ImageClassification-CoreML

Requirements

• Xcode 9.2+
• iOS 11.0+, 11.2+, 12.0+
• Swift 4

Model

Model Size, Minimum iOS Version, Download Link

Model	Size (MB)	Minimum iOS Version	Download Link
MobileNet	17.1	iOS11	머신 러닝 - 모델 실행 - Apple Developer
MobileNetV2	24.7	iOS11	Machine Learning - Models - Apple Developer
MobileNetV2FP16	12.4	iOS11.2	Machine Learning - Models - Apple Developer
MobileNetV2Int8LUT	6.3	iOS12	Machine Learning - Models - Apple Developer
Resnet50	102.6	iOS11	Machine Learning - Models - Apple Developer
Resnet50FP16	51.3	iOS11.2	Machine Learning - Models - Apple Developer
Resnet50Int8LUT	25.7	iOS12	Machine Learning - Models - Apple Developer
Resnet50Headless	94.4	iOS11	Machine Learning - Models - Apple Developer
SqueezeNet	5	iOS11	Machine Learning - Models - Apple Developer
SqueezeNetFP16	2.5	iOS11.2	Machine Learning - Models - Apple Developer
SqueezeNetInt8LUT	1.3	iOS12	Machine Learning - Models - Apple Developer

Infernece Time (ms)

Infernece Time (ms)

Model vs. Device	12 Pro	12	12 Mini	11 Pro	XS	XS Max	XR	X	7+	7
MobileNet	17	17	14	13	16	18	19	33	43	35
MobileNetV2	15	15	17	14	21	18	21	46	64	53
MobileNetV2FP16	8	17	14	14	20	19	20	48	65	57
MobileNetV2Int8LUT	18	16	16	14	21	21	20	53	64	53
Resnet50	21	18	24	20	27	25	26	61	78	63
Resnet50FP16	19	18	19	20	26	26	27	64	75	74
Resnet50Int8LUT	19	20	20	20	27	25	26	60	77	75
Resnet50Headless	11	11	11	13	18	13	18	36	54	53
SqueezeNet	14	15	17	12	17	17	18	24	35	29
SqueezeNetFP16	13	16	10	13	17	17	18	24	36	29
SqueezeNetInt8LUT	16	17	15	13	18	19	18	27	34	30

Total Time (ms)

Model vs. Device	12 Pro	12	12 Mini	11 Pro	XS	XS Max	XR	X	7+	7
MobileNet	19	18	15	15	18	20	21	35	46	37
MobileNetV2	16	18	19	16	23	21	23	48	67	55
MobileNetV2FP16	8	18	18	15	24	21	23	50	69	60
MobileNetV2Int8LUT	19	18	17	15	23	23	22	55	67	56
Resnet50	22	20	25	22	30	28	29	64	82	66
Resnet50FP16	20	19	20	22	28	28	30	66	78	76
Resnet50Int8LUT	21	21	23	22	29	28	28	63	80	78
Resnet50Headless	11	11	12	14	19	13	18	36	54	54
SqueezeNet	15	16	18	14	18	18	20	25	37	31
SqueezeNetFP16	14	17	11	13	18	18	19	26	38	31
SqueezeNetInt8LUT	18	17	17	14	20	20	19	29	37	32

FPS

Model vs. Device	12 Pro	12	12 Mini	11 Pro	XS	XS Max	XR	X	7+	7
MobileNet	22	24	24	29	23	23	23	23	20	23
MobileNetV2	25	24	24	29	23	23	23	20	13	17
MobileNetV2FP16	12	24	24	29	23	23	23	18	13	15
MobileNetV2Int8LUT	23	23	23	29	23	23	23	16	13	16
Resnet50	23	23	24	29	23	23	23	14	11	14
Resnet50FP16	23	24	24	29	23	23	23	14	11	12
Resnet50Int8LUT	23	24	23	29	23	23	23	15	11	12
Resnet50Headless	21	24	23	29	23	23	23	23	16	17
SqueezeNet	36	24	24	29	23	23	23	23	23	23
SqueezeNetFP16	25	23	24	29	23	23	23	23	22	23
SqueezeNetInt8LUT	22	23	23	29	23	23	23	23	23	23

Build & Run

1. Prerequisites

1.1 Import the Core ML model

Once you import the model, compiler generates model helper class on build path automatically. You can access the model through model helper class by creating an instance, not through build path.

1.2 Add permission in info.plist for device's camera access

2. Dependencies

No external library yet.

3. Code

3.1 Import Vision framework

import Vision

3.2 Define properties for Core ML

// MARK - Core ML model
typealias ClassificationModel = MobileNet
var coremlModel: ClassificationModel? = nil

// MARK: - Vision Properties
var request: VNCoreMLRequest?
var visionModel: VNCoreMLModel?

3.3 Configure and prepare the model

override func viewDidLoad() {
    super.viewDidLoad()

	if let visionModel = try? VNCoreMLModel(for: ClassificationModel().model) {
        self.visionModel = visionModel
        request = VNCoreMLRequest(model: visionModel, completionHandler: visionRequestDidComplete)
        request?.imageCropAndScaleOption = .scaleFill
    } else {
        fatalError()
    }
}

func visionRequestDidComplete(request: VNRequest, error: Error?) {
    /* ------------------------------------------------------ */
    /* something postprocessing what you want after inference */
    /* ------------------------------------------------------ */
}

3.4 Inference 🏃‍♂️

guard let request = request else { fatalError() }
let handler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer)
try? handler.perform([request])