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SemanticSegmentation-CoreML

platform-ios swift-version lisence

This project is Object Segmentation on iOS with Core ML.
If you are interested in iOS + Machine Learning, visit here you can see various DEMOs.

DeepLabV3-DEMO1 FaceParsing-DEMO DeepLabV3-DEMO-2 DeepLabV3-DEMO-3

How it works

When use Metal

image

Requirements

  • Xcode 10.2+
  • iOS 12.0+
  • Swift 5

Models

Download

Download model from apple's model page.

Matadata

Name Input Output Size iOS version+ Download
DeepLabV3 Image (Color 513 × 513) MultiArray (Int32 513 × 513) 8.6 MB iOS 12.0+ link
DeepLabV3FP16 Image (Color 513 × 513) MultiArray (Int32 513 × 513) 4.3 MB iOS 12.0+ link
DeepLabV3Int8LUT Image (Color 513 × 513) MultiArray (Int32 513 × 513) 2.3 MB iOS 12.0+ link
FaceParsing Image (Color 512 × 512) MultiArray (Int32) 512 × 512 52.7 MB iOS 14.0+ link

Inference Time − DeepLabV3

Device Inference Time Total Time (GPU) Total Time (CPU)
iPhone 12 Pro 29 ms 29 ms 240 ms
iPhone 12 Pro Max
iPhone 12 30 ms 31 ms 253 ms
iPhone 12 Mini 29 ms 30 ms 226 ms
iPhone 11 Pro 39 ms 40 ms 290 ms
iPhone 11 Pro Max 35 ms 36 ms 280 ms
iPhone 11
iPhone SE (2nd)
iPhone XS Max
iPhone XS 54 ms 55 ms 327 ms
iPhone XR 133 ms 402 ms
iPhone X 137 ms 143 ms 376 ms
iPhone 8+ 140 ms 146 ms 420 ms
iPhone 8 189 ms 529 ms
iPhone 7+ 240 ms 667 ms
iPhone 7 192 ms 208 ms 528 ms
iPhone 6S + 309 ms 1015 ms

⏲: need to measure

Inference Time − FaceParsing

Device Inference Time Total Time (GPU) Total Time (CPU)
iPhone 12 Pro
iPhone 11 Pro 37 ms 37 ms

Labels − DeepLabV3

# total 21
["background", "aeroplane", "bicycle", "bird", "boat", 
"bottle", "bus", "car", "cat", "chair", 
"cow", "diningtable", "dog", "horse", "motorbike", 
"person", "pottedplant", "sheep", "sofa", "train", 
"tv"]

Labels − FaceParsing

# total 19
["background", "skin", "l_brow", "r_brow", "l_eye", 
"r_eye", "eye_g", "l_ear", "r_ear", "ear_r", 
"nose", "mouth", "u_lip", "l_lip", "neck", 
"neck_l", "cloth", "hair", "hat"]

See also

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The example project of inferencing Semantic Segementation using Core ML

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  • Swift 90.4%
  • Metal 9.6%