https://github.com/john-rocky/coreml-models

Converted CoreML Model Zoo.
https://github.com/john-rocky/coreml-models
coreml coreml-framework coremltools deep-learning gan gans image-classification ios machine-learning object-detection semantic-segmentation style-gan super-resolution swift
Last synced: 5 days ago
JSON representation
Converted CoreML Model Zoo.
Host: GitHub
URL: https://github.com/john-rocky/coreml-models
Owner: john-rocky
Created: 2020-08-12T02:41:43.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2026-04-03T16:26:48.000Z (8 days ago)
Last Synced: 2026-04-03T16:36:52.127Z (8 days ago)
Topics: coreml, coreml-framework, coremltools, deep-learning, gan, gans, image-classification, ios, machine-learning, object-detection, semantic-segmentation, style-gan, super-resolution, swift
Language: Swift
Homepage:
Size: 75.5 MB
Stars: 1,705
Watchers: 45
Forks: 160
Open Issues: 34
Metadata Files:
- Readme: README.md
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README

          # CoreML-Models

Converted Core ML Model Zoo.



Core ML is a machine learning framework by Apple.

If you are iOS developer, you can easly use machine learning models in your Xcode project. 

# How to use

Take a look this model zoo, and if you found the CoreML model you want,

download the model from google drive link and bundle it in your project.

Or if the model have sample project link, try it and see how to use the model in the project.

You are free to do or not.

**If you like this repository, please give me a star so I can do my best.**

# Section Link

- [**Image Classifier**](#image-classifier)

  - [Efficientnetb0](#efficientnetb0)

  - [Efficientnetv2](#efficientnetv2)

  - [VisionTransformer](#visiontransformer)

  - [Conformer](#conformer)

  - [DeiT](#deit)

  - [RepVGG](#repvgg)

  - [RegNet](#regnet)

  - [MobileViTv2](#mobilevitv2)

  

- [**Object Detection**](#object-detection)

  - [D-FINE](#d-fine)

  - [RF-DETR](#rf-detr)

  - [YOLOv5s](#yolov5s)

  - [YOLOv7](#yolov7)

  - [YOLOv8](#yolov8)

  - [YOLOv9](#yolov9)

  - [YOLOv10](#yolov10)

  - [YOLO11](#yolo11)

  - [YOLO26](#yolo26)

  - [YOLO-World](#yolo-world)

- [**Segmentation**](#segmentation)

  - [U2Net](#u2net)

  - [IS-Net](#is-net)

  - [RMBG1.4](#rmbg14)

  - [face-parsing](#face-parsing)

  - [Segformer](#segformer)

  - [BiseNetv2](#bisenetv2)

  - [DNL](#dnl)

  - [ISANet](#isanet)

  - [FastFCN](#fastfcn)

  - [GCNet](#gcnet)

  - [DANet](#danet)

  - [Semantic FPN](#semantic-fpn)

  - [cloths_segmentation](#cloths_segmentation)

  - [easyportrait](#easyportrait)

  - [MobileSAM](#mobilesam)

  - [SAM2-Tiny](#sam2-tiny)

- [**Super Resolution**](#super-resolution)

  - [Real ESRGAN](#real-esrgan)

  - [GFPGAN](#gfpgan)

  - [BSRGAN](#bsrgan)

  - [A-ESRGAN](#a-esrgan)

  - [Beby-GAN](#beby-gan)

  - [RRDN](#rrdn)

  - [Fast-SRGAN](#fast-srgan)

  - [ESRGAN](#esrgan)

  - [UltraSharp](#ultrasharp)

  - [SRGAN](#srgan)

  - [SRResNet](#srresnet)

  - [LESRCNN](#lesrcnn)

  - [MMRealSR](#mmrealsr)

  - [DASR](#dasr)

      

- [**Low Light Enhancement**](#low-light-enhancement)

  - [StableLLVE](#stablellve)

  - [Zero-DCE](#zero-dce)

  - [Retinexformer](#retinexformer)

- [**Image Restoration**](#image-restroration)

  - [MPRNet](#mprnet)

  - [MIRNetv2](#mirnetv2)

  

- [**Image Generation**](#image-generation)

  - [MobileStyleGAN](#mobilestylegan)

  - [DCGAN](#dcgan)

- [**Image2Image**](#image2image)

  - [Anime2Sketch](#anime2sketch)

  - [AnimeGAN2Face_Paint_512_v2](#animegan2face_paint_512_v2)

  - [Photo2Cartoon](#photo2cartoon)

  - [AnimeGANv2_Hayao](#animeGANv2_hayao)

  - [AnimeGANv2_Paprika](#animeGANv2_paprika)

  - [WarpGAN Caricature](#warpgancaricature)

  - [UGATIT_selfie2anime](#ugatit_selfie2anime)

  - [Fast-Neural-Style-Transfer](#fast-neural-style-transfer)

  - [White_box_Cartoonization](#white_box_cartoonization)

  - [FacialCartoonization](#facialcartoonization)

- [**Inpainting**](#inpainting)

  - [AOT-GAN-for-Inpainting](#aot-gan-for-inpainting)

  - [Lama](#lama)

- [**Monocular Depth Estimation**](#monocular-depth-estimation)

  - [MiDaS](#midas)

  

- [**Stable Diffusion**](#stable-diffusion) **:text2image**

  - [stable-diffusion-v1-5](#stable-diffusion-v1-5)

  - [pastel-mix](#pastel-mix)

  - [Orange Mix](#orange-mix)

  - [Counterfeit-V2.5](#counterfeit)

  - [anything-v4.5](#anything-v4)

  - [Openjourney](#openjourney)

  - [dreamlike-photoreal-2.0](#dreamlike-photoreal-2)

- [**Image Colorization**](#image-colorization)

  - [DDColor Tiny](#ddcolor-tiny)

- [**Face Recognition**](#face-recognition)

  - [AdaFace IR-18](#adaface-ir-18)

- [**3D Face Pose Estimation**](#3d-face-pose-estimation)

  - [3DDFA_V2](#3ddfa_v2)

- [**Speaker Diarization**](#speaker-diarization)

  - [pyannote segmentation-3.0](#pyannote-segmentation-30)

- [**Voice Conversion**](#voice-conversion)

  - [OpenVoice V2](#openvoice-v2)

- [**Text-to-Music Generation**](#text-to-music-generation)

  - [Stable Audio Open Small](#stable-audio-open-small)

- [**Audio Source Separation**](#audio-source-separation)

  - [HTDemucs](#htdemucs)

- [**Vision-Language**](#vision-language)

  - [Florence-2-base](#florence-2-base)

- [**Zero-Shot Image Classification**](#zero-shot-image-classification)

  - [SigLIP ViT-B/16](#siglip-vit-b16)

# How to get the model

You can get the model converted to CoreML format from the link of Google drive.

See the section below for how to use it in Xcode.

The license for each model conforms to the license for the original project.

# Image Classifier

### Efficientnet



| Google Drive Link | Size | Dataset |Original Project | License |

| ------------- | ------------- | ------------- |------------- |------------- |

| [Efficientnetb0](https://drive.google.com/file/d/1mJq8SMuDaCQHW77ui3fAfe5o3Qu2GKMi/view?usp=sharing) | 22.7 MB | ImageNet | [TensorFlowHub](https://tfhub.dev/tensorflow/efficientnet/b0/classification/1)  |[Apache2.0](https://opensource.org/licenses/Apache-2.0)|

### Efficientnetv2



| Google Drive Link | Size | Dataset |Original Project | License | Year|

| ------------- | ------------- | ------------- |------------- |------------- |------------- |

| [Efficientnetv2](https://drive.google.com/file/d/12JiGwXh8pX3yjoG_GsJOKAnPd3lbVrrn/view?usp=sharing) | 85.8 MB | ImageNet | [Google/autoML](https://github.com/google/automl/tree/master/efficientnetv2)  | [Apache2.0](https://github.com/google/automl/blob/master/LICENSE)|2021|

### VisionTransformer

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.



| Google Drive Link | Size | Dataset |Original Project | License |Year|

| ------------- | ------------- | ------------- |------------- |------------- |------------- |

| [VisionTransformer-B16](https://drive.google.com/file/d/1VPo8Cjv7dyicM4lcJ6TgxnD4AN3ldMQp/view?usp=sharing) | 347.5 MB | ImageNet | [google-research/vision_transformer](https://github.com/google-research/vision_transformer)  | [Apache2.0](https://github.com/google-research/vision_transformer/blob/main/LICENSE)|2021|

### Conformer

Local Features Coupling Global Representations for Visual Recognition.



| Google Drive Link | Size | Dataset |Original Project | License |Year|

| ------------- | ------------- | ------------- |------------- |------------- |------------- |

| [Conformer-tiny-p16](https://drive.google.com/file/d/1-4qVbuTYr4r4o08656iGtV8KKblAVVyr/view?usp=sharing) | 94.1 MB | ImageNet | [pengzhiliang/Conformer](https://github.com/pengzhiliang/Conformer)  | [Apache2.0](https://github.com/google-research/vision_transformer/blob/main/LICENSE)|2021|

### DeiT

Data-efficient Image Transformers



| Google Drive Link | Size | Dataset |Original Project | License |Year|

| ------------- | ------------- | ------------- |------------- |------------- |------------- |

| [DeiT-base384](https://drive.google.com/file/d/1-7J-b0fTjmZi2VDPrDCWKBsCYGxYP5yW/view?usp=sharing) | 350.5 MB | ImageNet | [facebookresearch/deit](https://github.com/facebookresearch/deit)  | [Apache2.0](https://github.com/facebookresearch/deit/blob/main/LICENSE)|2021|

### RepVGG

Making VGG-style ConvNets Great Again



| Google Drive Link | Size | Dataset |Original Project | License |Year|

| ------------- | ------------- | ------------- |------------- |------------- |------------- |

| [RepVGG-A0](https://drive.google.com/file/d/1i8mDvRGn2_OjzIG9ioVJyQrefVliKsh_/view?usp=sharing) | 33.3 MB | ImageNet | [DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)  | [MIT](https://github.com/DingXiaoH/RepVGG/blob/main/LICENSE)|2021|

### RegNet

Designing Network Design Spaces



| Google Drive Link | Size | Dataset |Original Project | License |Year|

| ------------- | ------------- | ------------- |------------- |------------- |------------- |

| [regnet_y_400mf](https://drive.google.com/file/d/16jbUJ4gHSzdxxbYb99rOQe0FiKCuLyDB/view?usp=sharing) | 16.5 MB | ImageNet | [TORCHVISION.MODELS](https://pytorch.org/vision/stable/models.html#torchvision-models)  | [MIT](https://github.com/facebookresearch/pycls/blob/main/LICENSE)|2020|

### MobileViTv2

CVNets: A library for training computer vision networks



| Google Drive Link | Size | Dataset |Original Project | License |Year|Conversion Script|

| ------------- | ------------- | ------------- |------------- |------------- |------------- |------------- |

| [MobileViTv2](https://drive.google.com/file/d/1__aG67p6o5-NIchkHpfFJBszCpIhI0uf/view?usp=share_link) | 18.8 MB | ImageNet | [apple/ml-cvnets](https://github.com/apple/ml-cvnets)  | [apple](https://github.com/apple/ml-cvnets/blob/main/LICENSE)|2022|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)]([https://colab.research.google.com/drive/1QiTlFsN948Xt2e4WgqUB8DnGgwWwtVZS?usp=sharing](https://colab.research.google.com/drive/1UQwhFpVP_4Q9I6LXPdBSS0VDhIRdUBQA?usp=sharing)) |

# Object Detection

### D-FINE



| Download Link | Size | Output | Original Project | License | Note | Sample Project |

| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |

|[dfine-n-coco](https://github.com/john-rocky/peaceofcake/releases/download/v0.2.0/dfine_n_coco.mlpackage.zip)|13MB| Confidence(MultiArray (Float32 300 × 80)), Coordinates (MultiArray (Float32 300 × 4)) |[Peterande/D-FINE](https://github.com/Peterande/D-FINE)|[Apache 2.0](https://github.com/Peterande/D-FINE/blob/master/LICENSE)|Input 640×640. Coordinates are normalized cxcywh. No NMS — filter by confidence threshold.| [peaceofcake DFINEDemo](https://github.com/john-rocky/peaceofcake/tree/main/DFINEDemo) |

### RF-DETR



| Download Link | Size | Output | Original Project | License | Note | Sample Project |

| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |

|[rfdetr-n-coco](https://github.com/john-rocky/peaceofcake/releases/download/v0.2.0/rfdetr_n_coco.mlpackage.zip)|95MB| Confidence(MultiArray (Float32 300 × 91)), Coordinates (MultiArray (Float32 300 × 4)) |[roboflow/rf-detr](https://github.com/roboflow/rf-detr)|[Apache 2.0](https://github.com/roboflow/rf-detr/blob/main/LICENSE)|Input 384×384. 91 classes (index 0 = background, 1-90 = COCO category IDs). Coordinates are normalized cxcywh. No NMS.| [peaceofcake DFINEDemo](https://github.com/john-rocky/peaceofcake/tree/main/DFINEDemo) |

### YOLOv5s



| Google Drive Link | Size | Output | Original Project | License | Note | Sample Project |

| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |

|[YOLOv5s](https://drive.google.com/file/d/1KT-9eKO4F-LYIJVYJg7dy2LEW_hVUq0M/view?usp=sharing)|29.3MB| Confidence(MultiArray (Double 0 × 80)), Coordinates (MultiArray (Double 0 × 4)) |[ultralytics/yolov5](https://github.com/ultralytics/yolov5)|[GNU](https://github.com/ultralytics/yolov5/blob/master/LICENSE)|Non Maximum Suppression has been added.| [CoreML-YOLOv5](https://github.com/john-rocky/CoreML-YOLOv5) |

### YOLOv7



| Google Drive Link | Size | Output | Original Project | License | Note | Sample Project | Conversion Script |

| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |------------- |

|[YOLOv7](https://drive.google.com/file/d/1EKBC7tiwP1tDvXUm_ldD1Nq7hW8HofLe/view?usp=sharing)|147.9MB| Confidence(MultiArray (Double 0 × 80)), Coordinates (MultiArray (Double 0 × 4)) |[WongKinYiu/yolov7](https://github.com/WongKinYiu/yolov7)|[GNU](https://github.com/WongKinYiu/yolov7/blob/main/LICENSE.md)|Non Maximum Suppression has been added.| [CoreML-YOLOv5](https://github.com/john-rocky/CoreML-YOLOv5) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1QiTlFsN948Xt2e4WgqUB8DnGgwWwtVZS?usp=sharing) |

### YOLOv8



| Google Drive Link | Size | Output | Original Project | License | Note | Sample Project | 

| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |

|[YOLOv8s](https://drive.google.com/file/d/1pLRh1Y37KLEMpQn3v8qH-A12swakoHbI/view?usp=share_link)|45.1MB| Confidence(MultiArray (Double 0 × 80)), Coordinates (MultiArray (Double 0 × 4)) |[ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)|[GNU](https://github.com/ultralytics/ultralytics/blob/main/LICENSE)|Non Maximum Suppression has been added.| [CoreML-YOLOv5](https://github.com/john-rocky/CoreML-YOLOv5) |

### YOLOv9

YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. Uses PGI and GELAN architecture for efficient object detection.

| Download Link | Size | Output | Original Project | License | Year | Note | Sample Project |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [yolov9s.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/yolo-models-v1/yolov9s.mlpackage.zip) | 14 MB | Confidence (MultiArray (Double 0 × 80)), Coordinates (MultiArray (Double 0 × 4)) | [WongKinYiu/yolov9](https://github.com/WongKinYiu/yolov9) | [GPL-3.0](https://github.com/WongKinYiu/yolov9/blob/main/LICENSE.md) | 2024 | Non Maximum Suppression has been added. | [YOLOv9Demo](sample_apps/YOLOv9Demo) |

### YOLOv10

YOLOv10: Real-Time End-to-End Object Detection. NMS-free architecture using consistent dual assignments — no post-processing needed.

| Download Link | Size | Output | Original Project | License | Year | Note | Sample Project |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [yolov10s.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/yolo-models-v1/yolov10s.mlpackage.zip) | 14 MB | MultiArray (1 × 300 × 6) | [THU-MIG/yolov10](https://github.com/THU-MIG/yolov10) | [AGPL-3.0](https://github.com/THU-MIG/yolov10/blob/main/LICENSE) | 2024 | NMS-free end-to-end detection. | [YOLO26Demo](sample_apps/YOLO26Demo) |

### YOLO11

YOLO11: Ultralytics latest YOLO with improved backbone and neck architecture. 22% fewer parameters than YOLOv8 with higher mAP.

| Download Link | Size | Output | Original Project | License | Year | Note | Sample Project |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [yolo11s.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/yolo-models-v1/yolo11s.mlpackage.zip) | 18 MB | Confidence (MultiArray (Double 0 × 80)), Coordinates (MultiArray (Double 0 × 4)) | [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) | [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) | 2024 | Non Maximum Suppression has been added. | [YOLOv9Demo](sample_apps/YOLOv9Demo) |

### YOLO26

YOLO26: Edge-first vision AI with NMS-free end-to-end detection. Up to 43% faster CPU inference vs YOLO11 with DFL removal and ProgLoss.



| Download Link | Size | Output | Original Project | License | Year | Note | Sample Project |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [yolo26s.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/yolo-models-v1/yolo26s.mlpackage.zip) | 18 MB | MultiArray (1 × 300 × 6) | [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) | [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) | 2026 | NMS-free end-to-end detection. | [YOLO26Demo](sample_apps/YOLO26Demo) |

### YOLO-World

YOLO-World: Real-Time Open-Vocabulary Object Detection. Type any text query and detect it — no fixed class list. Uses CLIP text encoder for open-vocabulary matching.



| Download Link | Size | Description | Original Project | License | Year | Sample Project |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [yoloworld_detector.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/yolo-models-v1/yoloworld_detector.mlpackage.zip) | 25 MB | YOLO-World V2-S visual detector | [AILab-CVC/YOLO-World](https://github.com/AILab-CVC/YOLO-World) | [GPL-3.0](https://github.com/AILab-CVC/YOLO-World/blob/master/LICENSE) | 2024 | [YOLOWorldDemo](sample_apps/YOLOWorldDemo) |

| [clip_text_encoder.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/yolo-models-v1/clip_text_encoder.mlpackage.zip) | 121 MB | CLIP ViT-B/32 text encoder | [openai/CLIP](https://github.com/openai/CLIP) | [MIT](https://github.com/openai/CLIP/blob/main/LICENSE) | 2021 | — |

| [clip_vocab.json.zip](https://github.com/john-rocky/CoreML-Models/releases/download/yolo-models-v1/clip_vocab.json.zip) | 1.6 MB | BPE vocabulary for tokenizer | — | — | — | — |

# Segmentation

### [U2Net](https://drive.google.com/file/d/1cpm-x12Ih7Cqd_kOjfTvtt4ipGS3BpCx/view?usp=sharing)

 

| Google Drive Link | Size | Output |Original Project | License |

| ------------- | ------------- | ------------- | ------------- |------------- |

| [U2Net](https://drive.google.com/file/d/1cpm-x12Ih7Cqd_kOjfTvtt4ipGS3BpCx/view?usp=sharing) | 175.9 MB | Image(GRAYSCALE 320 × 320)| [xuebinqin/U-2-Net](https://github.com/xuebinqin)  | [Apache](https://github.com/john-rocky/CoreML-Models/blob/master/Apache-LICENSE)|

| [U2Netp](https://drive.google.com/file/d/1D-quPGy33PzSEC6A7EBNv7mCyuiBlO08/view?usp=sharing) | 4.6 MB | Image(GRAYSCALE 320 × 320) | [xuebinqin/U-2-Net](https://github.com/xuebinqin)  |  [Apache](https://github.com/john-rocky/CoreML-Models/blob/master/Apache-LICENSE)|

### [IS-Net](https://drive.google.com/drive/folders/13CkOTBCYc3FjGTU26lmCsRYsOkeHnAMA?usp=sharing)

 

 

| Google Drive Link | Size | Output |Original Project | License | Year | Conversion Script |

| ------------- | ------------- | ------------- | ------------- |------------- | ------------- |------------- |

| [IS-Net](https://drive.google.com/drive/folders/13CkOTBCYc3FjGTU26lmCsRYsOkeHnAMA?usp=sharing) | 176.1 MB | Image(GRAYSCALE 1024 × 1024)| [xuebinqin/DIS](https://github.com/xuebinqin/DIS)  | [Apache](https://github.com/xuebinqin/DIS/blob/main/LICENSE.md)| 2022 |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1xWD7LZbI-_09LXmiYMdhA28V2qujvOlZ?usp=sharing)|

| [IS-Net-General-Use](https://drive.google.com/file/d/1Vglh1zPwTglroMvycnkLdFP6nCHf_GuH/view?usp=sharing) | 176.1 MB | Image(GRAYSCALE 1024 × 1024)| [xuebinqin/DIS](https://github.com/xuebinqin/DIS)  | [Apache](https://github.com/xuebinqin/DIS/blob/main/LICENSE.md)| 2022 |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1xWD7LZbI-_09LXmiYMdhA28V2qujvOlZ?usp=sharing)|

### RMBG1.4

RMBG1.4 - The IS-Net enhanced with our unique training scheme and proprietary dataset. 

 

| Download Link | Size | Output |Original Project | License | year  | Sample Project | Conversion Script |

| ------------- | ------------- | ------------- |------------- | ------------- | ------------- |------------- |------------- |

| [RMBG_1_4.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/rmbg-v1/RMBG_1_4.mlpackage.zip) | 42 MB (INT8) | Alpha mask 1024x1024 |[briaai/RMBG-1.4](https://huggingface.co/briaai/RMBG-1.4) | [Creative Commons](https://huggingface.co/briaai/RMBG-1.4) |2024| [RMBGDemo](sample_apps/RMBGDemo) | [convert_rmbg.py](conversion_scripts/convert_rmbg.py) |

### face-Parsing

 

| Google Drive Link | Size | Output |Original Project | License | Sample Project |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [face-Parsing](https://drive.google.com/file/d/1I_cu8x0k6d1AEV_VPLyMu3Pqg3hwmo7g/view?usp=sharing) | 53.2 MB | MultiArray(1 x 512 × 512)| [zllrunning/face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch)  | [MIT](https://github.com/zllrunning/face-parsing.PyTorch/blob/master/LICENSE)|[CoreML-face-parsing](https://github.com/john-rocky/CoreML-Face-Parsing) |

### Segformer

Simple and Efficient Design for Semantic Segmentation with Transformers

 

| Google Drive Link | Size | Output |Original Project | License | year |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [SegFormer_mit-b0_1024x1024_cityscapes](https://drive.google.com/file/d/1-lcNjJM85DZh5-xQv4jlKL6I1ZMBk2uu/view?usp=sharing) | 14.9 MB | MultiArray(512 × 1024)| [NVlabs/SegFormer](https://github.com/NVlabs/SegFormer)  | [NVIDIA](https://github.com/NVlabs/SegFormer/blob/master/LICENSE)|2021|

### BiSeNetV2	

Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation

 

| Google Drive Link | Size | Output |Original Project | License | year |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [BiSeNetV2_1024x1024_cityscapes](https://drive.google.com/file/d/1-20x0-TP8zqXCzDhH06TyL03SJRFYY9n/view?usp=sharing) | 12.8 MB | MultiArray | [ycszen/BiSeNet](https://github.com/ycszen/BiSeNet)  | Apache2.0 |2021|

### DNL

Disentangled Non-Local Neural Networks

 

| Google Drive Link | Size | Output |Dataset|Original Project | License | year |

| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |

| [dnl_r50-d8_512x512_80k_ade20k](https://drive.google.com/file/d/1DOnPGocotsjXknBuNqikgpFVpmH6s_E3/view?usp=sharing) | 190.8 MB | MultiArray[512x512] |ADE20K| [yinmh17/DNL-Semantic-Segmentation](https://github.com/yinmh17/DNL-Semantic-Segmentation)  | [Apache2.0](https://github.com/yinmh17/DNL-Semantic-Segmentation/blob/master/LICENSE) |2020|

### ISANet

Interlaced Sparse Self-Attention for Semantic Segmentation

 

| Google Drive Link | Size | Output |Dataset|Original Project | License | year |

| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |

| [isanet_r50-d8_512x512_80k_ade20k](https://drive.google.com/file/d/114ypGU9S1BOT2otl7P_gsmZbA3bCmz5K/view?usp=sharing) | 141.5 MB | MultiArray[512x512] |ADE20K| [openseg-group/openseg.pytorch](https://github.com/openseg-group/openseg.pytorch) | [MIT](https://github.com/openseg-group/openseg.pytorch/blob/master/LICENSE) |ArXiv'2019/IJCV'2021|

### FastFCN

Rethinking Dilated Convolution in the Backbone for Semantic Segmentation

 

| Google Drive Link | Size | Output |Dataset|Original Project | License | year |

| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |

| [fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k](https://drive.google.com/file/d/1-2CUR1M-a4xzUxdf5enU_9cUdxONmFbT/view?usp=sharing) | 326.2 MB | MultiArray[512x512] |ADE20K| [wuhuikai/FastFCN](https://github.com/wuhuikai/FastFCN) | [MIT](https://github.com/wuhuikai/FastFCN/blob/master/LICENSE) |ArXiv'2019|

### GCNet

Non-local Networks Meet Squeeze-Excitation Networks and Beyond

 

| Google Drive Link | Size | Output |Dataset|Original Project | License | year |

| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |

| [gcnet_r50-d8_512x512_20k_voc12aug](https://drive.google.com/file/d/1-DfjorbUDFXOVasSPoGk7GP1XC_OnNVT/view?usp=sharing) | 189 MB | MultiArray[512x512] |PascalVOC| [xvjiarui/GCNet](https://github.com/xvjiarui/GCNet) | [Apache License 2.0](https://github.com/xvjiarui/GCNet/blob/master/LICENSE) |ICCVW'2019/TPAMI'2020|

### DANet

Dual Attention Network for Scene Segmentation(CVPR2019)

 

| Google Drive Link | Size | Output |Dataset|Original Project | License | year |

| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |

| [danet_r50-d8_512x1024_40k_cityscapes](https://drive.google.com/file/d/1A45r_725V7edPTSrjA4T-T03rPD6Sj2z/view?usp=sharing) | 189.7 MB | MultiArray[512x1024] |CityScapes| [junfu1115/DANet](https://github.com/junfu1115/DANet/) | [MIT](https://github.com/junfu1115/DANet/blob/master/LICENSE) |CVPR2019|

### Semantic-FPN

Panoptic Feature Pyramid Networks

 

| Google Drive Link | Size | Output |Dataset|Original Project | License | year |

| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |

| [fpn_r50_512x1024_80k_cityscapes](https://drive.google.com/file/d/1_IVhCnJ--54P7qVGLo8-ks_LRGXJQXht/view?usp=sharing) | 108.6 MB | MultiArray[512x1024] |CityScapes| [facebookresearch/detectron2](https://github.com/facebookresearch/detectron2) | [Apache License 2.0](https://github.com/facebookresearch/detectron2/blob/main/LICENSE) |2019|

### cloths_segmentation

Code for binary segmentation of various cloths.

 

| Google Drive Link | Size | Output |Dataset|Original Project | License | year |

| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |

| [clothSegmentation](https://drive.google.com/file/d/1-2AydEgkth6UTD5bu13R0fJYoqZZMG3e/view?usp=sharing) | 50.1 MB | Image(GrayScale 640x960) |[fashion-2019-FGVC6](https://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6)| [facebookresearch/detectron2](https://github.com/facebookresearch/detectron2) | [MIT](https://github.com/ternaus/cloths_segmentation/blob/main/LICENSE) |2020|

### easyportrait

EasyPortrait - Face Parsing and Portrait Segmentation Dataset.

 

| Google Drive Link | Size | Output |Original Project | License | year | Swift sample |Conversion Script |

| ------------- | ------------- | ------------- |------------- | ------------- | ------------- |------------- |------------- |

| [easyportrait-segformer512-fp](https://drive.google.com/drive/folders/13BUhNpQHodAgcj6eJaPbzuSUaFn3JuU-?usp=sharing) | 7.6 MB | Image(GrayScale 512x512) * 9 |[hukenovs/easyportrait](https://github.com/hukenovs/easyportrait) | [Creative Commons](https://github.com/hukenovs/easyportrait/tree/main/license) |2023|[easyportrait-coreml](https://github.com/john-rocky/easyportrait-coreml)|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/11a3XWFA8fa8V0a2zgWFqOMUaZgF4O1qt?usp=sharing)|

### MobileSAM

Faster Segment Anything: Towards Lightweight SAM for Mobile Applications. MobileSAM replaces the heavy ViT-H image encoder with a lightweight ViT-Tiny encoder via decoupled knowledge distillation, making it ~60x smaller and ~40x faster than the original SAM.



| Download Link | Size | Output | Original Project | License | Year | Sample Project |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [MobileSAM.zip](https://github.com/john-rocky/SamKit/releases/download/v1.0.0/MobileSAM.zip) | 23 MB (Encoder 13 MB + Decoder 9.8 MB) | Segmentation Mask | [ChaoningZhang/MobileSAM](https://github.com/ChaoningZhang/MobileSAM) | [Apache 2.0](https://github.com/ChaoningZhang/MobileSAM/blob/master/LICENSE) | 2023 | [SamKit](https://github.com/john-rocky/SamKit) |

### SAM2-Tiny

SAM 2: Segment Anything in Images and Videos. SAM 2 extends promptable segmentation from images to videos using a streaming architecture with memory. The Tiny variant uses a Hiera-T backbone for efficient on-device inference.

| Download Link | Size | Output | Original Project | License | Year | Sample Project |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [SAM2Tiny.zip](https://github.com/john-rocky/SamKit/releases/download/v1.0.0/SAM2Tiny.zip) | 76 MB (ImageEncoder 64 MB + PromptEncoder 2 MB + MaskDecoder 9.8 MB) | Segmentation Mask | [facebookresearch/sam2](https://github.com/facebookresearch/sam2) | [Apache 2.0](https://github.com/facebookresearch/sam2/blob/main/LICENSE) | 2024 | [SamKit](https://github.com/john-rocky/SamKit) |

# Super Resolution

### [Real ESRGAN](https://drive.google.com/file/d/1cpm-x12Ih7Cqd_kOjfTvtt4ipGS3BpCx/view?usp=sharing)

  

| Google Drive Link | Size | Output |Original Project | License | year |

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |

| [Real ESRGAN4x](https://drive.google.com/file/d/16JEWh48fgQc8az7avROePOd-PYda0Yi2/view?usp=sharing) | 66.9 MB | Image(RGB 2048x2048)| [xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)  | [BSD 3-Clause License](https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE) |2021|

| [Real ESRGAN Anime4x](https://drive.google.com/file/d/1qXdLx46Lpqya7Txc5Wvgkd2Dqlnqm3Qm/view?usp=sharing) | 66.9 MB | Image(RGB 2048x2048)| [xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)  | [BSD 3-Clause License](https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE) |2021|

### [GFPGAN](https://drive.google.com/file/d/1-3fF4aPnh8ygUOmKItIrZ318xI9JGmQx/view?usp=sharing)

Towards Real-World Blind Face Restoration with Generative Facial Prior

  

| Google Drive Link | Size | Output |Original Project | License |year |

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |

| [GFPGAN](https://drive.google.com/file/d/1-3fF4aPnh8ygUOmKItIrZ318xI9JGmQx/view?usp=sharing) | 337.4 MB | Image(RGB 512x512)| [TencentARC/GFPGAN](https://github.com/TencentARC/GFPGAN)  | [Apache2.0](https://github.com/TencentARC/GFPGAN/blob/master/LICENSE) |2021|

### [BSRGAN](https://drive.google.com/file/d/1-3K89vJZ5OUAh4xdSAifgnL52jbl2fVf/view?usp=sharing)

  

| Google Drive Link | Size | Output |Original Project | License |year |

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |

| [BSRGAN](https://drive.google.com/file/d/1-3K89vJZ5OUAh4xdSAifgnL52jbl2fVf/view?usp=sharing) | 66.9 MB | Image(RGB 2048x2048)| [cszn/BSRGAN](https://github.com/cszn/BSRGAN)  |  |2021|

### [A-ESRGAN](https://drive.google.com/file/d/1-0rKVQtFXNWfIBIpvyemjuO3O00GZBeb/view?usp=sharing)

  

| Google Drive Link | Size | Output |Original Project | License |year |Conversion Script|

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |------------- |

| [A-ESRGAN](https://drive.google.com/file/d/1-0rKVQtFXNWfIBIpvyemjuO3O00GZBeb/view?usp=sharing) | 63.8 MB | Image(RGB 1024x1024)| [aesrgan/A-ESRGANN](https://github.com/aesrgan/A-ESRGAN)  | [BSD 3-Clause License](https://github.com/aesrgan/A-ESRGAN/blob/main/LICENSE) |2021|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1UxtSXnVYOXEfTVdIeoP7HQEjsyVbqOKa?usp=sharing)|

### [Beby-GAN](https://drive.google.com/file/d/1bJ7_NgR2KXI46JiFk5hH_6IdCHMyhN05/view?usp=sharing)

Best-Buddy GANs for Highly Detailed Image Super-Resolution

  

| Google Drive Link | Size | Output |Original Project | License |year |

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |

| [Beby-GAN](https://drive.google.com/file/d/1bJ7_NgR2KXI46JiFk5hH_6IdCHMyhN05/view?usp=sharing) | 66.9 MB | Image(RGB 2048x2048)| [dvlab-research/Simple-SR](https://github.com/dvlab-research/Simple-SR)  | [MIT](https://github.com/dvlab-research/Simple-SR/blob/master/LICENSE) |2021|

### [RRDN](https://drive.google.com/file/d/1-M30vR0xMuYDn2p5O4KZrUnUXy4SNThF/view?usp=sharing)

The Residual in Residual Dense Network for image super-scaling.

 

| Google Drive Link | Size | Output |Original Project | License |year |

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |

| [RRDN](https://drive.google.com/file/d/1-M30vR0xMuYDn2p5O4KZrUnUXy4SNThF/view?usp=sharing) | 16.8 MB | Image(RGB 2048x2048)| [idealo/image-super-resolution](https://github.com/idealo/image-super-resolution)  | [Apache2.0](https://github.com/idealo/image-super-resolution/blob/master/LICENSE) |2018|

### [Fast-SRGAN](https://drive.google.com/file/d/1gYXbhcSUm5rhcCAmwLruonAhu8jvyDL8/view?usp=sharing)

Fast-SRGAN.

 

| Google Drive Link | Size | Output |Original Project | License |year |

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |

| [Fast-SRGAN](https://drive.google.com/file/d/1gYXbhcSUm5rhcCAmwLruonAhu8jvyDL8/view?usp=sharing) | 628 KB | Image(RGB 1024x1024)| [HasnainRaz/Fast-SRGAN](https://github.com/HasnainRaz/Fast-SRGAN)  | [MIT](https://github.com/HasnainRaz/Fast-SRGAN/blob/master/LICENSE) |2019|

### [ESRGAN](https://drive.google.com/file/d/1fkRbh_gckuFlgr357OIdOrEJK4T_2Xkz/view?usp=sharing)

Enhanced-SRGAN.

 

| Google Drive Link | Size | Output |Original Project | License |year |

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |

| [ESRGAN](https://drive.google.com/file/d/1fkRbh_gckuFlgr357OIdOrEJK4T_2Xkz/view?usp=sharing) | 66.9 MB | Image(RGB 2048x2048)| [xinntao/ESRGAN](https://github.com/xinntao/ESRGAN)  | [Apache 2.0](https://github.com/xinntao/ESRGAN/blob/master/LICENSE) |2018|

### [UltraSharp](https://drive.google.com/drive/folders/1-Q1SdS8iHWTfTs7FE39pUTEubPks30Ca?usp=drive_link)

Pretrained: 4xESRGAN

 

| Google Drive Link | Size | Output |Original Project | License |year |

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |

| [UltraSharp](https://drive.google.com/drive/folders/1-Q1SdS8iHWTfTs7FE39pUTEubPks30Ca?usp=drive_link) | 34 MB | Image(RGB 1024x1024)| [Kim2019/](https://openmodeldb.info/models/4x-UltraSharp)  | [CC-BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.ja) |2021|

### [SRGAN](https://drive.google.com/file/d/1-076W2o0wCtoODptikX1eOnlFBx2s3qK/view?usp=sharing)

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.

 

| Google Drive Link | Size | Output |Original Project | License |year |

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |

| [SRGAN](https://drive.google.com/file/d/1-076W2o0wCtoODptikX1eOnlFBx2s3qK/view?usp=sharing) | 6.1 MB | Image(RGB 2048x2048)| [dongheehand/SRGAN-PyTorch](https://github.com/dongheehand/SRGAN-PyTorch)  |  |2017|

### [SRResNet](https://drive.google.com/file/d/1-2kYZgF_Z6vntrRsHmRiwyHJg5TC1PSW/view?usp=sharing)

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.

 

| Google Drive Link | Size | Output |Original Project | License |year |

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |

| [SRResNet](https://drive.google.com/file/d/1-2kYZgF_Z6vntrRsHmRiwyHJg5TC1PSW/view?usp=sharing) | 6.1 MB | Image(RGB 2048x2048)| [dongheehand/SRGAN-PyTorch](https://github.com/dongheehand/SRGAN-PyTorch)  |  |2017|

### [LESRCNN](https://drive.google.com/file/d/1-0zgxURZwqX0mAAVy69K-owE7QP-7NfJ/view?usp=sharing)

Lightweight Image Super-Resolution with Enhanced CNN.

 

| Google Drive Link | Size | Output |Original Project | License |year | Conversion Script |

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |------------- |

| [LESRCNN](https://drive.google.com/file/d/1-0zgxURZwqX0mAAVy69K-owE7QP-7NfJ/view?usp=sharing) | 4.3 MB | Image(RGB 512x512)| [hellloxiaotian/LESRCNN](https://github.com/hellloxiaotian/LESRCNN)  |  |2020|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Q6piAJvXSmb-DcdFipcRUEYuHi9fnTm7?usp=sharing)|

### [MMRealSR](https://drive.google.com/file/d/1-HwMLvOy_hHycHNhojob6uT8t6tRyWqb/view?usp=sharing)

Metric Learning based Interactive Modulation for Real-World Super-Resolution

 

| Google Drive Link | Size | Output |Original Project | License |year | Conversion Script |

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |------------- |

| [MMRealSRGAN](https://drive.google.com/file/d/1-HwMLvOy_hHycHNhojob6uT8t6tRyWqb/view?usp=sharing) | 104.6 MB | Image(RGB 1024x1024)| [TencentARC/MM-RealSR](https://github.com/TencentARC/MM-RealSR)  | [BSD 3-Clause](https://github.com/TencentARC/MM-RealSR/blob/main/LICENSE) |2022|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1zhUhQhdtP02N2pFIxsO5lin7tDOExZCo?usp=sharing)|

| [MMRealSRNet](https://drive.google.com/file/d/1-77P8AtHFh5kca2kYZ6X7GaUueoa3el_/view?usp=sharing) | 104.6 MB | Image(RGB 1024x1024)| [TencentARC/MM-RealSR](https://github.com/TencentARC/MM-RealSR)  | [BSD 3-Clause](https://github.com/TencentARC/MM-RealSR/blob/main/LICENSE) |2022|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1zhUhQhdtP02N2pFIxsO5lin7tDOExZCo?usp=sharing)|

### [DASR](https://drive.google.com/drive/folders/10J2ehHewK2ppS5ToDqmtJ2Ei5k8vcdL0?usp=sharing)

Pytorch implementation of "Unsupervised Degradation Representation Learning for Blind Super-Resolution", CVPR 2021

 

| Google Drive Link | Size | Output |Original Project | License |year|

| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |

| [DASR](https://drive.google.com/drive/folders/10J2ehHewK2ppS5ToDqmtJ2Ei5k8vcdL0?usp=sharing) | 12.1 MB | Image(RGB 1024x1024)| [The-Learning-And-Vision-Atelier-LAVA/DASR](https://github.com/The-Learning-And-Vision-Atelier-LAVA/DASR)  | [MIT](https://github.com/The-Learning-And-Vision-Atelier-LAVA/DASR/blob/main/LICENSE) |2022|

# Low Light Enhancement

### StableLLVE

Learning Temporal Consistency for Low Light Video Enhancement from Single Images.

  

| Google Drive Link | Size | Output |Original Project | License |Year|

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [StableLLVE](https://drive.google.com/file/d/1-9xry7XeCJYsZadxcfTscjGi_Sna5NhM/view?usp=sharing) | 17.3 MB | Image(RGB 512x512)| [zkawfanx/StableLLVE](https://github.com/zkawfanx/StableLLVE)  | [MIT](https://github.com/zkawfanx/StableLLVE/blob/main/LICENSE) |2021|

### Zero-DCE

Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement

  

| Google Drive Link | Size | Output |Original Project | License |Year|Conversion Script|

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [Zero-DCE](https://drive.google.com/file/d/1-0lxlBNFm8E_y9ImhS2wxq0p1ZJlXyoA/view?usp=sharing) | 320KB | Image(RGB 512x512)| [Li-Chongyi/Zero-DCE](https://github.com/Li-Chongyi/Zero-DCE)  | [See Repo](https://github.com/Li-Chongyi/Zero-DCE) |2021|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1sh3O-4EvYv49Rlm59beH6koHe0sYxc2r?usp=sharing)|

### Retinexformer

Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

  

| Google Drive Link | Size | Output |Original Project | License |Year|Conversion Script|

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [ZRetinexformer FiveK](https://drive.google.com/drive/folders/1ea6vBuLG-z4TAK4iU6vrgABAAlHuDdhy?usp=drive_link) | 3.4MB | Image(RGB 512x512)| [caiyuanhao1998/Retinexformer](https://github.com/caiyuanhao1998/Retinexformer)  | [MIT](https://github.com/caiyuanhao1998/Retinexformer?tab=MIT-1-ov-file#readme) |2023|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/10PtPI4V72Pp6PQZcrah-vClGzjKLaGGK?usp=sharing)|

| [ZRetinexformer NTIRE](https://drive.google.com/drive/folders/14piyZVwzu4Abpfgwh2HIKoubeeE-3qoq?usp=drive_link) | 3.4MB | Image(RGB 512x512)| [caiyuanhao1998/Retinexformer](https://github.com/caiyuanhao1998/Retinexformer)  | [MIT](https://github.com/caiyuanhao1998/Retinexformer?tab=MIT-1-ov-file#readme) |2023|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/10PtPI4V72Pp6PQZcrah-vClGzjKLaGGK?usp=sharing)|

# Image Restoration

### MPRNet

Multi-Stage Progressive Image Restoration.

Debluring

  

Denoising

  

Deraining

  

| Google Drive Link | Size | Output |Original Project | License |Year|

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [MPRNetDebluring](https://drive.google.com/file/d/1--5L6BxxbyYGY9ey5WCIrl7g1yYBN27U/view?usp=sharing) | 137.1 MB | Image(RGB 512x512)| [swz30/MPRNet](https://github.com/swz30/MPRNet)  | [MIT](https://github.com/swz30/MPRNet/blob/main/LICENSE.md) |2021|

| [MPRNetDeNoising](https://drive.google.com/file/d/1-04xou-UgoflZb7MqTBycCpuLWKUAj0i/view?usp=sharing) | 108 MB | Image(RGB 512x512)| [swz30/MPRNet](https://github.com/swz30/MPRNet)  | [MIT](https://github.com/swz30/MPRNet/blob/main/LICENSE.md) |2021|

| [MPRNetDeraining](https://drive.google.com/file/d/1tGvjj49yaDym24vGdGqr1VKOtGd7ALKB/view?usp=sharing) | 24.5 MB | Image(RGB 512x512)| [swz30/MPRNet](https://github.com/swz30/MPRNet)  | [MIT](https://github.com/swz30/MPRNet/blob/main/LICENSE.md) |2021|

### MIRNetv2

Learning Enriched Features for Fast Image Restoration and Enhancement.

Denoising

  

Super Resolution

  

Contrast Enhancement

  

Low Light Enhancement

  

| Google Drive Link | Size | Output |Original Project | License |Year|Conversion Script|

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [MIRNetv2Denoising](https://drive.google.com/file/d/1-HY2AhQV84LUZMadsbIi4TGBhEntAOaF/view?usp=sharing) | 42.5 MB | Image(RGB 512x512)| [swz30/MIRNetv2](https://github.com/swz30/MIRNetv2)  | [ACADEMIC PUBLIC LICENSE](https://github.com/swz30/MIRNetv2/blob/main/LICENSE.md) |2022|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1lSWCn0et08hdS3sgKc40c7VXUvKcqCSi?usp=sharing)|

| [MIRNetv2SuperResolution](https://drive.google.com/file/d/1-BLfJj8xK_bw-GsGLfRR9uMvuA2VOqsh/view?usp=sharing) | 42.5 MB | Image(RGB 512x512)| [swz30/MIRNetv2](https://github.com/swz30/MIRNetv2)  | [ACADEMIC PUBLIC LICENSE](https://github.com/swz30/MIRNetv2/blob/main/LICENSE.md) |2022|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1lSWCn0et08hdS3sgKc40c7VXUvKcqCSi?usp=sharing)|

| [MIRNetv2ContrastEnhancement](https://drive.google.com/file/d/1--q9Decpy1ZZbSifiE26SkpXstoadpM8/view?usp=sharing) | 42.5 MB | Image(RGB 512x512)| [swz30/MIRNetv2](https://github.com/swz30/MIRNetv2)  | [ACADEMIC PUBLIC LICENSE](https://github.com/swz30/MIRNetv2/blob/main/LICENSE.md) |2022|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1lSWCn0et08hdS3sgKc40c7VXUvKcqCSi?usp=sharing)|

| [MIRNetv2LowLightEnhancement](https://drive.google.com/file/d/1Yh3FCogRfQ8k7Hh_UIZAnGwwhXHX6k6P/view?usp=sharing) | 42.5 MB | Image(RGB 512x512)| [swz30/MIRNetv2](https://github.com/swz30/MIRNetv2)  | [ACADEMIC PUBLIC LICENSE](https://github.com/swz30/MIRNetv2/blob/main/LICENSE.md) |2022|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1lSWCn0et08hdS3sgKc40c7VXUvKcqCSi?usp=sharing)|

# Image Generation

### [MobileStyleGAN](https://drive.google.com/drive/folders/1rUV6AXwp8JhPPmkog-0r0AUGzUvN9DmW?usp=sharing)

  

| Google Drive Link | Size | Output | Original Project | License | Sample Project |

| ------------- | ------------- | ------------- | ------------- |  ------------- |  ------------- | 

| [MobileStyleGAN](https://drive.google.com/drive/folders/1rUV6AXwp8JhPPmkog-0r0AUGzUvN9DmW?usp=sharing) | 38.6MB  | Image(Color 1024 × 1024)| [bes-dev/MobileStyleGAN.pytorch](https://github.com/bes-dev/MobileStyleGAN.pytorch)  | [Nvidia Source Code License-NC](https://github.com/bes-dev/MobileStyleGAN.pytorch/blob/develop/LICENSE-NVIDIA) | [CoreML-StyleGAN](https://github.com/john-rocky/CoreML-StyleGAN) |

### [DCGAN](https://drive.google.com/file/d/132GrmmuETSLTml1zWyLUnIksclP-8vGw/view?usp=sharing)



| Google Drive Link | Size | Output | Original Project | 

| ------------- | ------------- | ------------- | ------------- | 

| [DCGAN](https://drive.google.com/file/d/132GrmmuETSLTml1zWyLUnIksclP-8vGw/view?usp=sharing)　| 9.2MB | MultiArray | [TensorFlowCore](https://www.tensorflow.org/tutorials/generative/dcgan)|

# Image2Image

### [Anime2Sketch](https://drive.google.com/file/d/1-52NnZ1kajZI5Rk0tn3DegpU38la_jYk/view?usp=sharing)

 

| Google Drive Link | Size | Output | Original Project | License | Usage |

| ------------- | ------------- | ------------- | ------------- | ------------- |  ------------- | 

| [Anime2Sketch](https://drive.google.com/file/d/1-52NnZ1kajZI5Rk0tn3DegpU38la_jYk/view?usp=sharing) | 217.7MB  | Image(Color 512 × 512)| [Mukosame/Anime2Sketch](https://github.com/Mukosame/Anime2Sketch)  | [MIT](https://github.com/Mukosame/Anime2Sketch/blob/master/LICENSE)| Drop an image to preview|

### [AnimeGAN2Face_Paint_512_v2](https://drive.google.com/file/d/1phSgcAz3LNbk2v2RoSESmr7PFxTAHcxb/view?usp=sharing)

 

| Google Drive Link | Size | Output | Original Project | Conversion Script |

| ------------- | ------------- | ------------- | ------------- |  ------------- | 

| [AnimeGAN2Face_Paint_512_v2](https://drive.google.com/file/d/1phSgcAz3LNbk2v2RoSESmr7PFxTAHcxb/view?usp=sharing) | 8.6MB  | Image(Color 512 × 512)| [bryandlee/animegan2-pytorch](https://github.com/bryandlee/animegan2-pytorch#additional-model-weights)  |[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1WGAxMaikjNIfqdGRndEOmNyeVf33nGNh?usp=sharing) |

### [Photo2Cartoon](https://drive.google.com/file/d/1xFWZ9Rf1o_LtwBpmSw2zSwPGk2FY6Wya/view?usp=sharing)

 

| Google Drive Link | Size | Output | Original Project | License | Note |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | 

| [Photo2Cartoon](https://drive.google.com/file/d/1xFWZ9Rf1o_LtwBpmSw2zSwPGk2FY6Wya/view?usp=sharing) | 15.2 MB  | Image(Color 256 × 256)| [minivision-ai/photo2cartoon](https://github.com/minivision-ai/photo2cartoon) | [MIT](https://github.com/minivision-ai/photo2cartoon/blob/master/LICENSE) | The output is little bit different from the original model. It cause some operations were converted replaced　manually. |

### [AnimeGANv2_Hayao](https://drive.google.com/file/d/1G53oZ1hiMcLJs1loN_fe_VmBVfegh9ha/view?usp=sharing)

 

| Google Drive Link | Size | Output | Original Project | Sample |

| ------------- | ------------- | ------------- | ------------- | ------------- |

| [AnimeGANv2_Hayao](https://drive.google.com/file/d/1G53oZ1hiMcLJs1loN_fe_VmBVfegh9ha/view?usp=sharing)　| 8.7MB | Image(256 x 256) | [TachibanaYoshino/AnimeGANv2](https://github.com/TachibanaYoshino/AnimeGANv2)|[AnimeGANv2-iOS](https://github.com/john-rocky/AnimeGANv2-iOS)|

### [AnimeGANv2_Paprika](https://drive.google.com/file/d/10drMcmF67iREUK8NY8ekMHrsyVirs5XT/view?usp=sharing)

 

| Google Drive Link | Size | Output | Original Project | 

| ------------- | ------------- | ------------- | ------------- | 

| [AnimeGANv2_Paprika](https://drive.google.com/file/d/10drMcmF67iREUK8NY8ekMHrsyVirs5XT/view?usp=sharing)　| 8.7MB | Image(256 x 256) | [TachibanaYoshino/AnimeGANv2](https://github.com/TachibanaYoshino/AnimeGANv2)|

### [WarpGAN Caricature](https://drive.google.com/file/d/1HE3qvfjuXZMFelRcmmGsLzoO5dV8lnaQ/view?usp=sharing)

 

| Google Drive Link | Size | Output | Original Project | 

| ------------- | ------------- | ------------- | ------------- | 

| [WarpGAN Caricature](https://drive.google.com/file/d/1HE3qvfjuXZMFelRcmmGsLzoO5dV8lnaQ/view?usp=sharing)　| 35.5MB | Image(256 x 256) | [seasonSH/WarpGAN](https://github.com/seasonSH/WarpGAN)|

### [UGATIT_selfie2anime](https://drive.google.com/file/d/1o15OO0Kn0tq79fFkmBm3PES93IRQOxB-/view?usp=sharing)

 

| Google Drive Link | Size | Output | Original Project | 

| ------------- | ------------- | ------------- | ------------- | 

| [UGATIT_selfie2anime](https://drive.google.com/file/d/1o15OO0Kn0tq79fFkmBm3PES93IRQOxB-/view?usp=sharing) | 266.2MB(quantized) | Image(256x256) | [taki0112/UGATIT](https://github.com/taki0112/UGATIT)  |

### CartoonGAN

| Google Drive Link | Size | Output | Original Project | 

| ------------- | ------------- | ------------- | ------------- | 

| [CartoonGAN_Shinkai](https://drive.google.com/file/d/1j9bvHFBX5yctSeaE8FEvUv-r-hEVvXwi/view?usp=sharing)　| 44.6MB | MultiArray | [mnicnc404/CartoonGan-tensorflow](https://github.com/mnicnc404/CartoonGan-tensorflow)|

| [CartoonGAN_Hayao](https://drive.google.com/file/d/1-2dTGge4fza-TTBI9actkg_xp91zYT-F/view?usp=sharing)　| 44.6MB | MultiArray | [mnicnc404/CartoonGan-tensorflow](https://github.com/mnicnc404/CartoonGan-tensorflow)|

| [CartoonGAN_Hosoda](https://drive.google.com/file/d/1-5VB1g7kRt0KMe6u37fi_t18l-Zn_wr1/view?usp=sharing)　| 44.6MB | MultiArray | [mnicnc404/CartoonGan-tensorflow](https://github.com/mnicnc404/CartoonGan-tensorflow)|

| [CartoonGAN_Paprika](https://drive.google.com/file/d/1-5x3TYugodcnGYiEEDitFqMQPVHsCDs_/view?usp=sharing)　| 44.6MB | MultiArray | [mnicnc404/CartoonGan-tensorflow](https://github.com/mnicnc404/CartoonGan-tensorflow)|

### [Fast-Neural-Style-Transfer](https://drive.google.com/file/d/1o15OO0Kn0tq79fFkmBm3PES93IRQOxB-/view?usp=sharing)

 

 

 

| Google Drive Link | Size | Output |Original Project | License |Year|

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [fast-neural-style-transfer-cuphead](https://drive.google.com/file/d/1-LLQF8T6MrcpdiYZkdGZAizkj7c-lJ9e/view?usp=sharing) | 6.4MB | Image(RGB 960x640)| [eriklindernoren/Fast-Neural-Style-Transfer](https://github.com/eriklindernoren/Fast-Neural-Style-Transfer)  | [MIT](https://github.com/eriklindernoren/Fast-Neural-Style-Transfer/blob/master/LICENSE) |2019|

| [fast-neural-style-transfer-starry-night](https://drive.google.com/file/d/1-HLHIrV_WwZJsEkZ34nTfqnlIHIe04Vy/view?usp=sharing) |  6.4MB | Image(RGB 960x640)| [eriklindernoren/Fast-Neural-Style-Transfer](https://github.com/eriklindernoren/Fast-Neural-Style-Transfer)  | [MIT](https://github.com/eriklindernoren/Fast-Neural-Style-Transfer/blob/master/LICENSE) |2019|

| [fast-neural-style-transfer-mosaic](https://drive.google.com/file/d/1-GmnewjDz2Cs7-CfXPSFIgOruQvBbK2X/view?usp=sharing) |  6.4MB | Image(RGB 960x640)| [eriklindernoren/Fast-Neural-Style-Transfer](https://github.com/eriklindernoren/Fast-Neural-Style-Transfer)  | [MIT](https://github.com/eriklindernoren/Fast-Neural-Style-Transfer/blob/master/LICENSE) |2019|

### [White_box_Cartoonization](https://drive.google.com/file/d/1QGNJzEp0fo6oOryTos1dazEKaS34WzZC/view?usp=sharing)

Learning to Cartoonize Using White-box Cartoon Representations

 

| Google Drive Link | Size | Output | Original Project | License |Year|

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | 

| [White_box_Cartoonization](https://drive.google.com/file/d/1QGNJzEp0fo6oOryTos1dazEKaS34WzZC/view?usp=sharing) | 5.9MB | Image(1536x1536) | [SystemErrorWang/White-box-Cartoonization](https://github.com/SystemErrorWang/White-box-Cartoonization)  |[creativecommons](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)|CVPR2020|

### [FacialCartoonization](https://drive.google.com/file/d/1CJH4tuR3ArKvxrmAE_44lbsAwUzjtyXi/view?usp=sharing)

White-box facial image cartoonizaiton

 

| Google Drive Link | Size | Output | Original Project | License |Year|

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | 

| [FacialCartoonization](https://drive.google.com/file/d/1CJH4tuR3ArKvxrmAE_44lbsAwUzjtyXi/view?usp=sharing) | 8.4MB | Image(256x256) | [SystemErrorWang/FacialCartoonization](https://github.com/SystemErrorWang/FacialCartoonization)  |[creativecommons](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)|2020|

# Inpainting

### AOT-GAN-for-Inpainting



| Google Drive Link | Size | Output | Original Project | License | Note | Sample Project |

| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |

|[AOT-GAN-for-Inpainting](https://drive.google.com/file/d/16rF46DFcDPherlpgjuL60065xcP2N3nv/view?usp=share_link)|60.8MB| MLMultiArray(3,512,512) |[researchmm/AOT-GAN-for-Inpainting](https://github.com/researchmm/AOT-GAN-for-Inpainting)|[Apache2.0](https://github.com/open-mmlab/mmediting/blob/master/LICENSE)|To use see sample.| [john-rocky/Inpainting-CoreML](https://github.com/john-rocky/Inpainting-CoreML) |

### [Lama](https://drive.google.com/drive/folders/1s_uICJQykFFxgVubpBNeLLDL0JsxgdCd?usp=sharing)



| Google Drive Link | Size | Input | Output | Original Project | License | Note | Sample Project | Conversion Script |

| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |------------- |------------- |

|[Lama](https://drive.google.com/drive/folders/1s_uICJQykFFxgVubpBNeLLDL0JsxgdCd?usp=sharing)|216.6MB| Image (Color 800 × 800), Image (GrayScale 800 × 800)| Image (Color 800 × 800) |[advimman/lama](https://github.com/advimman/lama)|[Apache2.0](https://github.com/advimman/lama/blob/main/LICENSE)|To use see sample.| [john-rocky/lama-cleaner-iOS](https://github.com/john-rocky/lama-cleaner-iOS) | [mallman/CoreMLaMa](https://github.com/mallman/CoreMLaMa)|

# Monocular Depth Estimation

### [MiDaS](https://drive.google.com/file/d/1agGnt5Cq5CGzoNDl9Nb-3u7pB5SrIbN4/view?usp=share_link)

Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer

 

| Google Drive Link | Size | Output | Original Project | License |Year|Conversion Script |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | 

| [MiDaS_Small](https://drive.google.com/file/d/1agGnt5Cq5CGzoNDl9Nb-3u7pB5SrIbN4/view?usp=share_link) | 66.3MB | MultiArray(1x256x256) | [isl-org/MiDaS](https://github.com/isl-org/MiDaS)  |[MIT](https://github.com/isl-org/MiDaS/blob/master/LICENSE)|2022|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/13cVDO6gYdQvbKimcfbgGOfuoQmrTbarU?usp=sharing) |

# Stable Diffusion

### [stable-diffusion-v1-5](https://drive.google.com/file/d/1dqYEdhSPi7y0Dgans-Fk7_ViNviUTUJj/view?usp=share_link)



| Google Drive Link  | Original Model |Original Project | License | Run on mac |Conversion Script |Year|

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | 

| [stable-diffusion-v1-5](https://drive.google.com/file/d/1dqYEdhSPi7y0Dgans-Fk7_ViNviUTUJj/view?usp=share_link) |[runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)|[runwayml/stable-diffusion](https://github.com/runwayml/stable-diffusion)  |[Open RAIL M license](https://huggingface.co/runwayml/stable-diffusion-v1-5)|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion)|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-Stable-Diffusion-models-to-Core-ML#requirements) |2022|

### [pastel-mix](https://drive.google.com/file/d/1cp3VoF1R-as8_lScWGUoxl-BNVX3d7vb/view?usp=share_link)

Pastel Mix - a stylized latent diffusion model.This model is intended to produce high-quality, highly detailed anime style with just a few prompts.



| Google Drive Link  | Original Model | License | Run on mac |Conversion Script |Year|

| ------------- | ------------- | ------------- |  ------------- | ------------- | ------------- | 

| [pastelMixStylizedAnime_pastelMixPrunedFP16](https://drive.google.com/file/d/1cp3VoF1R-as8_lScWGUoxl-BNVX3d7vb/view?usp=share_link) |[andite/pastel-mix](https://huggingface.co/andite/pastel-mix)|[Fantasy.ai](https://huggingface.co/andite/pastel-mix)|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion)|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-Stable-Diffusion-models-to-Core-ML#requirements) |2023|

### [Orange Mix](https://drive.google.com/file/d/1ueU-RuZIsl3b3F7uu_gBa_SfAtGTzTI5/view?usp=share_link)



| Google Drive Link  | Original Model | License | Run on mac |Conversion Script |Year|

| ------------- | ------------- | ------------- |  ------------- | ------------- | ------------- | 

| [AOM3_orangemixs](https://drive.google.com/file/d/1ueU-RuZIsl3b3F7uu_gBa_SfAtGTzTI5/view?usp=share_link) |[WarriorMama777/OrangeMixs](https://huggingface.co/WarriorMama777/OrangeMixs)|[CreativeML OpenRAIL-M](https://huggingface.co/WarriorMama777/OrangeMixs)|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion)|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-Stable-Diffusion-models-to-Core-ML#requirements) |2023|

### [Counterfeit](https://drive.google.com/file/d/1Kt_8hnGUGnJAUnuergLki37GKnWjWOJp/view?usp=share_link)



| Google Drive Link  | Original Model | License | Run on mac |Conversion Script |Year|

| ------------- | ------------- | ------------- |  ------------- | ------------- | ------------- | 

| [Counterfeit-V2.5](https://drive.google.com/file/d/1Kt_8hnGUGnJAUnuergLki37GKnWjWOJp/view?usp=share_link) |[gsdf/Counterfeit-V2.5](https://huggingface.co/gsdf/Counterfeit-V2.5)|-|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion)|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-Stable-Diffusion-models-to-Core-ML#requirements) |2023|

### [anything-v4](https://drive.google.com/file/d/1yF55CGy4I3BKom_E70lLkU6N03nSvjDt/view?usp=share_link)



| Google Drive Link  | Original Model | License | Run on mac |Conversion Script |Year|

| ------------- | ------------- | ------------- |  ------------- | ------------- | ------------- | 

| [anything-v4.5](https://drive.google.com/file/d/1yF55CGy4I3BKom_E70lLkU6N03nSvjDt/view?usp=share_link) |[andite/anything-v4.0](https://huggingface.co/andite/anything-v4.0)|[Fantasy.ai](https://huggingface.co/andite/anything-v4.0)|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion)|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-Stable-Diffusion-models-to-Core-ML#requirements) |2023|

### [Openjourney](https://drive.google.com/file/d/1KIhSG7pHjgldg7r2mm1Yuwa85BceFLsk/view?usp=share_link)



| Google Drive Link  | Original Model | License | Run on mac |Conversion Script |Year|

| ------------- | ------------- | ------------- |  ------------- | ------------- | ------------- | 

| [Openjourney](https://drive.google.com/file/d/1KIhSG7pHjgldg7r2mm1Yuwa85BceFLsk/view?usp=share_link) |[prompthero/openjourney](https://huggingface.co/prompthero/openjourney)|-|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion)|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-Stable-Diffusion-models-to-Core-ML#requirements) |2023|

### [dreamlike-photoreal-2](https://drive.google.com/file/d/1D5RXYE52wyXPq6TdCHM8DIkP4dxHafwt/view?usp=share_link)



| Google Drive Link  | Original Model | License | Run on mac |Conversion Script |Year|

| ------------- | ------------- | ------------- |  ------------- | ------------- | ------------- | 

| [dreamlike-photoreal-2.0](https://drive.google.com/file/d/1D5RXYE52wyXPq6TdCHM8DIkP4dxHafwt/view?usp=share_link) |[dreamlike-art/dreamlike-photoreal-2.0](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0)|[CreativeML OpenRAIL-M](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0)|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion)|[godly-devotion/MochiDiffusion](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-Stable-Diffusion-models-to-Core-ML#requirements) |2023|

# Image Colorization

### DDColor Tiny

DDColor — AI image colorization for grayscale/B&W photos using dual decoders (ICCV 2023).

| Input | Output |

|---|---|

|  |  |

| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [DDColor_Tiny.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/ddcolor-v1/DDColor_Tiny.mlpackage.zip) | 242 MB | 512×512 RGB | AB channels (LAB) | [piddnad/DDColor](https://github.com/piddnad/DDColor) | [Apache-2.0](https://github.com/piddnad/DDColor/blob/master/LICENSE) | 2023 | [DDColorDemo](sample_apps/DDColorDemo) | [convert_ddcolor.py](conversion_scripts/convert_ddcolor.py) |

# Face Recognition

### AdaFace IR-18

AdaFace — Quality-adaptive face recognition. Outputs 512-dim embedding for face verification and identification.



| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [AdaFace_IR18.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/adaface-v1/AdaFace_IR18.mlpackage.zip) | 48 MB | Image (112×112 face) | 512-dim L2-normalized embedding | [mk-minchul/AdaFace](https://github.com/mk-minchul/AdaFace) | [MIT](https://github.com/mk-minchul/AdaFace/blob/master/LICENSE) | 2022 | [AdaFaceDemo](sample_apps/AdaFaceDemo) | [convert_adaface.py](conversion_scripts/convert_adaface.py) |

# 3D Face Pose Estimation

### 3DDFA_V2

3DDFA_V2 — 3D face reconstruction and head pose estimation (yaw, pitch, roll) from a single face image.



| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [3DDFA_V2.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/face3d-v1/3DDFA_V2.mlpackage.zip) | 6.3 MB | Image (120×120 RGB) | 62 params (12 pose + 40 shape + 10 expression) | [cleardusk/3DDFA_V2](https://github.com/cleardusk/3DDFA_V2) | [MIT](https://github.com/cleardusk/3DDFA_V2/blob/master/LICENSE) | 2020 | [Face3DDemo](sample_apps/Face3DDemo) |

# Speaker Diarization

### pyannote segmentation-3.0

pyannote segmentation — Speaker diarization with up to 3 simultaneous speakers. Identifies who speaks when, with overlap detection and per-speaker transcription.

| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [SpeakerSegmentation.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/diarization-v1/SpeakerSegmentation.mlpackage.zip) | 5.8 MB | 10s mono 16kHz [1,1,160000] | [1, 589, 7] speaker logits | [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) | [MIT](https://huggingface.co/pyannote/segmentation-3.0) | 2023 | [DiarizationDemo](sample_apps/DiarizationDemo) | [convert_diarization.py](conversion_scripts/convert_diarization.py) |

# Voice Conversion

### OpenVoice V2

OpenVoice — Zero-shot voice conversion. Record source and target voice, convert on-device.

| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [OpenVoice_SpeakerEncoder.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/openvoice-v1/OpenVoice_SpeakerEncoder.mlpackage.zip) | 1.7 MB | Spectrogram [1, T, 513] | 256-dim speaker embedding | [myshell-ai/OpenVoice](https://github.com/myshell-ai/OpenVoice) | [MIT](https://github.com/myshell-ai/OpenVoice/blob/main/LICENSE) | 2024 | [OpenVoiceDemo](sample_apps/OpenVoiceDemo) | [convert_openvoice.py](conversion_scripts/convert_openvoice.py) |

| [OpenVoice_VoiceConverter.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/openvoice-v1/OpenVoice_VoiceConverter.mlpackage.zip) | 64 MB | Spectrogram + speaker embeddings | Waveform audio (22050 Hz) | | | | | |

# Audio Source Separation

### HTDemucs

Hybrid Transformer Demucs — separates music into 4 stems: drums, bass, vocals, and other instruments.

| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [HTDemucs_SourceSeparation_F32.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/demucs-v1/HTDemucs_SourceSeparation_F32.mlpackage.zip) | 80 MB | Audio Waveform [1, 2, 343980] at 44.1kHz | 4 stems (drums, bass, other, vocals) stereo | [facebookresearch/demucs](https://github.com/facebookresearch/demucs) | [MIT](https://github.com/facebookresearch/demucs/blob/main/LICENSE) | 2022 | [DemucsDemo](sample_apps/DemucsDemo) | [convert_htdemucs.py](conversion_scripts/convert_htdemucs.py) |

# Vision-Language

### Florence-2-base

Microsoft Florence-2 — a unified vision-language model supporting image captioning, OCR, and object detection from a single model. Converted as 3 CoreML models (INT8): Vision Encoder (DaViT), Text Encoder (BART), and Decoder with autoregressive generation.

| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [Florence2VisionEncoder](https://github.com/john-rocky/CoreML-Models/releases/download/florence2-v1/Florence2VisionEncoder.mlpackage.zip) / [TextEncoder](https://github.com/john-rocky/CoreML-Models/releases/download/florence2-v1/Florence2TextEncoder.mlpackage.zip) / [Decoder](https://github.com/john-rocky/CoreML-Models/releases/download/florence2-v1/Florence2Decoder.mlpackage.zip) | 260 MB (INT8, 3 models total) | 768x768 RGB image + task prompt | Generated text (caption, OCR, etc.) | [microsoft/Florence-2-base](https://huggingface.co/microsoft/Florence-2-base) | [MIT](https://huggingface.co/microsoft/Florence-2-base/blob/main/LICENSE) | 2024 | [Florence2Demo](sample_apps/Florence2Demo) | [convert_florence2.py](conversion_scripts/convert_florence2.py) |

# Zero-Shot Image Classification

### SigLIP ViT-B/16

Google SigLIP — sigmoid-based contrastive image-text model for zero-shot classification. Type any labels (e.g. "cat, dog, car") and get per-label probabilities. Converted as 2 CoreML models (INT8): Image Encoder and Text Encoder.

| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [SigLIP_ImageEncoder](https://github.com/john-rocky/CoreML-Models/releases/download/siglip-v2/SigLIP_ImageEncoder.mlpackage.zip) / [TextEncoder](https://github.com/john-rocky/CoreML-Models/releases/download/siglip-v2/SigLIP_TextEncoder.mlpackage.zip) | 386 MB (FP16, 2 models total) | 224x224 RGB image + text labels | Per-label similarity scores (softmax) | [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) | [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) | 2024 | [SigLIPDemo](sample_apps/SigLIPDemo) | [convert_siglip.py](conversion_scripts/convert_siglip.py) |

# Text-to-Music Generation

### Stable Audio Open Small

[stabilityai/stable-audio-open-small](https://huggingface.co/stabilityai/stable-audio-open-small) — text-to-music generation (497M params). Generates up to 11.9 seconds of stereo 44.1kHz audio from text prompts using rectified flow diffusion.

4 CoreML models: T5 text encoder, NumberEmbedder (seconds conditioning), DiT (diffusion transformer), and VAE decoder (Oobleck).

| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |

| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |

| [StableAudioT5Encoder.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/stable-audio-v1/StableAudioT5Encoder.mlpackage.zip) | 105 MB | input_ids [1, 64] | text_embeddings [1, 64, 768] | [stabilityai/stable-audio-open-small](https://huggingface.co/stabilityai/stable-audio-open-small) | [Stability AI Community](https://huggingface.co/stabilityai/stable-audio-open-small/blob/main/LICENSE) | 2024 | [StableAudioDemo](sample_apps/StableAudioDemo) | [convert_stable_audio.py](conversion_scripts/convert_stable_audio.py) |

| [StableAudioNumberEmbedder.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/stable-audio-v1/StableAudioNumberEmbedder.mlpackage.zip) | 396 KB | normalized_seconds [1] | seconds_embedding [1, 768] | | | | | |

| [StableAudioDiT.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/stable-audio-v1/StableAudioDiT.mlpackage.zip) | 326 MB | latent [1,64,256] + timestep + conditioning | velocity [1,64,256] | | | | | |

| [StableAudioDiT_FP32.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/stable-audio-v1/StableAudioDiT_FP32.mlpackage.zip) | 1.3 GB | latent [1,64,256] + timestep + conditioning | velocity [1,64,256] | | | | | |

| [StableAudioVAEDecoder.mlpackage.zip](https://github.com/john-rocky/CoreML-Models/releases/download/stable-audio-v1/StableAudioVAEDecoder.mlpackage.zip) | 149 MB | latent [1, 64, 256] | stereo audio [1, 2, 524288] at 44.1kHz | | | | | |

**Conversion notes:**

- DiT INT8 (`StableAudioDiT`): use with `cpuAndGPU`. Fastest, slight quality loss from quantization.

- DiT FP32 (`StableAudioDiT_FP32`): use with `cpuOnly`. Best quality, slower (~1.3GB). FP16 weights overflow in attention on iOS GPU, so FP32 compute + CPU is required.

- T5 INT8: may produce occasional NaN values — the sample app sanitizes these before passing to DiT.

- VAE Decoder FP16: weight_norm must be removed before tracing (Snake activation).

## Models converted by someone other than me.

### [Stable Diffusion](https://github.com/apple/ml-stable-diffusion)

[apple/ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion)

## How to use in a xcode project.

### Option 1,implement Vision request.

```swift:

import Vision

lazy var coreMLRequest:VNCoreMLRequest = {

   let model = try! VNCoreMLModel(for: modelname().model)

   let request = VNCoreMLRequest(model: model, completionHandler: self.coreMLCompletionHandler)

   return request

   }()

let handler = VNImageRequestHandler(ciImage: ciimage,options: [:])

   DispatchQueue.global(qos: .userInitiated).async {

   try? handler.perform([coreMLRequest])

}

```

If the model has Image type output:

```swift

let result = request?.results?.first as! VNPixelBufferObservation

let uiimage = UIImage(ciImage: CIImage(cvPixelBuffer: result.pixelBuffer))

```

Else the model has Multiarray type output:

For visualizing multiArray as image, Mr. Hollance’s “CoreML Helpers” are very convenient.

[CoreML Helpers](https://github.com/hollance/CoreMLHelpers)

[Converting from MultiArray to Image with CoreML Helpers.](https://medium.com/@rockyshikoku/converting-from-multiarray-to-image-with-coreml-helpers-59fdc34d80d8)

```swift:

func coreMLCompletionHandler（request：VNRequest？、error：Error？）{

   let = coreMLRequest.results？.first as！VNCoreMLFeatureValueObservation

   let multiArray = result.featureValue.multiArrayValue

   let cgimage = multiArray？.cgImage（min：-1、max：1、channel：nil）

```

### Option 2,Use [CoreGANContainer](https://github.com/john-rocky/CoreGANContainer). You can use models with dragging&dropping into the container project. 

# Make the model lighter

You can make the model size lighter with Quantization if you want.

https://coremltools.readme.io/docs/quantization

>The lower the number of bits, more the chances of degrading the model accuracy. The loss in accuracy varies with the model.

```python

import coremltools as ct

from coremltools.models.neural_network import quantization_utils

# load full precision model

model_fp32 = ct.models.MLModel('model.mlmodel')

model_fp16 = quantization_utils.quantize_weights(model_fp32, nbits=16)

# nbits can be 16(half size model), 8(1/4), 4(1/8), 2, 1

```

##### quantized sample (U2Net)

##### InputImage / nbits=32(original) / nbits=16 / nbits=8 / nbits=4

    

# Thanks

Cover image was taken from Ghibli free images. 

On YOLOv5 convertion, [dbsystel/yolov5-coreml-tools](https://github.com/dbsystel/yolov5-coreml-tools) give me the super inteligent convert script. 

And all of original projects

# Auther

Daisuke Majima

Freelance engineer. iOS/MachineLearning/AR

I can work on mobile ML projects and AR project.

Feel free to contact: rockyshikoku@gmail.com

[GitHub](https://github.com/john-rocky)

[Twitter](https://twitter.com/JackdeS11)

[Medium](https://rockyshikoku.medium.com/)
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