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CoreML-Models\nConverted Core ML Model Zoo.\n\n\u003cimg width=\"1280\" src=\"https://user-images.githubusercontent.com/23278992/147420041-fdeb1fbb-7e93-41c6-84d6-80d7c1c45200.jpeg\"\u003e\n\nCore ML is a machine learning framework by Apple.\nIf you are iOS developer, you can easly use machine learning models in your Xcode project. \n\n# How to use\n\nTake a look this model zoo, and if you found the CoreML model you want,\ndownload the model from google drive link and bundle it in your project.\nOr if the model have sample project link, try it and see how to use the model in the project.\nYou are free to do or not.\n\n**If you like this repository, please give me a star so I can do my best.**\n\n# Section Link\n\n- [**Image Classifier**](#image-classifier)\n  - [Efficientnetb0](#efficientnetb0)\n  - [Efficientnetv2](#efficientnetv2)\n  - [VisionTransformer](#visiontransformer)\n  - [Conformer](#conformer)\n  - [DeiT](#deit)\n  - [RepVGG](#repvgg)\n  - [RegNet](#regnet)\n  - [MobileViTv2](#mobilevitv2)\n\n  \n- [**Object Detection**](#object-detection)\n  - [D-FINE](#d-fine)\n  - [RF-DETR](#rf-detr)\n  - [YOLOv5s](#yolov5s)\n  - [YOLOv7](#yolov7)\n  - [YOLOv8](#yolov8)\n  - [YOLOv9](#yolov9)\n  - [YOLOv10](#yolov10)\n  - [YOLO11](#yolo11)\n  - [YOLO26](#yolo26)\n  - [YOLO-World](#yolo-world)\n\n- [**Segmentation**](#segmentation)\n  - [U2Net](#u2net)\n  - [IS-Net](#is-net)\n  - [RMBG1.4](#rmbg14)\n  - [face-parsing](#face-parsing)\n  - [Segformer](#segformer)\n  - [BiseNetv2](#bisenetv2)\n  - [DNL](#dnl)\n  - [ISANet](#isanet)\n  - [FastFCN](#fastfcn)\n  - [GCNet](#gcnet)\n  - [DANet](#danet)\n  - [Semantic FPN](#semantic-fpn)\n  - [cloths_segmentation](#cloths_segmentation)\n  - [easyportrait](#easyportrait)\n  - [MobileSAM](#mobilesam)\n  - [SAM2-Tiny](#sam2-tiny)\n\n- [**Super Resolution**](#super-resolution)\n  - [Real ESRGAN](#real-esrgan)\n  - [GFPGAN](#gfpgan)\n  - [BSRGAN](#bsrgan)\n  - [A-ESRGAN](#a-esrgan)\n  - [Beby-GAN](#beby-gan)\n  - [RRDN](#rrdn)\n  - [Fast-SRGAN](#fast-srgan)\n  - [ESRGAN](#esrgan)\n  - [UltraSharp](#ultrasharp)\n  - [SRGAN](#srgan)\n  - [SRResNet](#srresnet)\n  - [LESRCNN](#lesrcnn)\n  - [MMRealSR](#mmrealsr)\n  - [DASR](#dasr)\n      \n- [**Low Light Enhancement**](#low-light-enhancement)\n  - [StableLLVE](#stablellve)\n  - [Zero-DCE](#zero-dce)\n  - [Retinexformer](#retinexformer)\n\n- [**Image Restoration**](#image-restroration)\n  - [MPRNet](#mprnet)\n  - [MIRNetv2](#mirnetv2)\n  \n- [**Image Generation**](#image-generation)\n  - [MobileStyleGAN](#mobilestylegan)\n  - [DCGAN](#dcgan)\n\n- [**Image2Image**](#image2image)\n  - [Anime2Sketch](#anime2sketch)\n  - [AnimeGAN2Face_Paint_512_v2](#animegan2face_paint_512_v2)\n  - [Photo2Cartoon](#photo2cartoon)\n  - [AnimeGANv2_Hayao](#animeGANv2_hayao)\n  - [AnimeGANv2_Paprika](#animeGANv2_paprika)\n  - [WarpGAN Caricature](#warpgancaricature)\n  - [UGATIT_selfie2anime](#ugatit_selfie2anime)\n  - [Fast-Neural-Style-Transfer](#fast-neural-style-transfer)\n  - [White_box_Cartoonization](#white_box_cartoonization)\n  - [FacialCartoonization](#facialcartoonization)\n\n- [**Inpainting**](#inpainting)\n  - [AOT-GAN-for-Inpainting](#aot-gan-for-inpainting)\n  - [Lama](#lama)\n\n- [**Monocular Depth Estimation**](#monocular-depth-estimation)\n  - [MiDaS](#midas)\n  \n- [**Stable Diffusion**](#stable-diffusion) **:text2image**\n  - [stable-diffusion-v1-5](#stable-diffusion-v1-5)\n  - [pastel-mix](#pastel-mix)\n  - [Orange Mix](#orange-mix)\n  - [Counterfeit-V2.5](#counterfeit)\n  - [anything-v4.5](#anything-v4)\n  - [Openjourney](#openjourney)\n  - [dreamlike-photoreal-2.0](#dreamlike-photoreal-2)\n\n- [**Image Colorization**](#image-colorization)\n  - [DDColor Tiny](#ddcolor-tiny)\n\n- [**Face Recognition**](#face-recognition)\n  - [AdaFace IR-18](#adaface-ir-18)\n\n- [**3D Face Pose Estimation**](#3d-face-pose-estimation)\n  - [3DDFA_V2](#3ddfa_v2)\n\n- [**Speaker Diarization**](#speaker-diarization)\n  - [pyannote segmentation-3.0](#pyannote-segmentation-30)\n\n- [**Voice Conversion**](#voice-conversion)\n  - [OpenVoice V2](#openvoice-v2)\n\n- [**Text-to-Music Generation**](#text-to-music-generation)\n  - [Stable Audio Open Small](#stable-audio-open-small)\n\n- [**Audio Source Separation**](#audio-source-separation)\n  - [HTDemucs](#htdemucs)\n\n- [**Vision-Language**](#vision-language)\n  - [Florence-2-base](#florence-2-base)\n\n- [**Zero-Shot Image Classification**](#zero-shot-image-classification)\n  - [SigLIP ViT-B/16](#siglip-vit-b16)\n\n# How to get the model\nYou can get the model converted to CoreML format from the link of Google drive.\nSee the section below for how to use it in Xcode.\nThe license for each model conforms to the license for the original project.\n\n# Image Classifier\n\n### Efficientnet\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2021-12-27 6 34 43\" src=\"https://user-images.githubusercontent.com/23278992/147420587-108b87f8-7996-4288-905a-ad53f9142221.png\"\u003e\n\n| Google Drive Link | Size | Dataset |Original Project | License |\n| ------------- | ------------- | ------------- |------------- |------------- |\n| [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)|\n\n\n### Efficientnetv2\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2021-12-31 4 30 22\" src=\"https://user-images.githubusercontent.com/23278992/147782567-bbf26186-8c84-4073-8df4-b08e06d4e791.png\"\u003e\n\n| Google Drive Link | Size | Dataset |Original Project | License | Year|\n| ------------- | ------------- | ------------- |------------- |------------- |------------- |\n| [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|\n\n### VisionTransformer\n\nAn Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2022-01-07 10 37 05\" src=\"https://user-images.githubusercontent.com/23278992/148482246-64269fb4-fda4-4bd5-b219-5bf860fd77e7.png\"\u003e\n\n| Google Drive Link | Size | Dataset |Original Project | License |Year|\n| ------------- | ------------- | ------------- |------------- |------------- |------------- |\n| [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|\n\n### Conformer\n\nLocal Features Coupling Global Representations for Visual Recognition.\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2022-01-07 11 34 33\" src=\"https://user-images.githubusercontent.com/23278992/148482144-2d5bb7e8-ed67-4146-9f9d-c95fe94735d3.png\"\u003e\n\n| Google Drive Link | Size | Dataset |Original Project | License |Year|\n| ------------- | ------------- | ------------- |------------- |------------- |------------- |\n| [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|\n\n### DeiT\n\nData-efficient Image Transformers\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2022-01-07 11 50 25\" src=\"https://user-images.githubusercontent.com/23278992/148484220-38494287-49b4-4992-9ceb-9dc7b75a250e.png\"\u003e\n\n| Google Drive Link | Size | Dataset |Original Project | License |Year|\n| ------------- | ------------- | ------------- |------------- |------------- |------------- |\n| [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|\n\n### RepVGG\n\nMaking VGG-style ConvNets Great Again\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2022-01-08 5 00 53\" src=\"https://user-images.githubusercontent.com/23278992/148600326-69dd9666-2709-4318-914b-30db8c294fd3.png\"\u003e\n\n| Google Drive Link | Size | Dataset |Original Project | License |Year|\n| ------------- | ------------- | ------------- |------------- |------------- |------------- |\n| [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|\n\n### RegNet\n\nDesigning Network Design Spaces\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2022-02-23 7 38 23\" src=\"https://user-images.githubusercontent.com/23278992/155233183-edf61ebe-922c-4b63-8a5e-7ef6c9f7eaa8.png\"\u003e\n\n| Google Drive Link | Size | Dataset |Original Project | License |Year|\n| ------------- | ------------- | ------------- |------------- |------------- |------------- |\n| [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|\n\n\n### MobileViTv2\n\nCVNets: A library for training computer vision networks\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2022-02-23 7 38 23\" src=\"https://user-images.githubusercontent.com/23278992/225600794-a0a4dc00-cc67-4614-82ed-3ed8633cf03e.png\"\u003e\n\n| Google Drive Link | Size | Dataset |Original Project | License |Year|Conversion Script|\n| ------------- | ------------- | ------------- |------------- |------------- |------------- |------------- |\n| [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)) |\n\n# Object Detection\n\n### D-FINE\n\n\u003cimg width=\"400\" alt=\"D-FINE iOS Demo\" src=\"https://github.com/user-attachments/assets/a9af3b06-dc8b-4384-88f3-765b85414b0f\"\u003e\n\n| Download Link | Size | Output | Original Project | License | Note | Sample Project |\n| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |\n|[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) |\n\n### RF-DETR\n\n\u003cimg width=\"400\" alt=\"RF-DETR iOS Demo\" src=\"https://github.com/user-attachments/assets/bde0438e-5c56-4528-a083-2952106e8073\"\u003e\n\n| Download Link | Size | Output | Original Project | License | Note | Sample Project |\n| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |\n|[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) |\n\n### YOLOv5s\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2021-12-29 6 17 08\" src=\"https://user-images.githubusercontent.com/23278992/147608051-be2ff345-22e8-4f82-83ed-7cc41ce4084d.png\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | License | Note | Sample Project |\n| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |\n|[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) |\n\n### YOLOv7\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2021-12-29 6 17 08\" src=\"https://user-images.githubusercontent.com/23278992/178128011-e0056777-0c2a-495b-b132-7741cc693077.png\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | License | Note | Sample Project | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |------------- |\n|[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) |\n\n### YOLOv8\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2021-12-29 6 17 08\" src=\"https://user-images.githubusercontent.com/23278992/211807010-d48854b3-beb0-46a8-bd99-cbb9351529b0.png\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | License | Note | Sample Project | \n| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |\n|[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) |\n\n### YOLOv9\n\nYOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. Uses PGI and GELAN architecture for efficient object detection.\n\n| Download Link | Size | Output | Original Project | License | Year | Note | Sample Project |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n### YOLOv10\n\nYOLOv10: Real-Time End-to-End Object Detection. NMS-free architecture using consistent dual assignments — no post-processing needed.\n\n| Download Link | Size | Output | Original Project | License | Year | Note | Sample Project |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n### YOLO11\n\nYOLO11: Ultralytics latest YOLO with improved backbone and neck architecture. 22% fewer parameters than YOLOv8 with higher mAP.\n\n| Download Link | Size | Output | Original Project | License | Year | Note | Sample Project |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n### YOLO26\n\nYOLO26: Edge-first vision AI with NMS-free end-to-end detection. Up to 43% faster CPU inference vs YOLO11 with DFL removal and ProgLoss.\n\n\u003cimg width=\"300\" src=\"https://github.com/user-attachments/assets/bade5e8b-25fd-4ef8-96d6-7f8dfbb954b2\"\u003e\n\n| Download Link | Size | Output | Original Project | License | Year | Note | Sample Project |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n### YOLO-World\n\nYOLO-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.\n\n\u003cimg width=\"300\" src=\"https://github.com/user-attachments/assets/999e063a-9ace-49b0-808e-e330516e1896\"\u003e\n\n| Download Link | Size | Description | Original Project | License | Year | Sample Project |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n| [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 | — |\n| [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 | — | — | — | — |\n\n# Segmentation\n\n### [U2Net](https://drive.google.com/file/d/1cpm-x12Ih7Cqd_kOjfTvtt4ipGS3BpCx/view?usp=sharing)\n\u003cimg width=\"400\" src=\"https://camo.qiitausercontent.com/a8e89c72c0950db66d63415b9010d203aae22617/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e61702d6e6f727468656173742d312e616d617a6f6e6177732e636f6d2f302f3233353235392f36303037393162322d633534332d613537652d303639622d3863663130373932643662392e6a706567\"\u003e \u003cimg width=\"400\" src=\"https://camo.qiitausercontent.com/4f502487cd9e9e02d150ad63b33683a1446e7516/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e61702d6e6f727468656173742d312e616d617a6f6e6177732e636f6d2f302f3233353235392f39636532633237612d643134322d663136352d343365662d6532373966646337386333382e706e67\"\u003e\n\n| Google Drive Link | Size | Output |Original Project | License |\n| ------------- | ------------- | ------------- | ------------- |------------- |\n| [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)|\n| [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)|\n\n### [IS-Net](https://drive.google.com/drive/folders/13CkOTBCYc3FjGTU26lmCsRYsOkeHnAMA?usp=sharing)\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/179818731-b919c8a2-f5c9-4a80-8666-e3034d1e86f0.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/179818740-38336aec-c9c5-4471-b529-ae45286062b5.JPG\"\u003e\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/186722092-3b8ed1a1-4a03-4357-9bfd-9ec213e7d87d.jpeg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/186791654-42b4ba54-f06f-43d3-805b-5bb89e5df272.JPG\"\u003e\n\n| Google Drive Link | Size | Output |Original Project | License | Year | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- |------------- | ------------- |------------- |\n| [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)|\n| [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)|\n\n### RMBG1.4\n\nRMBG1.4 - The IS-Net enhanced with our unique training scheme and proprietary dataset. \n\n\u003cimg src=\"https://github.com/john-rocky/PersonSegmentationSampler/assets/23278992/2a91ec10-fe94-43be-aedc-283e71fa9a1e\" width=400\u003e \u003cimg src=\"https://github.com/john-rocky/PersonSegmentationSampler/assets/23278992/04af501d-996d-48f4-b008-f0076dcbc117\" width=400\u003e\n\n| Download Link | Size | Output |Original Project | License | year  | Sample Project | Conversion Script |\n| ------------- | ------------- | ------------- |------------- | ------------- | ------------- |------------- |------------- |\n| [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) |\n\n### face-Parsing\n\n\u003cimg src=\"https://user-images.githubusercontent.com/23278992/147860040-14a7e022-5490-4e51-98cd-cd421066dd8c.png\" width=400\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/147860042-d27f37b0-227b-45ab-8d76-f6c6f2f5b3a4.png\" width=400\u003e\n\n| Google Drive Link | Size | Output |Original Project | License | Sample Project |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n### Segformer\n\nSimple and Efficient Design for Semantic Segmentation with Transformers\n\n\u003cimg src=\"https://user-images.githubusercontent.com/23278992/148621010-5ecf6b90-c501-4cf8-91e1-446850030265.png\" width=400\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/148621013-44d9cd29-ef3c-4250-bbd9-4e4093385a54.JPG\" width=400\u003e\n\n| Google Drive Link | Size | Output |Original Project | License | year |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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|\n\n### BiSeNetV2\t\n\nBilateral Network with Guided Aggregation for Real-time Semantic Segmentation\n\n\u003cimg src=\"https://user-images.githubusercontent.com/23278992/148663182-c1f3b9dd-8db4-49be-bf92-97a898a8b477.jpg\" width=400\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/148663183-327dc684-342d-43f1-a8d8-ebf817c91bdd.JPG\" width=400\u003e\n\n| Google Drive Link | Size | Output |Original Project | License | year |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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|\n\n### DNL\n\nDisentangled Non-Local Neural Networks\n\n\u003cimg src=\"https://user-images.githubusercontent.com/23278992/150061280-23a1de7c-2e12-41d2-9056-7c4b375193a6.jpg\" width=400\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/150061290-eed50b79-f5c0-4fa4-b5bf-728b9029f34c.png\" width=400\u003e\n\n| Google Drive Link | Size | Output |Dataset|Original Project | License | year |\n| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |\n| [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|\n\n### ISANet\n\nInterlaced Sparse Self-Attention for Semantic Segmentation\n\n\u003cimg src=\"https://user-images.githubusercontent.com/23278992/150234575-7dcb8521-4ebd-46aa-bd19-4c1036b514dc.jpg\" width=400\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/150234561-41478d2a-b411-48df-9980-8553c381e530.png\" width=400\u003e\n\n| Google Drive Link | Size | Output |Dataset|Original Project | License | year |\n| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |\n| [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|\n\n### FastFCN\n\nRethinking Dilated Convolution in the Backbone for Semantic Segmentation\n\n\u003cimg src=\"https://user-images.githubusercontent.com/23278992/150237380-3b8522e6-e310-436e-b5c3-60b7ff8cb606.jpg\" width=400\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/150237372-1d17f4e2-cf1b-49f0-82b8-d9e6644ff465.png\" width=400\u003e\n\n| Google Drive Link | Size | Output |Dataset|Original Project | License | year |\n| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |\n| [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|\n\n### GCNet\n\nNon-local Networks Meet Squeeze-Excitation Networks and Beyond\n\n\u003cimg src=\"https://user-images.githubusercontent.com/23278992/150239404-9d6438ec-cee5-44b9-9179-436ac5ceaab2.jpg\" width=400\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/150239421-cceaae77-eb6b-468d-a069-72750fc6b0f4.png\" width=400\u003e\n\n| Google Drive Link | Size | Output |Dataset|Original Project | License | year |\n| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |\n| [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|\n\n### DANet\n\nDual Attention Network for Scene Segmentation(CVPR2019)\n\n\u003cimg src=\"https://user-images.githubusercontent.com/23278992/150419837-980a0e0f-6333-4853-b638-6e6854e093e3.jpg\" width=400\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/150419740-052fca9b-0519-440c-bffd-5abc7a5ac240.png\" width=400\u003e\n\n| Google Drive Link | Size | Output |Dataset|Original Project | License | year |\n| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |\n| [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|\n\n### Semantic-FPN\n\nPanoptic Feature Pyramid Networks\n\n\u003cimg src=\"https://user-images.githubusercontent.com/23278992/150614015-6b712113-6b8f-484e-88dc-124b76229153.jpg\" width=400\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/150614022-590eb6fa-075f-4ff7-8ad5-b9d502b8763b.png\" width=400\u003e\n\n| Google Drive Link | Size | Output |Dataset|Original Project | License | year |\n| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |\n| [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|\n\n### cloths_segmentation\n\nCode for binary segmentation of various cloths.\n\n\u003cimg src=\"https://user-images.githubusercontent.com/23278992/154873792-54c12be0-d446-4789-bf00-bb89cab5a566.jpg\" width=400\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/154873786-2b90e0d9-dd86-4397-8977-ea1464ca2f75.JPG\" width=400\u003e\n\n| Google Drive Link | Size | Output |Dataset|Original Project | License | year |\n| ------------- | ------------- | ------------- |------------- | ------------- | ------------- | ------------- |\n| [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|\n\n### easyportrait\n\nEasyPortrait - Face Parsing and Portrait Segmentation Dataset.\n\n\u003cimg src=\"https://github.com/john-rocky/CoreML-Models/assets/23278992/6ab8ed6a-2de7-43fd-bb84-2fb77286bd6c\" width=400\u003e \u003cimg src=\"https://github.com/john-rocky/CoreML-Models/assets/23278992/a0b8e435-d04e-4a88-940b-bd5fb45cbc15\" width=400\u003e\n\n| Google Drive Link | Size | Output |Original Project | License | year | Swift sample |Conversion Script |\n| ------------- | ------------- | ------------- |------------- | ------------- | ------------- |------------- |------------- |\n| [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)|\n\n### MobileSAM\n\nFaster 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.\n\u003cimg src=\"https://github.com/user-attachments/assets/2a4364ee-a3fc-4e40-a0bb-b3c7c9bfa0f5\" width=200\u003e\n| Download Link | Size | Output | Original Project | License | Year | Sample Project |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n### SAM2-Tiny\n\nSAM 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.\n\n| Download Link | Size | Output | Original Project | License | Year | Sample Project |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n# Super Resolution\n\n### [Real ESRGAN](https://drive.google.com/file/d/1cpm-x12Ih7Cqd_kOjfTvtt4ipGS3BpCx/view?usp=sharing)\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/147418147-47f2089f-80ea-4688-ac06-7d9c4b46a08e.png\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/147418143-b8f89073-afa1-4c5c-95e9-2ee8a00a94b9.JPG\"\u003e \n\n| Google Drive Link | Size | Output |Original Project | License | year |\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |\n| [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|\n| [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|\n\n### [GFPGAN](https://drive.google.com/file/d/1-3fF4aPnh8ygUOmKItIrZ318xI9JGmQx/view?usp=sharing)\n\nTowards Real-World Blind Face Restoration with Generative Facial Prior\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/186315786-56634605-e357-4e9e-a0d9-51bb526bf69f.png\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/186316328-1fc64a6f-a443-4df2-bb86-0af343cd8a64.png\"\u003e \n\n| Google Drive Link | Size | Output |Original Project | License |year |\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |\n| [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|\n\n### [BSRGAN](https://drive.google.com/file/d/1-3K89vJZ5OUAh4xdSAifgnL52jbl2fVf/view?usp=sharing)\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/148810656-4c5faa33-1be9-45f6-b31a-defd931cb1f8.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/148811822-56844bc7-b197-44d5-8454-757890c890b5.jpg\"\u003e \n\n| Google Drive Link | Size | Output |Original Project | License |year |\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |\n| [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|\n\n### [A-ESRGAN](https://drive.google.com/file/d/1-0rKVQtFXNWfIBIpvyemjuO3O00GZBeb/view?usp=sharing)\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/151077592-a993a19c-8a05-471a-8924-c7302f7af84b.png\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/151077667-62bdbe2b-8e00-4816-945a-14890ccf1bcd.png\"\u003e \n\n| Google Drive Link | Size | Output |Original Project | License |year |Conversion Script|\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |------------- |\n| [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)|\n\n### [Beby-GAN](https://drive.google.com/file/d/1bJ7_NgR2KXI46JiFk5hH_6IdCHMyhN05/view?usp=sharing)\n\nBest-Buddy GANs for Highly Detailed Image Super-Resolution\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/151282027-14a5d386-60a8-4152-bff1-a0416db81d7a.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/151282014-1177b73d-a2b3-40eb-9a87-9cbe8ace504b.jpg\"\u003e \n\n| Google Drive Link | Size | Output |Original Project | License |year |\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |\n| [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|\n\n### [RRDN](https://drive.google.com/file/d/1-M30vR0xMuYDn2p5O4KZrUnUXy4SNThF/view?usp=sharing)\n\nThe Residual in Residual Dense Network for image super-scaling.\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/152622988-795c1279-43f7-4d8a-a2ea-a786bcd6a34b.png\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/152622984-fbc911c5-901c-4ce3-93b6-753f35dea531.png\"\u003e\n\n| Google Drive Link | Size | Output |Original Project | License |year |\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |\n| [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|\n\n\n### [Fast-SRGAN](https://drive.google.com/file/d/1gYXbhcSUm5rhcCAmwLruonAhu8jvyDL8/view?usp=sharing)\n\nFast-SRGAN.\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/156285673-a6239cec-41ec-46d8-a2fa-d0ad21498f1d.png\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/156285686-8d0333a2-b07f-4aa2-8a44-fe959758289f.png\"\u003e\n\n| Google Drive Link | Size | Output |Original Project | License |year |\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |\n| [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|\n\n### [ESRGAN](https://drive.google.com/file/d/1fkRbh_gckuFlgr357OIdOrEJK4T_2Xkz/view?usp=sharing)\n\nEnhanced-SRGAN.\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/156899173-bdc1ceed-c3f6-4abd-b217-18667fc88cf6.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/156899267-65343f4e-a963-4680-83ba-7ecd7e6680a5.jpg\"\u003e\n\n| Google Drive Link | Size | Output |Original Project | License |year |\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |\n| [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|\n\n### [UltraSharp](https://drive.google.com/drive/folders/1-Q1SdS8iHWTfTs7FE39pUTEubPks30Ca?usp=drive_link)\n\nPretrained: 4xESRGAN\n\n\u003cimg width=\"400\" src=\"https://github.com/john-rocky/PersonSegmentationSampler/assets/23278992/b98ab056-23b0-486e-a52c-a88e857c1048\"\u003e \u003cimg width=\"400\" src=\"https://github.com/john-rocky/PersonSegmentationSampler/assets/23278992/d4214ded-c9d2-4f18-8de3-222f912862b0\"\u003e\n\n| Google Drive Link | Size | Output |Original Project | License |year |\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |\n| [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|\n\n### [SRGAN](https://drive.google.com/file/d/1-076W2o0wCtoODptikX1eOnlFBx2s3qK/view?usp=sharing)\n\nPhoto-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/156899475-172b7ac5-a6ca-4b0b-a6d8-f0d0ddea986e.png\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/156899476-641af271-9b2e-4122-a048-099700d8335a.png\"\u003e\n\n| Google Drive Link | Size | Output |Original Project | License |year |\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |\n| [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|\n\n### [SRResNet](https://drive.google.com/file/d/1-2kYZgF_Z6vntrRsHmRiwyHJg5TC1PSW/view?usp=sharing)\n\nPhoto-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/156899905-40746d09-4580-4e30-b0b4-b146fd1975c2.png\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/156899906-ab5c8c4e-54af-4d55-874b-5d1e0aac961f.JPG\"\u003e\n\n| Google Drive Link | Size | Output |Original Project | License |year |\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |\n| [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|\n\n### [LESRCNN](https://drive.google.com/file/d/1-0zgxURZwqX0mAAVy69K-owE7QP-7NfJ/view?usp=sharing)\n\nLightweight Image Super-Resolution with Enhanced CNN.\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/180625941-3a6b44a6-35e1-4ff9-a85b-c5efc81fc101.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/180625939-308f7176-488a-40a1-ab6e-428dc01bbf67.jpg\"\u003e\n\n| Google Drive Link | Size | Output |Original Project | License |year | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |------------- |\n| [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)|\n\n### [MMRealSR](https://drive.google.com/file/d/1-HwMLvOy_hHycHNhojob6uT8t6tRyWqb/view?usp=sharing)\n\nMetric Learning based Interactive Modulation for Real-World Super-Resolution\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/186336018-9c5d5700-28a7-438e-bc07-5ca2a8e843cd.png\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/186336038-5e877d1a-33b1-4f54-9e4d-192f9bb765fe.png\"\u003e\n\n| Google Drive Link | Size | Output |Original Project | License |year | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |------------- |\n| [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)|\n| [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)|\n\n### [DASR](https://drive.google.com/drive/folders/10J2ehHewK2ppS5ToDqmtJ2Ei5k8vcdL0?usp=sharing)\n\nPytorch implementation of \"Unsupervised Degradation Representation Learning for Blind Super-Resolution\", CVPR 2021\n\n\u003cimg width=\"400\" src=\"https://github.com/john-rocky/PersonSegmentationSampler/assets/23278992/7e806f4d-0323-431a-89e8-816163e5c3f5\"\u003e \u003cimg width=\"400\" src=\"https://github.com/john-rocky/PersonSegmentationSampler/assets/23278992/8589f89b-367d-4777-8ebd-6e78253c4b33\"\u003e\n\n| Google Drive Link | Size | Output |Original Project | License |year|\n| ------------- | ------------- | ------------- | ------------- | ------------- |------------- |\n| [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|\n\n\n# Low Light Enhancement\n\n### StableLLVE\n\nLearning Temporal Consistency for Low Light Video Enhancement from Single Images.\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/148664179-4d0cd417-d8f9-4d0e-bc05-cff3a4a30b5a.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/148664220-c756198f-e8c5-4ea8-8737-59c004d2f08c.jpg\"\u003e \n\n| Google Drive Link | Size | Output |Original Project | License |Year|\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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|\n\n### Zero-DCE\n\nZero-Reference Deep Curve Estimation for Low-Light Image Enhancement\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/151897265-7c3c0295-69c3-4c90-9dcc-d04bbcfd41a3.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/151897430-f16d84f0-170c-4e54-a08d-ad4d5b6ca47a.jpg\"\u003e \n\n| Google Drive Link | Size | Output |Original Project | License |Year|Conversion Script|\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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)|\n\n### Retinexformer\n\nRetinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement\n\n\u003cimg width=\"256\" src=\"https://github.com/john-rocky/PersonSegmentationSampler/assets/23278992/296650ba-e2a9-49ba-b2d6-be02e8b56f09\"\u003e \u003cimg width=\"256\" src=\"https://github.com/john-rocky/PersonSegmentationSampler/assets/23278992/eac9f78a-2b00-442a-b73f-01760268184e\"\u003e \n\n| Google Drive Link | Size | Output |Original Project | License |Year|Conversion Script|\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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)|\n| [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)|\n\n# Image Restoration\n\n### MPRNet\n\nMulti-Stage Progressive Image Restoration.\n\nDebluring\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/149243725-79c68d8e-db6c-4114-ac64-738cd6b5c37c.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/149243509-7eff6ae8-65c2-45ba-bfa2-d730657ab2bd.png\"\u003e \n\nDenoising\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/149241165-534c54db-7e98-4356-8613-44acb93d4c6a.png\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/149242199-7cc3e456-7c8d-441c-b0aa-f1b6ca19a5c9.png\"\u003e \n\nDeraining\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/149241095-91791593-416e-41b0-8a95-71819cb7fb06.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/149241720-afe94607-e9c2-45bb-988d-3c322d7dde1a.jpg\"\u003e \n\n| Google Drive Link | Size | Output |Original Project | License |Year|\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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|\n| [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|\n| [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|\n\n\n### MIRNetv2\n\nLearning Enriched Features for Fast Image Restoration and Enhancement.\n\nDenoising\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/176293658-6715e545-fe9b-4b21-b374-1394740efdde.png\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/176293741-dc77831a-86d0-4bdc-a667-96d318d064c4.png\"\u003e \n\nSuper Resolution\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/176276244-93535414-bc0e-423d-9c0a-18ba432391a4.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/176276266-75228905-2266-4c2c-b42a-026803a0da3b.jpg\"\u003e \n\nContrast Enhancement\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/176286891-563c92cd-1817-406a-babb-7dd9b0cccc01.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/176296935-bce82abf-6420-43ae-924e-5b98ee956431.jpg\"\u003e \n\nLow Light Enhancement\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/176283269-145a5ce4-709a-4eea-91a7-b924b598a03d.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/176283354-c45a6247-b1c2-43f8-8b43-8fcf0ddac64f.jpg\"\u003e \n\n| Google Drive Link | Size | Output |Original Project | License |Year|Conversion Script|\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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)|\n| [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)|\n| [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)|\n| [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)|\n\n# Image Generation\n\n### [MobileStyleGAN](https://drive.google.com/drive/folders/1rUV6AXwp8JhPPmkog-0r0AUGzUvN9DmW?usp=sharing)\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/147397892-773c55ca-55fc-422b-a95b-a729eda04077.JPG\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/147397894-e2d3a1ef-7afa-410a-9580-f09ef7157c50.JPG\"\u003e \n\n| Google Drive Link | Size | Output | Original Project | License | Sample Project |\n| ------------- | ------------- | ------------- | ------------- |  ------------- |  ------------- | \n| [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) |\n\n\n### [DCGAN](https://drive.google.com/file/d/132GrmmuETSLTml1zWyLUnIksclP-8vGw/view?usp=sharing)\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/144690829-3a4cebcf-ee73-4df0-b8db-1dfc2e616798.png\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | \n| ------------- | ------------- | ------------- | ------------- | \n| [DCGAN](https://drive.google.com/file/d/132GrmmuETSLTml1zWyLUnIksclP-8vGw/view?usp=sharing)　| 9.2MB | MultiArray | [TensorFlowCore](https://www.tensorflow.org/tutorials/generative/dcgan)|\n\n\n# Image2Image\n\n### [Anime2Sketch](https://drive.google.com/file/d/1-52NnZ1kajZI5Rk0tn3DegpU38la_jYk/view?usp=sharing)\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/147990751-9ac35e43-b9a6-4db2-af5c-37978322240d.jpeg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/147990892-d676142c-62c4-433d-9835-337b1293bfc4.jpeg\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | License | Usage |\n| ------------- | ------------- | ------------- | ------------- | ------------- |  ------------- | \n| [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|\n\n\n### [AnimeGAN2Face_Paint_512_v2](https://drive.google.com/file/d/1phSgcAz3LNbk2v2RoSESmr7PFxTAHcxb/view?usp=sharing)\n\u003cimg width=\"400\" src=\"https://camo.qiitausercontent.com/74a02b6e0b80e52c2ae3af798c93eea9aa3e394d/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e61702d6e6f727468656173742d312e616d617a6f6e6177732e636f6d2f302f3233353235392f30313764616563342d333933312d643664662d303339322d6162313039303237313963642e706e67\"\u003e \u003cimg width=\"400\" src=\"https://camo.qiitausercontent.com/311349da47136ff9ce61701d09ce59dc663c95bf/68747470733a2f2f71696974612d696d6167652d73746f72652e73332e61702d6e6f727468656173742d312e616d617a6f6e6177732e636f6d2f302f3233353235392f66633337653936332d383533302d333731312d643163662d3335366266646666316665322e706e67\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- |  ------------- | \n| [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) |\n\n\n### [Photo2Cartoon](https://drive.google.com/file/d/1xFWZ9Rf1o_LtwBpmSw2zSwPGk2FY6Wya/view?usp=sharing)\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/147394190-01a2c6be-5056-4f83-b4af-3f494dad47f4.png\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/147394192-46de7634-c3ce-481f-afa5-8a7ab4603f2e.png\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | License | Note |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | \n| [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. |\n\n### [AnimeGANv2_Hayao](https://drive.google.com/file/d/1G53oZ1hiMcLJs1loN_fe_VmBVfegh9ha/view?usp=sharing)\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/147421574-8f38367c-d5c5-442d-9742-7b2bb24d43e4.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/147421569-df8e2e59-fef8-4db4-9cb2-65ee960ef705.png\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | Sample |\n| ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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)|\n\n\n### [AnimeGANv2_Paprika](https://drive.google.com/file/d/10drMcmF67iREUK8NY8ekMHrsyVirs5XT/view?usp=sharing)\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/144670978-1447ce28-db49-4cf9-b484-3142ef703ade.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/144671455-f7258cc9-1a3e-49df-8bbb-03285c619b17.png\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | \n| ------------- | ------------- | ------------- | ------------- | \n| [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)|\n\n\n### [WarpGAN Caricature](https://drive.google.com/file/d/1HE3qvfjuXZMFelRcmmGsLzoO5dV8lnaQ/view?usp=sharing)\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/147397894-e2d3a1ef-7afa-410a-9580-f09ef7157c50.JPG\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/147421276-574edb28-f909-4830-afd0-5cb41328bdba.JPG\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | \n| ------------- | ------------- | ------------- | ------------- | \n| [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)|\n\n### [UGATIT_selfie2anime](https://drive.google.com/file/d/1o15OO0Kn0tq79fFkmBm3PES93IRQOxB-/view?usp=sharing)\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2021-12-27 8 18 33\" src=\"https://user-images.githubusercontent.com/23278992/147422391-847b3c75-3e6e-419e-9a53-f6138b9ac813.png\"\u003e \u003cimg width=\"400\" alt=\"スクリーンショット 2021-12-27 8 28 11\" src=\"https://user-images.githubusercontent.com/23278992/147422387-2b71a135-cd9c-4f02-8223-65bf365cda4e.png\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | \n| ------------- | ------------- | ------------- | ------------- | \n| [UGATIT_selfie2anime](https://drive.google.com/file/d/1o15OO0Kn0tq79fFkmBm3PES93IRQOxB-/view?usp=sharing) | 266.2MB(quantized) | Image(256x256) | [taki0112/UGATIT](https://github.com/taki0112/UGATIT)  |\n\n### CartoonGAN\n\n| Google Drive Link | Size | Output | Original Project | \n| ------------- | ------------- | ------------- | ------------- | \n| [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)|\n| [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)|\n| [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)|\n| [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)|\n\n### [Fast-Neural-Style-Transfer](https://drive.google.com/file/d/1o15OO0Kn0tq79fFkmBm3PES93IRQOxB-/view?usp=sharing)\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/155708074-ab651a7c-b882-40f1-9ce5-a94e80bac62d.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/155708089-ee888836-3f18-41a1-97fd-72e17e604c9a.jpg\"\u003e\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/155707184-403ad161-6354-4ce4-87d4-284e323b1261.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/155708401-f76481ad-1de7-4262-acc2-9dcb61c89784.jpg\"\u003e\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/155707199-b77b2583-c355-4406-bc9a-3248492df2c7.jpg\"\u003e \u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/155706861-97e629a0-4322-4924-94ed-cb10c966bfb8.jpg\"\u003e\n\n| Google Drive Link | Size | Output |Original Project | License |Year|\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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|\n| [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|\n| [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|\n\n### [White_box_Cartoonization](https://drive.google.com/file/d/1QGNJzEp0fo6oOryTos1dazEKaS34WzZC/view?usp=sharing)\n\nLearning to Cartoonize Using White-box Cartoon Representations\n\n\u003cimg width=\"400\" img src=\"https://user-images.githubusercontent.com/23278992/189335273-d05f9cdb-1375-4553-8146-2f598676a95b.jpg\"\u003e \u003cimg width=\"400\" img src=\"https://user-images.githubusercontent.com/23278992/189335456-5184b222-9b55-429e-850a-adf4879a47fc.jpg\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | License |Year|\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | \n| [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|\n\n### [FacialCartoonization](https://drive.google.com/file/d/1CJH4tuR3ArKvxrmAE_44lbsAwUzjtyXi/view?usp=sharing)\n\nWhite-box facial image cartoonizaiton\n\n\u003cimg width=\"400\" img src=\"https://user-images.githubusercontent.com/23278992/189454922-1a95ca25-4031-47a7-8914-9fb8e5c7ff58.png\"\u003e \u003cimg width=\"400\" img src=\"https://user-images.githubusercontent.com/23278992/189454801-19d6ef20-7361-41a5-b85b-5dbd7cf05adb.png\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | License |Year|\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | \n| [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|\n\n# Inpainting\n\n### AOT-GAN-for-Inpainting\n\n\u003cimg width=\"400\" src=\"https://user-images.githubusercontent.com/23278992/220097750-0cd3f94e-1c60-4e03-b9dc-e1ea14f3e57c.gif\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | License | Note | Sample Project |\n| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |\n|[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) |\n\n### [Lama](https://drive.google.com/drive/folders/1s_uICJQykFFxgVubpBNeLLDL0JsxgdCd?usp=sharing)\n\n\u003cimg width=\"400\" src=\"https://github.com/john-rocky/PersonSegmentationSampler/assets/23278992/847f874b-7174-4317-8313-f82685bdd20c\"\u003e\n\n| Google Drive Link | Size | Input | Output | Original Project | License | Note | Sample Project | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- |------------- |------------- |------------- |------------- |------------- |\n|[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)|\n\n# Monocular Depth Estimation\n\n### [MiDaS](https://drive.google.com/file/d/1agGnt5Cq5CGzoNDl9Nb-3u7pB5SrIbN4/view?usp=share_link)\nTowards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer\n\n\u003cimg width=\"400\" img src=\"https://user-images.githubusercontent.com/23278992/224542700-701472b7-fa8c-4824-a966-f9490f7c780f.jpg\"\u003e \u003cimg width=\"400\" img src=\"https://user-images.githubusercontent.com/23278992/224542703-11ed535f-40c6-4a45-8e3f-d42ce2b9c6f9.jpeg\"\u003e\n\n| Google Drive Link | Size | Output | Original Project | License |Year|Conversion Script |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | \n| [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) |\n\n# Stable Diffusion\n\n### [stable-diffusion-v1-5](https://drive.google.com/file/d/1dqYEdhSPi7y0Dgans-Fk7_ViNviUTUJj/view?usp=share_link)\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2023-03-21 18 52 18\" src=\"https://user-images.githubusercontent.com/23278992/226571395-0815ebdb-39e1-4763-bb16-25c33c5ae9bb.png\"\u003e\n\n| Google Drive Link  | Original Model |Original Project | License | Run on mac |Conversion Script |Year|\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | \n| [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|\n\n### [pastel-mix](https://drive.google.com/file/d/1cp3VoF1R-as8_lScWGUoxl-BNVX3d7vb/view?usp=share_link)\n\nPastel Mix - a stylized latent diffusion model.This model is intended to produce high-quality, highly detailed anime style with just a few prompts.\n\n\u003cimg width=\"400\" alt=\"スクリーンショット 2023-03-21 19 54 13\" src=\"https://user-images.githubusercontent.com/23278992/226585761-3eaba244-7fea-4529-af36-0962fe624936.png\"\u003e\n\n| Google Drive Link  | Original Model | License | Run on mac |Conversion Script |Year|\n| ------------- | ------------- | ------------- |  ------------- | ------------- | ------------- | \n| [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|\n\n### [Orange Mix](https://drive.google.com/file/d/1ueU-RuZIsl3b3F7uu_gBa_SfAtGTzTI5/view?usp=share_link)\n\n\u003cimg width=\"800\" alt=\"スクリーンショット 2023-03-21 23 34 13\" src=\"https://user-images.githubusercontent.com/23278992/226656177-8260d83c-6e93-4d9b-8fbd-154a0028f88d.png\"\u003e\n\n| Google Drive Link  | Original Model | License | Run on mac |Conversion Script |Year|\n| ------------- | ------------- | ------------- |  ------------- | ------------- | ------------- | \n| [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|\n\n### [Counterfeit](https://drive.google.com/file/d/1Kt_8hnGUGnJAUnuergLki37GKnWjWOJp/view?usp=share_link)\n\n\u003cimg width=\"800\" alt=\"スクリーンショット 2023-03-22 0 47 53\" src=\"https://user-images.githubusercontent.com/23278992/226731352-c6ad077d-6f91-4a03-a6e5-dd01ce398d9c.png\"\u003e\n\n| Google Drive Link  | Original Model | License | Run on mac |Conversion Script |Year|\n| ------------- | ------------- | ------------- |  ------------- | ------------- | ------------- | \n| [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|\n\n\n### [anything-v4](https://drive.google.com/file/d/1yF55CGy4I3BKom_E70lLkU6N03nSvjDt/view?usp=share_link)\n\n\u003cimg width=\"800\" alt=\"スクリーンショット 2023-03-22 0 47 53\" src=\"https://user-images.githubusercontent.com/23278992/226734890-8b48320f-5b4c-4f6c-bd56-07954f573582.png\"\u003e\n\n| Google Drive Link  | Original Model | License | Run on mac |Conversion Script |Year|\n| ------------- | ------------- | ------------- |  ------------- | ------------- | ------------- | \n| [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|\n\n### [Openjourney](https://drive.google.com/file/d/1KIhSG7pHjgldg7r2mm1Yuwa85BceFLsk/view?usp=share_link)\n\n\u003cimg width=\"800\" alt=\"スクリーンショット 2023-03-22 7 49 39\" src=\"https://user-images.githubusercontent.com/23278992/226909583-42efdb55-e2f0-4331-be0d-7f4bcd2c8b2c.png\"\u003e\n\n| Google Drive Link  | Original Model | License | Run on mac |Conversion Script |Year|\n| ------------- | ------------- | ------------- |  ------------- | ------------- | ------------- | \n| [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|\n\n### [dreamlike-photoreal-2](https://drive.google.com/file/d/1D5RXYE52wyXPq6TdCHM8DIkP4dxHafwt/view?usp=share_link)\n\n\u003cimg width=\"800\" alt=\"dreamlike\" src=\"https://user-images.githubusercontent.com/23278992/226922948-1af2334b-0798-4aef-bfb4-464438dde1b9.png\"\u003e\n\n| Google Drive Link  | Original Model | License | Run on mac |Conversion Script |Year|\n| ------------- | ------------- | ------------- |  ------------- | ------------- | ------------- | \n| [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|\n\n# Image Colorization\n\n### DDColor Tiny\n\nDDColor — AI image colorization for grayscale/B\u0026W photos using dual decoders (ICCV 2023).\n\n| Input | Output |\n|---|---|\n| \u003cimg width=\"300\" src=\"https://github.com/user-attachments/assets/051491a3-14c2-42af-9992-41c4238bcfd1\"\u003e | \u003cimg width=\"300\" src=\"https://github.com/user-attachments/assets/ab1a8e3a-7b6c-4150-945b-6f94e4858ed7\"\u003e |\n\n| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n# Face Recognition\n\n### AdaFace IR-18\n\nAdaFace — Quality-adaptive face recognition. Outputs 512-dim embedding for face verification and identification.\n\n\u003cimg width=\"300\" src=\"https://github.com/user-attachments/assets/e5938858-9560-4aaf-9d21-f0059daa255e\"\u003e\n\n| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n# 3D Face Pose Estimation\n\n### 3DDFA_V2\n\n3DDFA_V2 — 3D face reconstruction and head pose estimation (yaw, pitch, roll) from a single face image.\n\n\u003cimg width=\"300\" src=\"https://github.com/user-attachments/assets/43e4c2a6-c30b-4a31-b759-fb5468c843a7\"\u003e\n\n| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n# Speaker Diarization\n\n### pyannote segmentation-3.0\n\npyannote segmentation — Speaker diarization with up to 3 simultaneous speakers. Identifies who speaks when, with overlap detection and per-speaker transcription.\n\n| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n# Voice Conversion\n\n### OpenVoice V2\n\nOpenVoice — Zero-shot voice conversion. Record source and target voice, convert on-device.\n\n\u003cvideo src=\"https://github.com/user-attachments/assets/70078691-14df-4350-846c-9ba1682433ce\" width=\"300\"\u003e\u003c/video\u003e\n\n| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n| [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) | | | | | |\n\n# Audio Source Separation\n\n### HTDemucs\n\nHybrid Transformer Demucs — separates music into 4 stems: drums, bass, vocals, and other instruments.\n\n\u003cvideo src=\"https://github.com/user-attachments/assets/98dea359-e557-4e46-af1d-2010503c86ba\" width=\"400\"\u003e\u003c/video\u003e\n\n| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n# Vision-Language\n\n### Florence-2-base\n\nMicrosoft 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.\n\n| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n# Zero-Shot Image Classification\n\n### SigLIP ViT-B/16\n\nGoogle 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.\n\n| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n\n# Text-to-Music Generation\n\n### Stable Audio Open Small\n\n[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.\n\n\u003cvideo src=\"https://github.com/user-attachments/assets/ea448e41-d5ae-407e-84a6-8312c1108cfd\" width=\"400\"\u003e\u003c/video\u003e\n\n4 CoreML models: T5 text encoder, NumberEmbedder (seconds conditioning), DiT (diffusion transformer), and VAE decoder (Oobleck).\n\n| Download Link | Size | Input | Output | Original Project | License | Year | Sample Project | Conversion Script |\n| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- |\n| [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) |\n| [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] | | | | | |\n| [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] | | | | | |\n| [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] | | | | | |\n| [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 | | | | | |\n\n**Conversion notes:**\n- DiT INT8 (`StableAudioDiT`): use with `cpuAndGPU`. Fastest, slight quality loss from quantization.\n- 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.\n- T5 INT8: may produce occasional NaN values — the sample app sanitizes these before passing to DiT.\n- VAE Decoder FP16: weight_norm must be removed before tracing (Snake activation).\n\n## Models converted by someone other than me.\n\n### [Stable Diffusion](https://github.com/apple/ml-stable-diffusion)\n[apple/ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion)\n\n## How to use in a xcode project.\n\n### Option 1,implement Vision request.\n\n```swift:\n\nimport Vision\nlazy var coreMLRequest:VNCoreMLRequest = {\n   let model = try! VNCoreMLModel(for: modelname().model)\n   let request = VNCoreMLRequest(model: model, completionHandler: self.coreMLCompletionHandler)\n   return request\n   }()\n\nlet handler = VNImageRequestHandler(ciImage: ciimage,options: [:])\n   DispatchQueue.global(qos: .userInitiated).async {\n   try? handler.perform([coreMLRequest])\n}\n```\n\nIf the model has Image type output:\n\n```swift\nlet result = request?.results?.first as! VNPixelBufferObservation\nlet uiimage = UIImage(ciImage: CIImage(cvPixelBuffer: result.pixelBuffer))\n```\n\nElse the model has Multiarray type output:\n\nFor visualizing multiArray as image, Mr. Hollance’s “CoreML Helpers” are very convenient.\n[CoreML Helpers](https://github.com/hollance/CoreMLHelpers)\n\n[Converting from MultiArray to Image with CoreML Helpers.](https://medium.com/@rockyshikoku/converting-from-multiarray-to-image-with-coreml-helpers-59fdc34d80d8)\n\n```swift:\nfunc coreMLCompletionHandler（request：VNRequest？、error：Error？）{\n   let = coreMLRequest.results？.first as！VNCoreMLFeatureValueObservation\n   let multiArray = result.featureValue.multiArrayValue\n   let cgimage = multiArray？.cgImage（min：-1、max：1、channel：nil）\n```\n\n### Option 2,Use [CoreGANContainer](https://github.com/john-rocky/CoreGANContainer). You can use models with dragging\u0026dropping into the container project. \n\n# Make the model lighter\nYou can make the model size lighter with Quantization if you want.\nhttps://coremltools.readme.io/docs/quantization\n\u003eThe lower the number of bits, more the chances of degrading the model accuracy. The loss in accuracy varies with the model.\n\n```python\nimport coremltools as ct\nfrom coremltools.models.neural_network import quantization_utils\n\n# load full precision model\nmodel_fp32 = ct.models.MLModel('model.mlmodel')\n\nmodel_fp16 = quantization_utils.quantize_weights(model_fp32, nbits=16)\n# nbits can be 16(half size model), 8(1/4), 4(1/8), 2, 1\n```\n\n##### quantized sample (U2Net)\n\n##### InputImage / nbits=32(original) / nbits=16 / nbits=8 / nbits=4\n\n\u003cimg src=\"https://user-images.githubusercontent.com/23278992/147712147-0959c0b9-9d4b-4049-9dd9-7a9d1ffa0eed.JPEG\" width=200\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/147712215-dd0c8788-75ad-4676-804a-fdd47233daa6.JPG\" width=200\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/147712220-d02ab436-9716-4cdc-91d3-8b6f3aa01fac.JPG\" width=200\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/147712259-aabf5ecf-db59-476d-8f36-e6027dfb91e2.JPG\" width=200\u003e \u003cimg src=\"https://user-images.githubusercontent.com/23278992/147712328-a44f538c-aa3e-431d-98ec-626239262e01.JPG\" width=200\u003e\n\n\n\n# Thanks\nCover image was taken from Ghibli free images. \n\nOn YOLOv5 convertion, [dbsystel/yolov5-coreml-tools](https://github.com/dbsystel/yolov5-coreml-tools) give me the super inteligent convert script. \n\nAnd all of original projects\n\n# Auther\n\nDaisuke Majima\nFreelance engineer. iOS/MachineLearning/AR\nI can work on mobile ML projects and AR project.\nFeel free to contact: rockyshikoku@gmail.com\n\n[GitHub](https://github.com/john-rocky)\n[Twitter](https://twitter.com/JackdeS11)\n[Medium](https://rockyshikoku.medium.com/)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjohn-rocky%2Fcoreml-models","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjohn-rocky%2Fcoreml-models","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjohn-rocky%2Fcoreml-models/lists"}