Converted Core ML Model Zoo.

Core ML is a machine learning framework by Apple. If you are iOS developer, you can easly use machine learning models in your Xcode project.

Take a look this model zoo, and if you found the CoreML model you want, download the model from google drive link and bundle it in your project. Or if the model have sample project link, try it and see how to use the model in the project. You are free to do or not.

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

• Image Classifier

  • Efficientnetb0
  • Efficientnetv2
  • VisionTransformer
  • Conformer
  • DeiT
  • RepVGG
  • RegNet
  • MobileViTv2

• Object Detection

  • YOLOv5s
  • YOLOv7
  • YOLOv8

• Segmentation

  • U2Net
  • IS-Net
  • RMBG1.4
  • face-parsing
  • Segformer
  • BiseNetv2
  • DNL
  • ISANet
  • FastFCN
  • GCNet
  • DANet
  • Semantic FPN
  • cloths_segmentation
  • easyportrait

• Super Resolution

  • Real ESRGAN
  • GFPGAN
  • BSRGAN
  • A-ESRGAN
  • Beby-GAN
  • RRDN
  • Fast-SRGAN
  • ESRGAN
  • SRGAN
  • SRResNet
  • LESRCNN
  • MMRealSR
  • DASR

• Low Light Enhancement

  • StableLLVE
  • Zero-DCE

• Image Restoration

  • MPRNet
  • MIRNetv2

• Image Generation

  • MobileStyleGAN
  • DCGAN

• Image2Image

  • Anime2Sketch
  • AnimeGAN2Face_Paint_512_v2
  • Photo2Cartoon
  • AnimeGANv2_Hayao
  • AnimeGANv2_Paprika
  • WarpGAN Caricature
  • UGATIT_selfie2anime
  • Fast-Neural-Style-Transfer
  • White_box_Cartoonization
  • FacialCartoonization

• Inpainting

  • AOT-GAN-for-Inpainting
  • Lama

• Monocular Depth Estimation

  • MiDaS

• Stable Diffusion :text2image

  • stable-diffusion-v1-5
  • pastel-mix
  • Orange Mix
  • Counterfeit-V2.5
  • anything-v4.5
  • Openjourney
  • dreamlike-photoreal-2.0

You can get the model converted to CoreML format from the link of Google drive. See the section below for how to use it in Xcode. The license for each model conforms to the license for the original project.

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

Local Features Coupling Global Representations for Visual Recognition.

Data-efficient Image Transformers

Making VGG-style ConvNets Great Again

Designing Network Design Spaces

CVNets: A library for training computer vision networks

Google Drive Link	Size	Dataset	Original Project	License	Year	Conversion Script
MobileViTv2	18.8 MB	ImageNet	apple/ml-cvnets	apple	2022

Google Drive Link	Size	Output	Original Project	License	Note	Sample Project	Conversion Script
YOLOv7	147.9MB	Confidence(MultiArray (Double 0 × 80)), Coordinates (MultiArray (Double 0 × 4))	WongKinYiu/yolov7	GNU	Non Maximum Suppression has been added.	CoreML-YOLOv5

Google Drive Link	Size	Output	Original Project	License	Year	Conversion Script
IS-Net	176.1 MB	Image(GRAYSCALE 1024 × 1024)	xuebinqin/DIS	Apache	2022
IS-Net-General-Use	176.1 MB	Image(GRAYSCALE 1024 × 1024)	xuebinqin/DIS	Apache	2022

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

Google Drive Link	Size	Output	Original Project	License	year	Conversion Script
RMBG.mlpackage/RMBG.mlmodel	176 MB	Image(GrayScale 1024x1024)	briaai/RMBG-1.4	Creative Commons	2024

Simple and Efficient Design for Semantic Segmentation with Transformers

Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation

Disentangled Non-Local Neural Networks

Interlaced Sparse Self-Attention for Semantic Segmentation

Rethinking Dilated Convolution in the Backbone for Semantic Segmentation

Non-local Networks Meet Squeeze-Excitation Networks and Beyond

Dual Attention Network for Scene Segmentation(CVPR2019)

Panoptic Feature Pyramid Networks

Code for binary segmentation of various cloths.

EasyPortrait - Face Parsing and Portrait Segmentation Dataset.

Google Drive Link	Size	Output	Original Project	License	year	Swift sample	Conversion Script
easyportrait-segformer512-fp	7.6 MB	Image(GrayScale 512x512) * 9	hukenovs/easyportrait	Creative Commons	2023	easyportrait-coreml

Towards Real-World Blind Face Restoration with Generative Facial Prior

Google Drive Link	Size	Output	Original Project	License	year	Conversion Script
A-ESRGAN	63.8 MB	Image(RGB 1024x1024)	aesrgan/A-ESRGANN	BSD 3-Clause License	2021

Best-Buddy GANs for Highly Detailed Image Super-Resolution

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

Fast-SRGAN.

Enhanced-SRGAN.

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

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

Lightweight Image Super-Resolution with Enhanced CNN.

Google Drive Link	Size	Output	Original Project	License	year	Conversion Script
LESRCNN	4.3 MB	Image(RGB 512x512)	hellloxiaotian/LESRCNN		2020

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

Google Drive Link	Size	Output	Original Project	License	year	Conversion Script
MMRealSRGAN	104.6 MB	Image(RGB 1024x1024)	TencentARC/MM-RealSR	BSD 3-Clause	2022
MMRealSRNet	104.6 MB	Image(RGB 1024x1024)	TencentARC/MM-RealSR	BSD 3-Clause	2022

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

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

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

Google Drive Link	Size	Output	Original Project	License	Year	Conversion Script
Zero-DCE	320KB	Image(RGB 512x512)	Li-Chongyi/Zero-DCE	See Repo	2021

Multi-Stage Progressive Image Restoration.

Debluring

Denoising

Deraining

Learning Enriched Features for Fast Image Restoration and Enhancement.

Denoising

Super Resolution

Contrast Enhancement

Low Light Enhancement

Google Drive Link	Size	Output	Original Project	License	Year	Conversion Script
MIRNetv2Denoising	42.5 MB	Image(RGB 512x512)	swz30/MIRNetv2	ACADEMIC PUBLIC LICENSE	2022
MIRNetv2SuperResolution	42.5 MB	Image(RGB 512x512)	swz30/MIRNetv2	ACADEMIC PUBLIC LICENSE	2022
MIRNetv2ContrastEnhancement	42.5 MB	Image(RGB 512x512)	swz30/MIRNetv2	ACADEMIC PUBLIC LICENSE	2022
MIRNetv2LowLightEnhancement	42.5 MB	Image(RGB 512x512)	swz30/MIRNetv2	ACADEMIC PUBLIC LICENSE	2022

Google Drive Link	Size	Output	Original Project	Conversion Script
AnimeGAN2Face_Paint_512_v2	8.6MB	Image(Color 512 × 512)	bryandlee/animegan2-pytorch

Learning to Cartoonize Using White-box Cartoon Representations

White-box facial image cartoonizaiton

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

Google Drive Link	Size	Output	Original Project	License	Year	Conversion Script
MiDaS_Small	66.3MB	MultiArray(1x256x256)	isl-org/MiDaS	MIT	2022

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

apple/ml-stable-diffusion

import Vision
lazy var coreMLRequest:VNCoreMLRequest = {
   let model = try! VNCoreMLModel(for: modelname().model)
   let request = VNCoreMLRequest(model: model, completionHandler: self.coreMLCompletionHandler)
   return request
   }()

let handler = VNImageRequestHandler(ciImage: ciimage,options: [:])
   DispatchQueue.global(qos: .userInitiated).async {
   try? handler.perform([coreMLRequest])
}

If the model has Image type output:

let result = request?.results?.first as! VNPixelBufferObservation
let uiimage = UIImage(ciImage: CIImage(cvPixelBuffer: result.pixelBuffer))

Else the model has Multiarray type output:

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

Converting from MultiArray to Image with CoreML Helpers.

func coreMLCompletionHandler（request：VNRequest？、error：Error？）{
   let = coreMLRequest.results？.first as！VNCoreMLFeatureValueObservation
   let multiArray = result.featureValue.multiArrayValue
   let cgimage = multiArray？.cgImage（min：-1、max：1、channel：nil）

You can make the model size lighter with Quantization if you want. https://coremltools.readme.io/docs/quantization

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

import coremltools as ct
from coremltools.models.neural_network import quantization_utils

# load full precision model
model_fp32 = ct.models.MLModel('model.mlmodel')

model_fp16 = quantization_utils.quantize_weights(model_fp32, nbits=16)
# nbits can be 16(half size model), 8(1/4), 4(1/8), 2, 1

Cover image was taken from Ghibli free images.

On YOLOv5 convertion, dbsystel/yolov5-coreml-tools give me the super inteligent convert script.

And all of original projects

Daisuke Majima Freelance engineer. iOS/MachineLearning/AR I can work on mobile ML projects and AR project. Feel free to contact: rockyshikoku@gmail.com

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