Best Object Detection Models for Apple Silicon Macs in 2026

Quick Answer

Microsoft Table Transformer (structure recognition) (Microsoft) is the top pick as of September 2026 under the ordering rule, newest release first, then downloads: it shares the newest release date and wins the downloads tiebreak; every candidate has no public benchmark yet, too few releases meet the recency gate so the newest available are listed, and eligibility covers only object detection models tagged object-detection from labs with a published paper or leaderboard record [1][2][3][4][5][6]. In order, the ranking is Microsoft Table Transformer (structure recognition) (Microsoft), Microsoft Table Transformer (table detection) (Microsoft), Facebook Detection Transformer (DETR) with ResNet-50 (Facebook), and Facebook Detection Transformer (DETR) with ResNet-101 (Facebook) [1][2].

Key Takeaways

• Ranking note: scope admits only object detection models from labs with a published paper or leaderboard record and the Hugging Face object-detection tag.[1][2][3][4][5][6] Recent releases are insufficient, so the newest available candidates are listed; all were published in 2022.[1][3][4][6] No public benchmark scores any candidate: newest release first, then downloads. The established picks are the structure-recognition, table-detection and ResNet-50 models; their recency exemption does not determine the winner.[1][3][4]
• Microsoft Table Transformer (structure recognition) from Microsoft takes the first position because it shares the newest publication date and wins the download tiebreak.[1][3] Its 29M parameters imply 14.5 MB of weight memory at 4-bit—estimated (params x 0.5 bytes); the license is MIT, with no public benchmark yet.[1]
• Microsoft Table Transformer (table detection) from Microsoft follows its structure-recognition sibling on the download tiebreak.[1][3] Its 29M parameters imply 14.5 MB of weight memory at 4-bit—estimated (params x 0.5 bytes); the license is MIT, with no public benchmark yet.[3]
• Facebook Detection Transformer (DETR) with ResNet-50 from Facebook precedes its sibling on the download tiebreak.[4][6] Its 42M parameters imply 21 MB of weight memory at 4-bit—estimated (params x 0.5 bytes); the license is Apache-2.0, with no public benchmark yet.[4]
• Facebook Detection Transformer (DETR) with ResNet-101 from Facebook follows its sibling under the same ordering rule.[4][6] Its 61M parameters imply 30.5 MB of weight memory at 4-bit—estimated (params x 0.5 bytes); the license is Apache-2.0, with no public benchmark yet.[6]
• Apple Silicon runtime performance remains unestablished here: the listed checkpoints provide F32 weights, and the calculated quantized weight sizes do not establish working quantization, total application memory or Mac inference speed.[1][3][4][6]

How do these object detection models compare on specifications and published benchmarks?

Which object detection models should you consider for local use on Apple Silicon Macs?

1. Microsoft Table Transformer (structure recognition)

Microsoft Table Transformer (structure recognition) by Microsoft ranks first because its publication date ties Microsoft’s detection variant, while its download count breaks the tie.[1][3] Ranking note: the ordering is newest release first, then downloads; no public benchmark yet supports a performance ranking. The shortlist has too few recent releases, so it includes the newest available candidates, whose publication dates fall outside the requested window.[1][3][4][6] Eligibility is limited to object-detection models from labs with a published paper or leaderboard record.

The model has 29M parameters, a listed context length of 1K tokens, and 0.1 GB of F32 weights under the MIT license.[1] Weight storage at 4-bit would be 14.5 MB, estimated (params x 0.5 bytes) from the cited 29M parameters.[1] Treat that calculation as a storage estimate, not a verified Apple Silicon RAM requirement. For local deployment, confirm that your chosen runtime supports the model and measure total memory use before selecting hardware.

Choose the structure-recognition variant for table extraction from unstructured documents, the application described in Microsoft’s accompanying paper, “PubTables-1M: Towards comprehensive table extraction from unstructured documents.”[2] The practical caveat is that the published F32 weight size does not establish quantized runtime support or Mac performance.[1] Its ranking position therefore provides a selection order, not evidence of superior detection accuracy or faster execution on Apple Silicon.

2. Microsoft Table Transformer (table detection)

Microsoft Table Transformer (table detection) by Microsoft ranks second: its Hugging Face publication date matches the structure-recognition model, but its download count is lower.[1][3] The ordering is newest release first, then downloads; no public benchmark yet separates the candidates. Both Microsoft releases were published on October 14, 2022, so they are older available options rather than recent releases.[1][3] This ranking admits only object detection models from labs with a published paper or leaderboard record, using the Hugging Face object-detection pipeline tag.

The detector has 29M parameters, a listed context length of 1K tokens, F32 weights occupying 0.1 GB, and an MIT license.[3] Weight storage at 4-bit would be 14.5 MB, estimated (params x 0.5 bytes) from the cited 29M parameters.[3] Treat that calculation as a storage estimate, not a downloadable quantized configuration or a complete memory budget. The listed context length also should not be treated as a document-page capacity.

Use the model for table detection within a document-extraction workflow associated with Microsoft’s PubTables-1M: Towards comprehensive table extraction from unstructured documents.[2][3] Table detection is the appropriate role here; the separately listed structure-recognition model addresses a different stage.[1][3] For local deployment on an Apple Silicon Mac, allow memory beyond the weights for execution. The practical caveat is that weight size alone cannot establish a required Mac memory configuration, runtime compatibility, or inference speed.

3. Facebook Detection Transformer (DETR) with ResNet-50

Facebook Detection Transformer (DETR) with ResNet-50 by Facebook ranks third: its Hugging Face publication predates the Microsoft entries, while downloads break the release-date tie with the remaining Facebook candidate.[1][3][4][6] The ordering is newest release first, then downloads; no public benchmark yet supports a performance ranking. The shortlist admits only object detection models from labs with a published paper or leaderboard record and uses the newest available entries because too few meet the recency gate.[2][5]

An established pick, the model was first published on Hugging Face on March 2, 2022, and recorded 349,004 downloads over the reporting period of 30 days.[4] Its specifications list 42M parameters, a context length of 1K tokens, F32 weights occupying 0.2 GB, and an Apache-2.0 license.[4] The architecture is documented in “End-to-End Object Detection with Transformers,” making general object detection its relevant use here.[5]

For Mac hardware planning, 4-bit weight storage is approximately 21 MB, estimated (params x 0.5 bytes) from the listed 42M parameters.[4] That calculation covers weights alone; it does not establish total memory consumption or a minimum Mac configuration. Treat the model as a candidate for local object detection evaluation. The practical caveat is that the listing does not establish Apple Silicon runtime compatibility, working quantization, or measured inference speed.

4. Facebook Detection Transformer (DETR) with ResNet-101

Facebook Detection Transformer (DETR) with ResNet-101 by Facebook ranks fourth under the ordering rule “newest release first, then downloads.”[1][3][4][6] Its Hugging Face publication date matches Facebook Detection Transformer (DETR) with ResNet-50 by Facebook: March 2, 2022.[4][6] Downloads break that tie: 17,800 versus 349,004 over the reported 30-day period.[4][6] The benchmark status is “no public benchmark yet”; the position does not establish a performance difference.

The model has 61 million parameters, a listed context length of 1K tokens, and 0.2 GB of F32 weights, under the Apache-2.0 license.[6] For those 61 million parameters, 4-bit weight storage is approximately 30.5 MB, estimated (params x 0.5 bytes).[6] Treat that calculation as a weight-storage estimate, not a Mac RAM requirement. A practical hardware recommendation still requires verification of runtime support, quantized loading and total memory consumption on Apple Silicon.

Use the model as an object-detection candidate when the Apache-2.0 license suits your project.[6] The deployment caveat is that the listed specifications do not establish Apple Silicon compatibility or measured local speed. The ranking admits only object detection models from labs with a published paper or leaderboard record, using Hugging Face’s object-detection pipeline tag.[1][2][3][4][5][6] The available selection falls outside the requested release window; the newest available candidates are listed instead.[1][3][4][6]

How much memory would quantized object detection weights need?

Quantized object detection weights would need approximately 14.5–30.5 MB at 4-bit precision, estimated (params x 0.5 bytes) from the listed parameter counts.[1][3][4][6] Those figures describe weight storage alone, rather than total memory needed to run detection on an Apple Silicon Mac.

Microsoft’s Table Transformer Structure Recognition (microsoft/table-transformer-structure-recognition) has 29M parameters, giving 14.5 MB of quantized weights, estimated (params x 0.5 bytes).[1] Microsoft’s Table Transformer Detection (microsoft/table-transformer-detection) also has 29M parameters and the same 14.5 MB estimate, estimated (params x 0.5 bytes).[3]

Facebook’s DEtection TRansformer (DETR) ResNet-50 (facebook/detr-resnet-50) has 42M parameters, giving 21 MB of quantized weights, estimated (params x 0.5 bytes).[4] Facebook’s DETR ResNet-101 (facebook/detr-resnet-101) has 61M parameters, giving 30.5 MB, estimated (params x 0.5 bytes).[6]

The published checkpoints contain F32 weights: 0.1 GB for each Microsoft model and 0.2 GB for each Facebook model.[1][3][4][6] The quantized estimates therefore describe hypothetical storage after conversion; they are not measured sizes of supplied quantized checkpoints.

For practical planning, keep weight storage separate from application memory. The arithmetic does not establish quantization support, successful execution on Apple Silicon, runtime memory consumption, or detection speed. Treat it as a starting point for evaluating a compatible implementation, not a Mac RAM requirement.

What licenses cover Microsoft Table Transformer and Facebook Detection Transformer?

Microsoft’s Table Transformer models use the MIT license, while Facebook’s Detection Transformer (DETR) models use the Apache License 2.0.[1][3][4][6] Both Microsoft checkpoints share a license, and both Facebook checkpoints share a license.[1][3][4][6]

Microsoft Table Transformer Structure Recognition (microsoft/table-transformer-structure-recognition) is listed under MIT.[1] Microsoft Table Transformer Detection (microsoft/table-transformer-detection) is also listed under MIT.[3] Choosing between those checkpoints therefore does not change the stated model license.[1][3] For a deployment review, record the exact repository and checkpoint you intend to distribute or run.

Facebook Detection Transformer with ResNet-50 (facebook/detr-resnet-50) is listed under Apache License 2.0.[4] Facebook Detection Transformer with ResNet-101 (facebook/detr-resnet-101) carries the same license.[6] Moving between those Facebook checkpoints likewise preserves the stated license choice.[4][6]

For a local Mac application, keep licensing review separate from runtime selection. Read the license text shipped with your chosen checkpoint before packaging weights, modifying them, or distributing an application. Check the terms for your inference framework and other bundled components separately. Document any applicable attribution, notice, and redistribution requirements alongside the model’s repository identifier so the release process can account for them.

What Apple Silicon performance information is missing from the model listings?

The model listings lack Apple silicon measurements for inference latency, throughput, peak runtime memory, and execution backend, so they do not establish how well the models run locally on Apple’s Macs.[1][3][4][6]

Parameter counts and downloadable weight sizes describe the models, but the listings provide no corresponding measurements of memory during inference.[1][3][4][6] A practical comparison needs peak memory for a specified image resolution and batch size, with the model loaded and processing images. Weight storage alone should not be treated as a measured RAM requirement.

Quantization also needs validation. The listings identify full-precision weights but provide no quantized Apple silicon performance results or accuracy comparisons.[1][3][4][6] A useful deployment report would identify the conversion method, runtime, supported operations, and any accuracy change before recommending a quantized version.

Hardware and timing details remain missing: the listings supply no chip-specific results or breakdown of loading, preprocessing, inference, and postprocessing time.[1][3][4][6] Engineers need the exact Mac configuration, software versions, input dimensions, and timing procedure to reproduce a result.

Published papers establish the models’ academic background, but the listings provide no shared Apple silicon benchmark for choosing between them.[1][2][3][4][5][6] Treat the ranking as a shortlist for local evaluation, not a demonstrated ordering of Mac inference performance.

Frequently Asked Questions

Which object detection model ranks first for Apple Silicon Macs?

Microsoft’s Table Transformer structure-recognition model (microsoft/table-transformer-structure-recognition) ranks first because it shares the later publication date with Microsoft’s table-detection checkpoint and has more downloads. [1][3] The checkpoint has no public benchmark yet. Its position follows the publication-date and download rules; an Apple Silicon speed or accuracy advantage is not established here. Treat the ranking as a shortlist for local evaluation.

How are the models ranked?

Ranking note: this ranking admits only object detection models from labs with a published paper or leaderboard record (Hugging Face pipeline tags: object-detection). [2][5] No public benchmark scores any candidate, so the rule gives: newest release first, then downloads. [1][3][4][6] The remaining order is Microsoft’s Table Transformer detection (microsoft/table-transformer-detection), Facebook’s Detection Transformer with ResNet-50 (facebook/detr-resnet-50), then Facebook’s Detection Transformer with ResNet-101 (facebook/detr-resnet-101). [3][4][6]

Are these recent releases or established picks?

The family has too few qualifying recent releases, so the newest available candidates are listed. Both Microsoft checkpoints were first published on Hugging Face on 2022-10-14; both Facebook checkpoints were first published on 2022-03-02. [1][3][4][6] Ranking note: the established picks are the three most-downloaded candidates—both Table Transformer checkpoints and DETR ResNet-50—and bypass the recency gate, but that exemption never grants first place. [1][3][4][6]

How much memory would quantized weights need?

Each Table Transformer checkpoint has 29M parameters: 14.5 MB estimated (params x 0.5 bytes) for 4-bit weights. [1][3] DETR ResNet-50 has 42M parameters: 21 MB estimated (params x 0.5 bytes). [4] DETR ResNet-101 has 61M parameters: 30.5 MB estimated (params x 0.5 bytes). [6] Those calculations describe weight storage only. They do not establish total runtime memory, a working quantized implementation, or a particular Mac’s ability to run the model.

What licenses and context lengths do the models have?

Both Microsoft Table Transformer checkpoints use the MIT license and list a context length of 1K tokens. [1][3] Both Facebook DETR checkpoints use Apache-2.0 and list a context length of 1K tokens. [4][6] Treat those context values as listed metadata when comparing the checkpoints. The values alone do not establish supported image dimensions, document page capacity, or memory requirements for your application.

Which model has the strongest published benchmark results on a Mac?

No public benchmark yet is the status for every candidate in this comparison. A benchmark-based winner therefore cannot be named, and the ranking does not establish Apple Silicon throughput or latency. Downloads break publication-date ties: Table Transformer structure recognition has 1,057,033 versus detection’s 530,864; DETR ResNet-50 has 349,004 versus ResNet-101’s 17,800, measured over the last 30 days as of 2026-09-25. [1][3][4][6]

Sources

1. microsoft/table-transformer-structure-recognition model card (Hugging Face) — 2026-09-25
2. PubTables-1M: Towards comprehensive table extraction from unstructured documents — 2021-09-30
3. microsoft/table-transformer-detection model card (Hugging Face) — 2026-09-25
4. facebook/detr-resnet-50 model card (Hugging Face) — 2026-09-25
5. End-to-End Object Detection with Transformers — 2020-05-26
6. facebook/detr-resnet-101 model card (Hugging Face) — 2026-09-25