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    var md5: String {
        let str = self.cString(using: .utf8)
        let strLen = CUnsignedInt(self.lengthOfBytes(using: .utf8))
        let digestLen = Int(CC_MD5_DIGEST_LENGTH)
        let result = UnsafeMutablePointer<CUnsignedChar>.allocate(capacity: digestLen)
        CC_MD5(str!, strLen, result)
        let hash = NSMutableString()
        for i in 0..<digestLen {
            hash.appendFormat("%02x", result[i])
        }

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import Foundation

struct MarvelURL {
    private let ts = Int(Date().timeIntervalSinceReferenceDate)
    private let privateKey = Bundle.main.object(forInfoDictionaryKey: "MarvelPrivateKey")!
    private let publicKey = "3e2e1997fd3ada9d85d651c8627d1052"
    
    let urlString: String
    
    func authorized() -> URL {

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private let classifier = Resnet50()

func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) {
    let imageBuffer = CMSampleBufferGetImageBuffer(sampleBuffer)
    let rescaled = rescaledImageRectangle(createUIImage(from: imageBuffer), dimension: inputImageScale)
    let rescaledBuffer = createCVPixelBuffer(from: rescaled)
        
    guard let prediction = try? classifier.prediction(image: rescaledBuffer!) else { return }
    DispatchQueue.main.async {
        self.predictionLabel.text = prediction.classLabel

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pip install -U coremltools
pip install jupyter
jupyter-notebook

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pip install ipykernel
python -m ipykernel install --user --name=your-enviroment-name

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import coremltools
age_net = coremltools.converters.caffe.convert(('models/age_net.caffemodel', 'models/deploy_age.prototxt'), 
                                              	image_input_names='data',
                                              	class_labels='models/age_labels.txt')
gender_net = coremltools.converters.caffe.convert(('models/gender_net.caffemodel', 'models/deploy_gender.prototxt'),
                                               		image_input_names='data',
                                               		class_labels='models/gender_labels.txt')
age_net.save('AgeNet.mlmodel')
gender_net.save('GenderNet.mlmodel')

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input: "data"
input_dim: 1
input_dim: 3
input_dim: 227
input_dim: 227

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(0, 2)
(4, 6)
(8, 12)
(15, 20)
(25, 32)
(38, 43)
(48, 53)
(60, 100)

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let image = UIImage(buffer: givenBuffer)!.rescaled(width: desiredWidth, height: desiredHeight)
let buffer = image.buffer

let model = GenderNet()
let prediction = try! model.prediction(data: buffer)

let probabilites = prediction.prob // ['Male': 66.22, 'Female': 22.3212341]
let output = prediction.classLabel // 'Male

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extension LiveCameraViewController: LiveCameraDelegate {
    
    private let ageModel = try! VNCoreMLModel(for: AgeNet().model)
    private let genderModel = try! VNCoreMLModel(for: GenderNet().model)
    
    func createAgeRequest() -> VNCoreMLRequest {
        return VNCoreMLRequest(model: ageModel, completionHandler: onClassification)
    }
    
    func createGenderRequest() -> VNCoreMLRequest {