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.
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Stable Diffusion :text2image
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.
Google Drive Link | Size | Dataset | Original Project | License |
---|---|---|---|---|
Efficientnetb0 | 22.7 MB | ImageNet | TensorFlowHub | Apache2.0 |
Google Drive Link | Size | Dataset | Original Project | License | Year |
---|---|---|---|---|---|
Efficientnetv2 | 85.8 MB | ImageNet | Google/autoML | Apache2.0 | 2021 |
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 |
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 |
Data-efficient Image Transformers
Google Drive Link | Size | Dataset | Original Project | License | Year |
---|---|---|---|---|---|
DeiT-base384 | 350.5 MB | ImageNet | facebookresearch/deit | Apache2.0 | 2021 |
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 |
Designing Network Design Spaces
Google Drive Link | Size | Dataset | Original Project | License | Year |
---|---|---|---|---|---|
regnet_y_400mf | 16.5 MB | ImageNet | TORCHVISION.MODELS | MIT | 2020 |
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 |
---|---|---|---|---|---|---|
YOLOv5s | 29.3MB | Confidence(MultiArray (Double 0 × 80)), Coordinates (MultiArray (Double 0 × 4)) | ultralytics/yolov5 | GNU | Non Maximum Suppression has been added. | CoreML-YOLOv5 |
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 | Note | Sample Project |
---|---|---|---|---|---|---|
YOLOv8s | 45.1MB | Confidence(MultiArray (Double 0 × 80)), Coordinates (MultiArray (Double 0 × 4)) | ultralytics/ultralytics | GNU | Non Maximum Suppression has been added. | CoreML-YOLOv5 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
Towards Real-World Blind Face Restoration with Generative Facial Prior
Google Drive Link | Size | Output | Original Project | License | year |
---|---|---|---|---|---|
GFPGAN | 337.4 MB | Image(RGB 512x512) | TencentARC/GFPGAN | Apache2.0 | 2021 |
Google Drive Link | Size | Output | Original Project | License | year |
---|---|---|---|---|---|
BSRGAN | 66.9 MB | Image(RGB 2048x2048) | cszn/BSRGAN | 2021 |
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
Google Drive Link | Size | Output | Original Project | License | year |
---|---|---|---|---|---|
Beby-GAN | 66.9 MB | Image(RGB 2048x2048) | dvlab-research/Simple-SR | MIT | 2021 |
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.
Google Drive Link | Size | Output | Original Project | License | year |
---|---|---|---|---|---|
Fast-SRGAN | 628 KB | Image(RGB 1024x1024) | HasnainRaz/Fast-SRGAN | MIT | 2019 |
Enhanced-SRGAN.
Google Drive Link | Size | Output | Original Project | License | year |
---|---|---|---|---|---|
ESRGAN | 66.9 MB | Image(RGB 2048x2048) | xinntao/ESRGAN | Apache 2.0 | 2018 |
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.
Google Drive Link | Size | Output | Original Project | License | year |
---|---|---|---|---|---|
SRGAN | 6.1 MB | Image(RGB 2048x2048) | dongheehand/SRGAN-PyTorch | 2017 |
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.
Google Drive Link | Size | Output | Original Project | License | year |
---|---|---|---|---|---|
SRResNet | 6.1 MB | Image(RGB 2048x2048) | dongheehand/SRGAN-PyTorch | 2017 |
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
Google Drive Link | Size | Output | Original Project | License | year |
---|---|---|---|---|---|
DASR | 12.1 MB | Image(RGB 1024x1024) | The-Learning-And-Vision-Atelier-LAVA/DASR | MIT | 2022 |
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-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
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 |
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 | License | Sample Project |
---|---|---|---|---|---|
MobileStyleGAN | 38.6MB | Image(Color 1024 × 1024) | bes-dev/MobileStyleGAN.pytorch | Nvidia Source Code License-NC | CoreML-StyleGAN |
Google Drive Link | Size | Output | Original Project |
---|---|---|---|
DCGAN | 9.2MB | MultiArray | TensorFlowCore |
Google Drive Link | Size | Output | Original Project | License | Usage |
---|---|---|---|---|---|
Anime2Sketch | 217.7MB | Image(Color 512 × 512) | Mukosame/Anime2Sketch | MIT | Drop an image to preview |
Google Drive Link | Size | Output | Original Project | Conversion Script |
---|---|---|---|---|
AnimeGAN2Face_Paint_512_v2 | 8.6MB | Image(Color 512 × 512) | bryandlee/animegan2-pytorch |
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. |
Google Drive Link | Size | Output | Original Project | Sample |
---|---|---|---|---|
AnimeGANv2_Hayao | 8.7MB | Image(256 x 256) | TachibanaYoshino/AnimeGANv2 | AnimeGANv2-iOS |
Google Drive Link | Size | Output | Original Project |
---|---|---|---|
AnimeGANv2_Paprika | 8.7MB | Image(256 x 256) | TachibanaYoshino/AnimeGANv2 |
Google Drive Link | Size | Output | Original Project |
---|---|---|---|
WarpGAN Caricature | 35.5MB | Image(256 x 256) | seasonSH/WarpGAN |
Google Drive Link | Size | Output | Original Project |
---|---|---|---|
UGATIT_selfie2anime | 266.2MB(quantized) | Image(256x256) | taki0112/UGATIT |
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 |
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 |
Learning to Cartoonize Using White-box Cartoon Representations
Google Drive Link | Size | Output | Original Project | License | Year |
---|---|---|---|---|---|
White_box_Cartoonization | 5.9MB | Image(1536x1536) | SystemErrorWang/White-box-Cartoonization | creativecommons | CVPR2020 |
White-box facial image cartoonizaiton
Google Drive Link | Size | Output | Original Project | License | Year |
---|---|---|---|---|---|
FacialCartoonization | 8.4MB | Image(256x256) | SystemErrorWang/FacialCartoonization | creativecommons | 2020 |
Google Drive Link | Size | Output | Original Project | License | Note | Sample Project |
---|---|---|---|---|---|---|
AOT-GAN-for-Inpainting | 60.8MB | MLMultiArray(3,512,512) | researchmm/AOT-GAN-for-Inpainting | Apache2.0 | To use see sample. | john-rocky/Inpainting-CoreML |
![](https://private-user-images.githubusercontent.com/23278992/293049342-847f874b-7174-4317-8313-f82685bdd20c.gif?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.R6tQrhdCwh2Vi6GAQRvrD_LvtLZnEe00Oc97M9cwEvU)
Google Drive Link | Size | Input | Output | Original Project | License | Note | Sample Project | Conversion Script |
---|---|---|---|---|---|---|---|---|
Lama | 216.6MB | Image (Color 800 × 800), Image (GrayScale 800 × 800) | Image (Color 800 × 800) | advimman/lama | Apache2.0 | To use see sample. | john-rocky/lama-cleaner-iOS | mallman/CoreMLaMa |
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 |
Google Drive Link | Original Model | Original Project | License | Run on mac | Conversion Script | Year |
---|---|---|---|---|---|---|
stable-diffusion-v1-5 | runwayml/stable-diffusion-v1-5 | runwayml/stable-diffusion | Open RAIL M license | godly-devotion/MochiDiffusion | godly-devotion/MochiDiffusion | 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.
Google Drive Link | Original Model | License | Run on mac | Conversion Script | Year |
---|---|---|---|---|---|
pastelMixStylizedAnime_pastelMixPrunedFP16 | andite/pastel-mix | Fantasy.ai | godly-devotion/MochiDiffusion | godly-devotion/MochiDiffusion | 2023 |
Google Drive Link | Original Model | License | Run on mac | Conversion Script | Year |
---|---|---|---|---|---|
AOM3_orangemixs | WarriorMama777/OrangeMixs | CreativeML OpenRAIL-M | godly-devotion/MochiDiffusion | godly-devotion/MochiDiffusion | 2023 |
Google Drive Link | Original Model | License | Run on mac | Conversion Script | Year |
---|---|---|---|---|---|
Counterfeit-V2.5 | gsdf/Counterfeit-V2.5 | - | godly-devotion/MochiDiffusion | godly-devotion/MochiDiffusion | 2023 |
Google Drive Link | Original Model | License | Run on mac | Conversion Script | Year |
---|---|---|---|---|---|
anything-v4.5 | andite/anything-v4.0 | Fantasy.ai | godly-devotion/MochiDiffusion | godly-devotion/MochiDiffusion | 2023 |
Google Drive Link | Original Model | License | Run on mac | Conversion Script | Year |
---|---|---|---|---|---|
Openjourney | prompthero/openjourney | - | godly-devotion/MochiDiffusion | godly-devotion/MochiDiffusion | 2023 |
Google Drive Link | Original Model | License | Run on mac | Conversion Script | Year |
---|---|---|---|---|---|
dreamlike-photoreal-2.0 | dreamlike-art/dreamlike-photoreal-2.0 | CreativeML OpenRAIL-M | godly-devotion/MochiDiffusion | godly-devotion/MochiDiffusion | 2023 |
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.
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: [email protected]