CoreML-Models

Converted CoreML Model Zoo.

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

How to use

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

Section Link

Image Classifier
Object Detection
- YOLOv5s
Segmentation
- U2Net
- face-parsing
- Segformer
- BiseNetv2
- DNL
- ISANet
- FastFCN
- GCNet
- DANet
- Semantic FPN
- cloths_segmentation
Super Resolution
- Real ESRGAN
- BSRGAN
- A-ESRGAN
- Beby-GAN
- RRDN
- Fast-SRGAN
Low Light Enhancement
- StableLLVE
- Zero-DCE
Image Restoration
- MPRNet
Image Generation
- MobileStyleGAN
- DCGAN
Image2Image

How to get the model

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

Image Classifier

Efficientnet

Google Drive Link	Size	Dataset	Original Project	License
Efficientnetb0	22.7 MB	ImageNet	TensorFlowHub	Apache2.0

Efficientnetv2

Google Drive Link	Size	Dataset	Original Project	License	Year
Efficientnetv2	85.8 MB	ImageNet	Google/autoML	Apache2.0	2021

VisionTransformer

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

Google Drive Link	Size	Dataset	Original Project	License	Year
VisionTransformer-B16	347.5 MB	ImageNet	google-research/vision_transformer	Apache2.0	2021

Conformer

Local Features Coupling Global Representations for Visual Recognition.

Google Drive Link	Size	Dataset	Original Project	License	Year
Conformer-tiny-p16	94.1 MB	ImageNet	pengzhiliang/Conformer	Apache2.0	2021

DeiT

Data-efficient Image Transformers

Google Drive Link	Size	Dataset	Original Project	License	Year
DeiT-base384	350.5 MB	ImageNet	facebookresearch/deit	Apache2.0	2021

RepVGG

Making VGG-style ConvNets Great Again

Google Drive Link	Size	Dataset	Original Project	License	Year
RepVGG-A0	33.3 MB	ImageNet	DingXiaoH/RepVGG	MIT	2021

RegNet

Designing Network Design Spaces

Google Drive Link	Size	Dataset	Original Project	License	Year
regnet_y_400mf	16.5 MB	ImageNet	TORCHVISION.MODELS	MIT	2020

Object Detection

YOLOv5s

Google Drive Link	Size	Output	Original Project	License	Note	Sample Project
YOLOv5s	29.3MB	Confidence(MultiArray (Double 0 × 80)), Coordinates (MultiArray (Double 0 × 4))	ultralytics/yolov5	GNU	Non Maximum Suppression has been added.	CoreML-YOLOv5

Segmentation

U2Net

Google Drive Link	Size	Output	Original Project	License
U2Net	175.9 MB	Image(GRAYSCALE 320 × 320)	xuebinqin/U-2-Net	Apache
U2Netp	4.6 MB	Image(GRAYSCALE 320 × 320)	xuebinqin/U-2-Net	Apache

face-Parsing

Google Drive Link	Size	Output	Original Project	License	Sample Project
face-Parsing	53.2 MB	MultiArray(1 x 512 × 512)	zllrunning/face-parsing.PyTorch	MIT	CoreML-face-parsing

Segformer

Simple and Efficient Design for Semantic Segmentation with Transformers

Google Drive Link	Size	Output	Original Project	License	year
SegFormer_mit-b0_1024x1024_cityscapes	14.9 MB	MultiArray(512 × 1024)	NVlabs/SegFormer	NVIDIA	2021

BiSeNetV2

Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation

Google Drive Link	Size	Output	Original Project	License	year
BiSeNetV2_1024x1024_cityscapes	12.8 MB	MultiArray	ycszen/BiSeNet	Apache2.0	2021

DNL

Disentangled Non-Local Neural Networks

Google Drive Link	Size	Output	Dataset	Original Project	License	year
dnl_r50-d8_512x512_80k_ade20k	190.8 MB	MultiArray[512x512]	ADE20K	yinmh17/DNL-Semantic-Segmentation	Apache2.0	2020

ISANet

Interlaced Sparse Self-Attention for Semantic Segmentation

Google Drive Link	Size	Output	Dataset	Original Project	License	year
isanet_r50-d8_512x512_80k_ade20k	141.5 MB	MultiArray[512x512]	ADE20K	openseg-group/openseg.pytorch	MIT	ArXiv'2019/IJCV'2021

FastFCN

Rethinking Dilated Convolution in the Backbone for Semantic Segmentation

Google Drive Link	Size	Output	Dataset	Original Project	License	year
fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k	326.2 MB	MultiArray[512x512]	ADE20K	wuhuikai/FastFCN	MIT	ArXiv'2019

GCNet

Non-local Networks Meet Squeeze-Excitation Networks and Beyond

Google Drive Link	Size	Output	Dataset	Original Project	License	year
gcnet_r50-d8_512x512_20k_voc12aug	189 MB	MultiArray[512x512]	PascalVOC	xvjiarui/GCNet	Apache License 2.0	ICCVW'2019/TPAMI'2020

DANet

Dual Attention Network for Scene Segmentation(CVPR2019)

Google Drive Link	Size	Output	Dataset	Original Project	License	year
danet_r50-d8_512x1024_40k_cityscapes	189.7 MB	MultiArray[512x1024]	CityScapes	junfu1115/DANet	MIT	CVPR2019

Semantic-FPN

Panoptic Feature Pyramid Networks

Google Drive Link	Size	Output	Dataset	Original Project	License	year
fpn_r50_512x1024_80k_cityscapes	108.6 MB	MultiArray[512x1024]	CityScapes	facebookresearch/detectron2	Apache License 2.0	2019

cloths_segmentation

Code for binary segmentation of various cloths.

Google Drive Link	Size	Output	Dataset	Original Project	License	year
clothSegmentation	50.1 MB	Image(GrayScale 640x960)	fashion-2019-FGVC6	facebookresearch/detectron2	MIT	2020

Super Resolution

Real ESRGAN

Google Drive Link	Size	Output	Original Project	License	year
Real ESRGAN4x	66.9 MB	Image(RGB 2048x2048)	xinntao/Real-ESRGAN	BSD 3-Clause License	2021
Real ESRGAN Anime4x	66.9 MB	Image(RGB 2048x2048)	xinntao/Real-ESRGAN	BSD 3-Clause License	2021

BSRGAN

Google Drive Link	Size	Output	Original Project	License	year
BSRGAN	66.9 MB	Image(RGB 2048x2048)	cszn/BSRGAN		2021

A-ESRGAN

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

Beby-GAN

Best-Buddy GANs for Highly Detailed Image Super-Resolution

Google Drive Link	Size	Output	Original Project	License	year
Beby-GAN	66.9 MB	Image(RGB 2048x2048)	dvlab-research/Simple-SR	MIT	2021

RRDN

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

Google Drive Link	Size	Output	Original Project	License	year
RRDN	16.8 MB	Image(RGB 2048x2048)	idealo/image-super-resolution	Apache2.0	2018

Fast-SRGAN

Fast-SRGAN.

Google Drive Link	Size	Output	Original Project	License	year
Fast-SRGAN	628 KB	Image(RGB 1024x1024)	HasnainRaz/Fast-SRGAN	MIT	2019

Low Light Enhancement

StableLLVE

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

Google Drive Link	Size	Output	Original Project	License	Year
StableLLVE	17.3 MB	Image(RGB 512x512)	zkawfanx/StableLLVE	MIT	2021

Zero-DCE

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

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

Image Restoration

MPRNet

Multi-Stage Progressive Image Restoration.

Debluring

Denoising

Deraining

Google Drive Link	Size	Output	Original Project	License	Year
MPRNetDebluring	137.1 MB	Image(RGB 512x512)	swz30/MPRNet	MIT	2021
MPRNetDeNoising	108 MB	Image(RGB 512x512)	swz30/MPRNet	MIT	2021
MPRNetDeraining	24.5 MB	Image(RGB 512x512)	swz30/MPRNet	MIT	2021

Image Generation

MobileStyleGAN

Google Drive Link	Size	Output	Original Project	License	Sample Project
MobileStyleGAN	38.6MB	Image(Color 1024 × 1024)	bes-dev/MobileStyleGAN.pytorch	Nvidia Source Code License-NC	CoreML-StyleGAN

DCGAN

Google Drive Link	Size	Output	Original Project
DCGAN	9.2MB	MultiArray	TensorFlowCore

Image2Image

Anime2Sketch

Google Drive Link	Size	Output	Original Project	License	Usage
Anime2Sketch	217.7MB	Image(Color 512 × 512)	Mukosame/Anime2Sketch	MIT	Drop an image to preview

AnimeGAN2Face_Paint_512_v2

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

Photo2Cartoon

Google Drive Link	Size	Output	Original Project	License	Note
Photo2Cartoon	15.2 MB	Image(Color 256 × 256)	minivision-ai/photo2cartoon	MIT	The output is little bit different from the original model. It cause some operations were converted replaced　manually.

AnimeGANv2_Hayao

Google Drive Link	Size	Output	Original Project
AnimeGANv2_Hayao	8.7MB	Image(256 x 256)	TachibanaYoshino/AnimeGANv2

AnimeGANv2_Paprika

Google Drive Link	Size	Output	Original Project
AnimeGANv2_Paprika	8.7MB	Image(256 x 256)	TachibanaYoshino/AnimeGANv2

WarpGAN Caricature

Google Drive Link	Size	Output	Original Project
WarpGAN Caricature	35.5MB	Image(256 x 256)	seasonSH/WarpGAN

UGATIT_selfie2anime

Google Drive Link	Size	Output	Original Project
UGATIT_selfie2anime	266.2MB(quantized)	Image(256x256)	taki0112/UGATIT

CartoonGAN

Google Drive Link	Size	Output	Original Project
CartoonGAN_Shinkai	44.6MB	MultiArray	mnicnc404/CartoonGan-tensorflow
CartoonGAN_Hayao	44.6MB	MultiArray	mnicnc404/CartoonGan-tensorflow
CartoonGAN_Hosoda	44.6MB	MultiArray	mnicnc404/CartoonGan-tensorflow
CartoonGAN_Paprika	44.6MB	MultiArray	mnicnc404/CartoonGan-tensorflow

Fast-Neural-Style-Transfer

Google Drive Link	Size	Output	Original Project	License	Year
fast-neural-style-transfer-cuphead	6.4MB	Image(RGB 960x640)	eriklindernoren/Fast-Neural-Style-Transfer	MIT	2019
fast-neural-style-transfer-starry-night	6.4MB	Image(RGB 960x640)	eriklindernoren/Fast-Neural-Style-Transfer	MIT	2019
fast-neural-style-transfer-mosaic	6.4MB	Image(RGB 960x640)	eriklindernoren/Fast-Neural-Style-Transfer	MIT	2019

How to use in a xcode project.

Option 1,implement Vision request.


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）

Option 2,Use CoreGANContainer. You can use models with dragging&dropping into the container project.

Make the model lighter

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

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

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

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

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

quantized sample (U2Net)

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

Thanks

Cover image was taken from Ghibli free images.

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

And all of original projects

Auther

Daisuke Majima Freelance engineer. iOS/MachineLearning/AR I can work on mobile ML projects and AR project. Feel free to contact: [email protected]

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