection uses the newest available releases because recent releases are too scarce. Scope is limited to image segmentation models from labs with a published paper or leaderboard record, using Hugging Face’s image-segmentation pipeline tag.

Facebook lists 216M parameters and 0.9 GB of F32 weights.[6] At 4-bit precision, weight storage would be approximately 108 MB, estimated (params x 0.5 bytes) from the cited 216M parameters.[6] Treat that calculation as a weight-storage estimate, not a Mac RAM requirement. A verified Apple Silicon memory requirement, supported quantized runtime and local performance measurement are unavailable for this ranking, so a specific Mac configuration cannot be recommended confidently.

Consider this checkpoint for ADE semantic segmentation when that task matches your application. The architecture is described in Facebook’s Masked-attention Mask Transformer for Universal Image Segmentation paper.[2] The practical caveat is validation: no public benchmark yet supports its placement as a measured quality or speed advantage. Confirm runtime compatibility, input requirements and licensing before committing to local deployment; the download tiebreak does not establish how well the model runs on your Mac.

3. SegFormer B0 Fine-Tuned ADE 512-512

SegFormer B0 Fine-Tuned ADE 512-512 by NVIDIA is a semantic segmentation model with 4 million parameters.[3][4] Its position follows the ordering rule: newest release first, then downloads. Its Hugging Face publication date is March 2, 2022,[3] behind the preceding models’ January 5, 2023 publication date.[1][6] Among candidates sharing its publication date, its 368,092 downloads put it ahead as a popularity tiebreak, not a measure of segmentation quality.[3][5][7][9]

The 4 million parameters imply a weight footprint of approximately 2 MB at 4-bit precision—estimated (params x 0.5 bytes).[3] That calculation covers weights alone; it does not establish total runtime memory or confirm a working quantized implementation. For local deployment, a specific Apple Silicon Mac configuration cannot be recommended without verified runtime support and memory measurements. The small estimated weight footprint makes the model worth evaluating when storage and memory budgets constrain model selection.

Semantic segmentation experiments are the practical use case.[4] Treat the model as a candidate to validate on your own images before committing to deployment. The benchmark status is “no public benchmark yet,” so its position does not establish accuracy or Mac performance. The ranking admits only image segmentation models from labs with a published paper or leaderboard record. Recent releases are insufficient for this selection, which therefore includes older available models.[1][3][5][6][7][9]

4. SegFormer B2 Fine-Tuned ADE 512-512

SegFormer B2 Fine-Tuned ADE 512-512 by NVIDIA takes fourth place under the ordering rule “newest release first, then downloads.”[5] Its first Hugging Face publication was March 2, 2022, matching NVIDIA’s SegFormer B0 Fine-Tuned ADE 512-512.[3][5] The download tiebreak puts it behind that checkpoint: 291,568 versus 368,092 downloads during the preceding 30 days, as of September 25, 2026.[3][5] Its position reflects release order and adoption, rather than a demonstrated accuracy advantage.

NVIDIA’s SegFormer architecture targets semantic segmentation with transformers, making this checkpoint a candidate for assigning semantic labels across an image.[4] Consider it for a local semantic-segmentation evaluation when you want to compare checkpoints within that family. The practical caveat is no public benchmark yet for this ranking; download counts cannot establish segmentation quality. Evaluate its output on images representative of your workload before choosing it.

Apple Silicon hardware requirements remain unverified for this comparison. Parameter count, weight storage, quantized memory, supported input dimensions and license terms are not established here, so a specific Mac or RAM recommendation would be premature. A local deployment decision needs confirmation of runtime compatibility and measured memory use. Treat this entry as a candidate to validate, with no confirmed Mac performance or memory-fit claim.

5. BEiT Large Fine-Tuned ADE 640-640

BEiT Large Fine-Tuned ADE 640-640 by Microsoft ranks here under the ordering rule: newest release first, then downloads. Its Hugging Face publication date is March 2, 2022,[7] and it has no public benchmark yet. Within that release-date group, its 29,850 downloads over the reporting month put it ahead of Microsoft’s BEiT Base Fine-Tuned ADE 640-640, with 28,849 downloads.[7][9] The position reflects release timing and adoption, rather than demonstrated segmentation quality or Mac performance.

The model has 503 million parameters and a listed F32 weight size of 2.3 GB.[7] Its weight payload at 4-bit precision would be 251.5 MB, estimated (params x 0.5 bytes).[7] That calculation covers weights alone; it does not establish total runtime memory or confirm an available quantized implementation. A specific Apple Silicon Mac or unified-memory capacity therefore cannot be recommended confidently. Local deployment needs verification of runtime compatibility and actual memory use before hardware sizing.

Consider this checkpoint for evaluating ADE image segmentation when the Apache-2.0 license suits your project.[7] The practical caveat is deployment uncertainty: Apple Silicon throughput, runtime memory requirements and a supported local execution path remain unverified. Treat the compact estimated weight payload as a planning input, not a promise that the model will run efficiently on your Mac.

6. BEiT Base Fine-Tuned ADE 640-640

BEiT Base Fine-Tuned ADE 640-640 by Microsoft ranks sixth under the ordering rule: newest release first, then downloads.[9] No public benchmark scores any candidate; its benchmark status is “no public benchmark yet.” Its Hugging Face publication date matches Microsoft’s large variant, but its download count is lower.[7][9] The ranking admits only image segmentation models from labs with a published paper or leaderboard record. The available selection falls outside the requested recency window, so the newest available entries are included.[1][3][5][6][7][9]

Microsoft published the checkpoint on Hugging Face on March 2, 2022; it recorded 28,849 downloads over the preceding 30 days as of September 25, 2026.[9] The license is Apache-2.0.[9] The model belongs to the family described in “BEiT: BERT Pre-Training of Image Transformers.”[8] Parameter count, weight size and input limits are unspecified here. A defensible quantized-weight estimate therefore cannot be calculated, and no minimum Apple Silicon unified-memory configuration can be recommended.

Consider this checkpoint for a local segmentation evaluation where its explicit license helps you assess suitability.[9] Treat Apple Silicon deployment as an evaluation task: verify runtime support and measure memory use on your intended Mac before adopting it. The practical caveat is missing deployment evidence: Apple Silicon compatibility, latency and quantization support are unconfirmed here. Its ranking position does not establish segmentation quality or local performance.

How much memory could quantized segmentation weights require?

Quantized segmentation weights could require about 2 MB for NVIDIA’s nvidia/segformer-b0-finetuned-ade-512-512 at 4-bit precision: estimated (params x 0.5 bytes) from its reported 4M parameters.[3] Treat that figure as a weight-storage calculation, not a measured Apple Silicon memory requirement or confirmation that a compatible quantized checkpoint exists.

Facebook’s facebook/mask2former-swin-large-cityscapes-semantic and facebook/mask2former-swin-large-ade-semantic each report 216M parameters.[1][6] Each therefore has a theoretical 4-bit weight footprint of 108 MB, estimated (params x 0.5 bytes).[1][6] Equal parameter counts produce equal storage estimates; those calculations do not establish equal runtime memory consumption.

Microsoft’s microsoft/beit-large-finetuned-ade-640-640 reports 503M parameters, giving a theoretical 4-bit weight footprint of 251.5 MB, estimated (params x 0.5 bytes).[7] Its listed F32 weights occupy 2.3 GB.[7] Keep the published file size separate from the calculated quantized payload: neither establishes how much unified memory an inference session requires.

Use these estimates to compare potential weight storage when planning a local deployment. A Mac fit assessment still needs a compatible implementation and a measured runtime footprint for the intended image workload. The calculations alone do not establish quantization support, segmentation quality after quantization, or execution speed on Apple Silicon.

Which segmentation models have a documented license?

Microsoft’s microsoft/beit-large-finetuned-ade-640-640 and microsoft/beit-base-finetuned-ade-640-640 have a documented Apache License, Version 2.0 (Apache-2.0).[7][9] Both are candidates to consider when an explicitly identified license is a prerequisite for evaluation.

License status remains unverified in this comparison for Facebook’s facebook/mask2former-swin-large-cityscapes-semantic and facebook/mask2former-swin-large-ade-semantic, and NVIDIA’s nvidia/segformer-b0-finetuned-ade-512-512 and nvidia/segformer-b2-finetuned-ade-512-512.[1][6][3][5] Treat that status as a verification task before adoption, rather than a conclusion that those checkpoints have no license.

For either Microsoft checkpoint, record the license alongside the exact repository and revision you intend to use. Check the license files accompanying your chosen download, and repeat that check if you switch to a converted or quantized distribution. Keep the original checkpoint and any downstream package clearly identified in your project documentation.

A documented license answers only the licensing part of the selection process. Apple Silicon execution, quantization support and runtime memory still need separate verification. Choose between the Microsoft checkpoints after checking your deployment requirements; their shared Apache-2.0 designation does not distinguish their segmentation quality or suitability for your Mac.[7][9]

Why does the ranking include older segmentation models?

The ranking includes older segmentation models because too few recent releases qualify, so the newest available candidates are included instead.[1][3][5][6][7][9] The listed checkpoints were first published on Hugging Face in January 2023 or March 2022; their inclusion does not imply a recent model release.[1][3][5][6][7][9]

The ranking admits only image segmentation models from labs with a published paper or leaderboard record, using Hugging Face’s image-segmentation pipeline tag. Facebook’s Mask2Former, NVIDIA’s SegFormer and Microsoft’s BEiT each have an accompanying paper.[2][4][8]

The ordering basis is exactly “newest release first, then downloads.”[1][3][5][6][7][9] No public benchmark scores any candidate against the others, so every entry carries “no public benchmark yet.” Downloads serve only as a tiebreaker, not evidence of segmentation accuracy or Mac performance.

Facebook’s facebook/mask2former-swin-large-cityscapes-semantic takes the first position because it shares the newest publication date with Facebook’s facebook/mask2former-swin-large-ade-semantic and has more downloads.[1][6]

The established picks are Facebook’s facebook/mask2former-swin-large-cityscapes-semantic, NVIDIA’s nvidia/segformer-b0-finetuned-ade-512-512 and NVIDIA’s nvidia/segformer-b2-finetuned-ade-512-512: the three most-downloaded candidates.[1][3][5][6][7][9] Their exemption permits inclusion despite age; the same ordering rule applies, and the exemption itself cannot award first place.

For Apple Silicon buyers, inclusion is a shortlist decision. A ranking position does not establish local inference speed, runtime memory requirements or quantized compatibility. Those practical questions remain separate from publication dates and download counts.

Frequently Asked Questions

Which segmentation model ranks first for Apple Silicon Macs?

Facebook’s Mask2Former Swin Large Cityscapes Semantic (facebook/mask2former-swin-large-cityscapes-semantic) ranks first because it shares the newest listed publication date and wins the downloads tiebreak.[1][6] Its Hugging Face publication date is 2023-01-05, with 483,243 downloads in the reporting window.[1] The position reflects release order and popularity among tied releases; Apple Silicon speed, runtime compatibility and segmentation accuracy remain unverified in this comparison.

How is the ranking ordered?

Ranking note: no public benchmark scores any candidate, so the order is newest release first, then downloads. The sequence is Facebook’s facebook/mask2former-swin-large-cityscapes-semantic,[1] facebook/mask2former-swin-large-ade-semantic,[6] NVIDIA’s nvidia/segformer-b0-finetuned-ade-512-512,[3] nvidia/segformer-b2-finetuned-ade-512-512,[5] Microsoft’s microsoft/beit-large-finetuned-ade-640-640,[7] and microsoft/beit-base-finetuned-ade-640-640.[9] Downloads break release-date ties; parameter count does not determine placement. Scope: this ranking admits only image segmentation models from labs with a published paper or leaderboard record, using the Hugging Face image-segmentation pipeline tag.[2][4][8]

Are these recent releases or established picks?

The family has too few releases within the requested twelve-month window, so the newest available candidates are listed; their Hugging Face publication dates fall in January 2023 and March 2022.[1][3][5][6][7][9] The established picks—the three most-downloaded candidates—are Mask2Former Swin Large Cityscapes Semantic, SegFormer B0 and SegFormer B2.[1][3][5] Ranking note: established picks bypass the recency gate but follow the same ordering rule; the exemption itself cannot confer first place.

How much memory would quantized weights need?

For both Mask2Former checkpoints, 216M parameters imply 108 MB of four-bit weights, estimated (params x 0.5 bytes).[1][6] NVIDIA’s SegFormer B0 has 4M parameters, implying 2 MB, estimated (params x 0.5 bytes).[3] Microsoft’s BEiT Large has 503M parameters, implying 251.5 MB, estimated (params x 0.5 bytes).[7] Treat these as weight-storage calculations. Confirm quantization support and measure total runtime memory before choosing a Mac configuration.

Which models have a confirmed license for a local project?

Microsoft’s BEiT Large and BEiT Base checkpoints list Apache-2.0.[7][9] License terms for the listed Facebook Mask2Former and NVIDIA SegFormer checkpoints are unconfirmed in this comparison. Check each checkpoint’s license before adopting it, particularly for redistribution or commercial deployment. A confirmed license also leaves a separate engineering question: whether your chosen local runtime supports the checkpoint.

Do published benchmarks establish which model runs well on a Mac?

Every listed candidate has no public benchmark yet for this ranking.[1][3][5][6][7][9] The associated papers establish a publication record for Mask2Former, SegFormer and BEiT, but do not supply candidate-level Apple Silicon results here.[2][4][8] Mac latency, peak memory, backend compatibility and context or input-size limits remain unverified in this comparison. Validate those requirements with your intended runtime and representative images before committing to a checkpoint.

Sources

1. facebook/mask2former-swin-large-cityscapes-semantic model card (Hugging Face) — 2026-09-25
2. Masked-attention Mask Transformer for Universal Image Segmentation — 2021-12-02
3. nvidia/segformer-b0-finetuned-ade-512-512 model card (Hugging Face) — 2026-09-25
4. SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers — 2021-05-31
5. nvidia/segformer-b2-finetuned-ade-512-512 model card (Hugging Face) — 2026-09-25
6. facebook/mask2former-swin-large-ade-semantic model card (Hugging Face) — 2026-09-25
7. microsoft/beit-large-finetuned-ade-640-640 model card (Hugging Face) — 2026-09-25
8. BEiT: BERT Pre-Training of Image Transformers — 2021-06-15
9. microsoft/beit-base-finetuned-ade-640-640 model card (Hugging Face) — 2026-09-25