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mlboydaisuke
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coreml-zoo

Core ML
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on-device
model-zoo
coremltools
mlpackage
arxiv:1808.00897
arxiv:2009.09960
arxiv:2101.04061
arxiv:2107.10833
arxiv:2109.07161
arxiv:2204.00964
arxiv:2211.08553
arxiv:2212.11613
arxiv:2303.14535
arxiv:2303.15343
arxiv:2306.07691
arxiv:2306.14289
arxiv:2310.13025
arxiv:2311.06242
arxiv:2311.14760
arxiv:2312.01479
arxiv:2401.17270
arxiv:2404.13686
arxiv:2405.14458
arxiv:2501.14677
arxiv:2503.07465
arxiv:2505.08175
arxiv:2507.02546
arxiv:2511.09554
arxiv:2511.10647
Model card Files
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  This repo is also the backing store for the [Models Zoo app](https://apps.apple.com/app/id6762083207) on the App Store, which downloads and runs any of these models on device without writing code. `models.json` is that app's manifest — treat it as machine-owned.
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  ## Models
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- | Model | Task | Packages | Size | License | Upstream |
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- |---|---|---:|---:|---|---|
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- | [Face Parsing](./faceparsing) | image segmentation | 1 | 47 MB | MIT | [zllrunning/face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch) |
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- | [MobileSAM](./mobilesam) | mask generation | 1 | 19 MB | Apache-2.0 | [ChaoningZhang/MobileSAM](https://github.com/ChaoningZhang/MobileSAM) |
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- | [RMBG-1.4](./rmbg) | image segmentation | 1 | 37 MB | Bria RMBG-1.4 License | [briaai/RMBG-1.4](https://huggingface.co/briaai/RMBG-1.4) |
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- | [DDColor Tiny](./ddcolor) | image to image | 1 | 203 MB | Apache-2.0 | [piddnad/DDColor](https://github.com/piddnad/DDColor) |
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- | [Pixelization](./pixelization) | image to image | 1 | 35 MB | Research use only | [WuZongWei6/Pixelization](https://github.com/WuZongWei6/Pixelization) |
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- | [Real-ESRGAN 4x](./realesrgan) | image to image | 1 | 59 MB | BSD-3-Clause | [xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) |
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- | [SinSR](./sinsr) | image to image | 3 | 517 MB | CC BY-NC-SA 4.0 | [wyf0912/SinSR](https://github.com/wyf0912/SinSR) |
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- | [RF-DETR Nano](./rfdetr) | object detection | 1 | 95 MB | Apache-2.0 | [roboflow/rf-detr](https://github.com/roboflow/rf-detr) |
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- | [YOLO-World](./yoloworld) | zero shot object detection | 2 | 134 MB | GPL-3.0 | [AILab-CVC/YOLO-World](https://github.com/AILab-CVC/YOLO-World) |
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- | [YOLO11s](./yolov9) | object detection | 1 | 17 MB | AGPL-3.0 | [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) |
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- | [YOLO26s](./yolo26) | object detection | 1 | 17 MB | AGPL-3.0 | [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) |
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- | [YOLOv10n](./yolov10) | object detection | 1 | 4 MB | AGPL-3.0 | [THU-MIG/yolov10](https://github.com/THU-MIG/yolov10) |
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- | [Depth Anything 3 Base (504×504)](./depth_anything_v3) | depth estimation | 1 | 173 MB | Apache-2.0 | [ByteDance-Seed/Depth-Anything-3](https://github.com/ByteDance-Seed/Depth-Anything-3) |
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- | [Depth Anything 3 Small (504×504)](./depth_anything_v3) | depth estimation | 1 | 44 MB | Apache-2.0 | [ByteDance-Seed/Depth-Anything-3](https://github.com/ByteDance-Seed/Depth-Anything-3) |
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- | [MoGe-2 ViT-B (504×504)](./moge2) | depth estimation | 1 | 184 MB | MIT | [microsoft/MoGe](https://github.com/microsoft/MoGe) |
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- | [Florence-2](./florence2) | image text to text | 3 | 229 MB | MIT | [microsoft/Florence-2](https://huggingface.co/microsoft/Florence-2-base) |
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- | [SigLIP](./siglip) | zero shot image classification | 2 | 358 MB | Apache-2.0 | [google-research/big_vision](https://github.com/google-research/big_vision) |
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- | [3DDFA V2](./face3d) | keypoint detection | 1 | 6 MB | MIT | [cleardusk/3DDFA_V2](https://github.com/cleardusk/3DDFA_V2) |
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- | [Hyper-SD (1-Step)](./hypersd) | text to image | 4 | 905 MB | OpenRAIL-M | [ByteDance/Hyper-SD](https://huggingface.co/ByteDance/Hyper-SD) |
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- | [Nitro-E (4-Step)](./nitroe) | text to image | 3 | 987 MB | MIT (Nitro-E) + Llama 3.2 Community License (text encoder) | [amd/Nitro-E](https://huggingface.co/amd/Nitro-E) |
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- | [MatAnyone](./matanyone) | image segmentation | 5 | 83 MB | S-Lab License 1.0 | [pq-yang/MatAnyone](https://github.com/pq-yang/MatAnyone) |
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- | [HTDemucs](./demucs) | audio to audio | 1 | 75 MB | MIT | [adefossez/demucs](https://github.com/adefossez/demucs) |
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- | [OpenVoice V2](./openvoice) | audio to audio | 2 | 58 MB | MIT | [myshell-ai/OpenVoice](https://github.com/myshell-ai/OpenVoice) |
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- | [Pyannote Diarization](./diarization) | voice activity detection | 1 | 5 MB | MIT | [pyannote/pyannote-audio](https://github.com/pyannote/pyannote-audio) |
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- | [Kokoro-82M](./kokoro) | text to speech | 4 | 724 MB | Apache-2.0 | [hexgrad/Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) |
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- | [Stable Audio Open](./stableaudio) | text to audio | 4 | 1.41 GB | Stability AI Community License | [stabilityai/stable-audio-open-small](https://huggingface.co/stabilityai/stable-audio-open-small) |
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- | [LaMa](./lama) | image to image | 1 | 187 MB | Apache-2.0 | [advimman/lama](https://github.com/advimman/lama) |
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- | [GFPGAN](./gfpgan) | image to image | 1 | 298 MB | Apache-2.0 | [TencentARC/GFPGAN](https://github.com/TencentARC/GFPGAN) |
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- | [AdaFace IR-18](./adaface) † | face recognition | 1 | 42 MB | MIT | [mk-minchul/AdaFace](https://github.com/mk-minchul/AdaFace) |
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- | [EfficientAD (MVTec bottle)](./efficientad) † | anomaly detection | 1 | 14 MB | MIT | [openvinotoolkit/anomalib](https://github.com/openvinotoolkit/anomalib) |
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- | [YOLOE-S](./yoloe) † | zero shot object detection + segmentation | 3 | 133 MB | AGPL-3.0 | [THU-MIG/yoloe](https://github.com/THU-MIG/yoloe) |
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  † Downloadable here, but not yet selectable inside the Models Zoo app — the shipped app build has no UI template for them.
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  - arxiv:2107.10833
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  - arxiv:2211.08553
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  - arxiv:2212.11613
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+ - arxiv:2303.14535
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  - arxiv:2303.15343
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  - arxiv:2306.07691
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  This repo is also the backing store for the [Models Zoo app](https://apps.apple.com/app/id6762083207) on the App Store, which downloads and runs any of these models on device without writing code. `models.json` is that app's manifest — treat it as machine-owned.
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+ Each model also has its own repo with a model card, a demo image and the unzipped `.mlpackage` — the **Model** column links there. The **In this repo** column is the zipped copy the app downloads.
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  ## Models
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+ | Model | Task | Packages | Size | License | Upstream | In this repo |
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+ |---|---|---:|---:|---|---|---|
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+ | [Face Parsing](https://huggingface.co/mlboydaisuke/Face-Parsing-CoreML) | image segmentation | 1 | 47 MB | MIT | [zllrunning/face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch) | [zip](./faceparsing) |
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+ | [MobileSAM](https://huggingface.co/mlboydaisuke/MobileSAM-CoreML) | mask generation | 1 | 19 MB | Apache-2.0 | [ChaoningZhang/MobileSAM](https://github.com/ChaoningZhang/MobileSAM) | [zip](./mobilesam) |
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+ | [RMBG-1.4](https://huggingface.co/mlboydaisuke/RMBG-1.4-CoreML) | image segmentation | 1 | 37 MB | Bria RMBG-1.4 License | [briaai/RMBG-1.4](https://huggingface.co/briaai/RMBG-1.4) | [zip](./rmbg) |
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+ | [DDColor Tiny](https://huggingface.co/mlboydaisuke/DDColor-Tiny-CoreML) | image to image | 1 | 203 MB | Apache-2.0 | [piddnad/DDColor](https://github.com/piddnad/DDColor) | [zip](./ddcolor) |
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+ | [Pixelization](https://huggingface.co/mlboydaisuke/Pixelization-CoreML) | image to image | 1 | 35 MB | Research use only | [WuZongWei6/Pixelization](https://github.com/WuZongWei6/Pixelization) | [zip](./pixelization) |
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+ | [Real-ESRGAN 4x](https://huggingface.co/mlboydaisuke/Real-ESRGAN-x4-CoreML) | image to image | 1 | 59 MB | BSD-3-Clause | [xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) | [zip](./realesrgan) |
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+ | [SinSR](https://huggingface.co/mlboydaisuke/SinSR-CoreML) | image to image | 3 | 517 MB | CC BY-NC-SA 4.0 | [wyf0912/SinSR](https://github.com/wyf0912/SinSR) | [zip](./sinsr) |
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+ | [RF-DETR Nano](https://huggingface.co/mlboydaisuke/RF-DETR-Nano-CoreML) | object detection | 1 | 95 MB | Apache-2.0 | [roboflow/rf-detr](https://github.com/roboflow/rf-detr) | [zip](./rfdetr) |
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+ | [YOLO-World](https://huggingface.co/mlboydaisuke/YOLO-World-V2-S-CoreML) | zero shot object detection | 2 | 134 MB | GPL-3.0 | [AILab-CVC/YOLO-World](https://github.com/AILab-CVC/YOLO-World) | [zip](./yoloworld) |
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+ | [YOLO11s](https://huggingface.co/mlboydaisuke/YOLO11s-CoreML) | object detection | 1 | 17 MB | AGPL-3.0 | [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) | [zip](./yolov9) |
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+ | [YOLO26s](https://huggingface.co/mlboydaisuke/YOLO26s-CoreML) | object detection | 1 | 17 MB | AGPL-3.0 | [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) | [zip](./yolo26) |
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+ | [YOLOv10n](https://huggingface.co/mlboydaisuke/YOLOv10n-CoreML) | object detection | 1 | 4 MB | AGPL-3.0 | [THU-MIG/yolov10](https://github.com/THU-MIG/yolov10) | [zip](./yolov10) |
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+ | [Depth Anything 3 Base (504×504)](https://huggingface.co/mlboydaisuke/Depth-Anything-3-Base-CoreML) | depth estimation | 1 | 173 MB | Apache-2.0 | [ByteDance-Seed/Depth-Anything-3](https://github.com/ByteDance-Seed/Depth-Anything-3) | [zip](./depth_anything_v3) |
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+ | [Depth Anything 3 Small (504×504)](https://huggingface.co/mlboydaisuke/Depth-Anything-3-Small-CoreML) | depth estimation | 1 | 44 MB | Apache-2.0 | [ByteDance-Seed/Depth-Anything-3](https://github.com/ByteDance-Seed/Depth-Anything-3) | [zip](./depth_anything_v3) |
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+ | [MoGe-2 ViT-B (504×504)](https://huggingface.co/mlboydaisuke/MoGe-2-ViT-B-CoreML) | depth estimation | 1 | 184 MB | MIT | [microsoft/MoGe](https://github.com/microsoft/MoGe) | [zip](./moge2) |
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+ | [Florence-2](https://huggingface.co/mlboydaisuke/Florence-2-base-CoreML) | image text to text | 3 | 229 MB | MIT | [microsoft/Florence-2](https://huggingface.co/microsoft/Florence-2-base) | [zip](./florence2) |
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+ | [SigLIP](https://huggingface.co/mlboydaisuke/SigLIP-base-patch16-224-CoreML) | zero shot image classification | 2 | 358 MB | Apache-2.0 | [google-research/big_vision](https://github.com/google-research/big_vision) | [zip](./siglip) |
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+ | [3DDFA V2](https://huggingface.co/mlboydaisuke/3DDFA-V2-CoreML) | keypoint detection | 1 | 6 MB | MIT | [cleardusk/3DDFA_V2](https://github.com/cleardusk/3DDFA_V2) | [zip](./face3d) |
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+ | [Hyper-SD (1-Step)](https://huggingface.co/mlboydaisuke/Hyper-SD-1step-CoreML) | text to image | 4 | 905 MB | OpenRAIL-M | [ByteDance/Hyper-SD](https://huggingface.co/ByteDance/Hyper-SD) | [zip](./hypersd) |
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+ | [Nitro-E (4-Step)](https://huggingface.co/mlboydaisuke/Nitro-E-CoreML) | text to image | 3 | 987 MB | MIT (Nitro-E) + Llama 3.2 Community License (text encoder) | [amd/Nitro-E](https://huggingface.co/amd/Nitro-E) | [zip](./nitroe) |
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+ | [MatAnyone](https://huggingface.co/mlboydaisuke/MatAnyone-CoreML) | image segmentation | 5 | 83 MB | S-Lab License 1.0 | [pq-yang/MatAnyone](https://github.com/pq-yang/MatAnyone) | [zip](./matanyone) |
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+ | [HTDemucs](https://huggingface.co/mlboydaisuke/HTDemucs-CoreML) | audio to audio | 1 | 75 MB | MIT | [adefossez/demucs](https://github.com/adefossez/demucs) | [zip](./demucs) |
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+ | [OpenVoice V2](https://huggingface.co/mlboydaisuke/OpenVoice-V2-CoreML) | audio to audio | 2 | 58 MB | MIT | [myshell-ai/OpenVoice](https://github.com/myshell-ai/OpenVoice) | [zip](./openvoice) |
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+ | [Pyannote Diarization](https://huggingface.co/mlboydaisuke/pyannote-segmentation-3.0-CoreML) | voice activity detection | 1 | 5 MB | MIT | [pyannote/pyannote-audio](https://github.com/pyannote/pyannote-audio) | [zip](./diarization) |
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+ | [Kokoro-82M](https://huggingface.co/mlboydaisuke/Kokoro-82M-CoreML) | text to speech | 4 | 724 MB | Apache-2.0 | [hexgrad/Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) | [zip](./kokoro) |
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+ | [Stable Audio Open](https://huggingface.co/mlboydaisuke/Stable-Audio-Open-Small-CoreML) | text to audio | 4 | 1.41 GB | Stability AI Community License | [stabilityai/stable-audio-open-small](https://huggingface.co/stabilityai/stable-audio-open-small) | [zip](./stableaudio) |
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+ | [LaMa](https://huggingface.co/mlboydaisuke/LaMa-CoreML) | image to image | 1 | 187 MB | Apache-2.0 | [advimman/lama](https://github.com/advimman/lama) | [zip](./lama) |
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+ | [GFPGAN](https://huggingface.co/mlboydaisuke/GFPGAN-CoreML) | image to image | 1 | 298 MB | Apache-2.0 | [TencentARC/GFPGAN](https://github.com/TencentARC/GFPGAN) | [zip](./gfpgan) |
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+ | [AdaFace IR-18](https://huggingface.co/mlboydaisuke/AdaFace-IR18-CoreML) † | face recognition | 1 | 42 MB | MIT | [mk-minchul/AdaFace](https://github.com/mk-minchul/AdaFace) | [zip](./adaface) |
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+ | [EfficientAD (MVTec bottle)](https://huggingface.co/mlboydaisuke/EfficientAD-CoreML) † | anomaly detection | 1 | 14 MB | MIT | [openvinotoolkit/anomalib](https://github.com/openvinotoolkit/anomalib) | [zip](./efficientad) |
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+ | [YOLOE-S](https://huggingface.co/mlboydaisuke/YOLOE-S-CoreML) † | zero shot object detection + segmentation | 3 | 133 MB | AGPL-3.0 | [THU-MIG/yoloe](https://github.com/THU-MIG/yoloe) | [zip](./yoloe) |
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  † Downloadable here, but not yet selectable inside the Models Zoo app — the shipped app build has no UI template for them.
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